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3e0c064f49
* [WIP] testvisualizationhook * add TestNaiveVisualizationHook * fix comment * unpad * batch imdenormalize * fix comment * fix comment
85 lines
3.0 KiB
Python
85 lines
3.0 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.data import BaseDataSample
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from mmengine.hooks import NaiveVisualizationHook
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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.writer.add_image = Mock()
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inputs = torch.randn(1, 3, 15, 15)
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# test with normalize, resize, pad
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gt_datasamples = [
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BaseDataSample(
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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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]
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pred_datasamples = [BaseDataSample()]
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data_batch = (inputs, gt_datasamples)
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naive_visualization_hook.after_test_iter(Runner, data_batch,
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pred_datasamples)
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# test with resize, pad
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gt_datasamples = [
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BaseDataSample(
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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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]
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pred_datasamples = [BaseDataSample()]
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data_batch = (inputs, gt_datasamples)
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naive_visualization_hook.after_test_iter(Runner, data_batch,
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pred_datasamples)
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# test with only resize
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gt_datasamples = [
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BaseDataSample(
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metainfo=dict(
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scale=(15, 15),
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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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]
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pred_datasamples = [BaseDataSample()]
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data_batch = (inputs, gt_datasamples)
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naive_visualization_hook.after_test_iter(Runner, data_batch,
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pred_datasamples)
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# test with only pad
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gt_datasamples = [
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BaseDataSample(
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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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]
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pred_datasamples = [BaseDataSample()]
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data_batch = (inputs, gt_datasamples)
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naive_visualization_hook.after_test_iter(Runner, data_batch,
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pred_datasamples)
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# test no transform
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gt_datasamples = [
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BaseDataSample(
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metainfo=dict(ori_height=15, ori_width=15,
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img_path='tmp.jpg')),
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]
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pred_datasamples = [BaseDataSample()]
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data_batch = (inputs, gt_datasamples)
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naive_visualization_hook.after_test_iter(Runner, data_batch,
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pred_datasamples)
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