mirror of
https://github.com/open-mmlab/mmengine.git
synced 2025-06-03 21:54:44 +08:00
7e1d7af2d9
* Rename data to structure * adjust the way to import module * adjust the way to import module * rename Structure to Data Structures in docs api * rename structure to structures * support using some modules of mmengine without torch * fix circleci config * fix circleci config * fix registry ut * minor fix * move init method from model/utils to model/weight_init.py * move init method from model/utils to model/weight_init.py * move sync_bn to model * move functions depending on torch to dl_utils * format import * fix logging ut * add weight init in model/__init__.py * move get_config and get_model to mmengine/hub * move log_processor.py to mmengine/runner * fix ut * Add TimeCounter in dl_utils/__init__.py
72 lines
2.9 KiB
Python
72 lines
2.9 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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from unittest.mock import Mock
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import torch
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from mmengine.hooks import NaiveVisualizationHook
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from mmengine.structures import BaseDataElement
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class TestNaiveVisualizationHook:
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def test_after_train_iter(self):
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naive_visualization_hook = NaiveVisualizationHook()
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runner = Mock(iter=1)
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runner.visualizer.add_image = Mock()
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inputs = torch.randn(1, 3, 15, 15)
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batch_idx = 10
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# test with normalize, resize, pad
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gt_datasamples = BaseDataElement(
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metainfo=dict(
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img_norm_cfg=dict(
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mean=(0, 0, 0), std=(0.5, 0.5, 0.5), to_bgr=True),
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scale=(10, 10),
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pad_shape=(15, 15, 3),
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ori_height=5,
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ori_width=5,
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img_path='tmp.jpg'))
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pred_datasamples = [BaseDataElement()]
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data_batch = [dict(inputs=inputs, data_sample=gt_datasamples)]
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naive_visualization_hook.after_test_iter(runner, batch_idx, data_batch,
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pred_datasamples)
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# test with resize, pad
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gt_datasamples = BaseDataElement(
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metainfo=dict(
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scale=(10, 10),
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pad_shape=(15, 15, 3),
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ori_height=5,
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ori_width=5,
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img_path='tmp.jpg'))
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pred_datasamples = [BaseDataElement()]
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data_batch = [dict(inputs=inputs, data_sample=gt_datasamples)]
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naive_visualization_hook.after_test_iter(runner, batch_idx, data_batch,
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pred_datasamples)
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# test with only resize
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gt_datasamples = BaseDataElement(
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metainfo=dict(
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scale=(15, 15), ori_height=5, ori_width=5, img_path='tmp.jpg'))
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pred_datasamples = [BaseDataElement()]
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data_batch = [dict(inputs=inputs, data_sample=gt_datasamples)]
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naive_visualization_hook.after_test_iter(runner, batch_idx, data_batch,
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pred_datasamples)
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# test with only pad
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gt_datasamples = BaseDataElement(
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metainfo=dict(
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pad_shape=(15, 15, 3),
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ori_height=5,
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ori_width=5,
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img_path='tmp.jpg'))
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pred_datasamples = [BaseDataElement()]
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data_batch = [dict(inputs=inputs, data_sample=gt_datasamples)]
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naive_visualization_hook.after_test_iter(runner, batch_idx, data_batch,
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pred_datasamples)
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# test no transform
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gt_datasamples = BaseDataElement(
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metainfo=dict(ori_height=15, ori_width=15, img_path='tmp.jpg'))
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pred_datasamples = [BaseDataElement()]
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data_batch = [dict(inputs=inputs, data_sample=gt_datasamples)]
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naive_visualization_hook.after_test_iter(runner, batch_idx, data_batch,
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pred_datasamples)
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