mmsegmentation/tests/test_engine/test_visualization_hook.py

63 lines
2.2 KiB
Python

# Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
from unittest.mock import Mock
import torch
from mmengine.data import PixelData
from mmseg.engine.hooks import SegVisualizationHook
from mmseg.structures import SegDataSample
from mmseg.visualization import SegLocalVisualizer
class TestVisualizationHook(TestCase):
def setUp(self) -> None:
h = 288
w = 512
num_class = 2
SegLocalVisualizer.get_instance('visualizer')
SegLocalVisualizer.dataset_meta = dict(
classes=('background', 'foreground'),
palette=[[120, 120, 120], [6, 230, 230]])
data_sample = SegDataSample()
data_sample.set_metainfo({'img_path': 'tests/data/color.jpg'})
self.data_batch = [{'data_sample': data_sample}] * 2
pred_sem_seg_data = dict(data=torch.randint(0, num_class, (1, h, w)))
pred_sem_seg = PixelData(**pred_sem_seg_data)
pred_seg_data_sample = SegDataSample()
pred_seg_data_sample.pred_sem_seg = pred_sem_seg
self.outputs = [pred_seg_data_sample] * 2
def test_after_iter(self):
runner = Mock()
runner.iter = 1
hook = SegVisualizationHook(draw=True, interval=1)
hook._after_iter(
runner, 1, self.data_batch, self.outputs, mode='train')
hook._after_iter(runner, 1, self.data_batch, self.outputs, mode='val')
hook._after_iter(runner, 1, self.data_batch, self.outputs, mode='test')
def test_after_val_iter(self):
runner = Mock()
runner.iter = 2
hook = SegVisualizationHook(interval=1)
hook.after_val_iter(runner, 1, self.data_batch, self.outputs)
hook = SegVisualizationHook(draw=True, interval=1)
hook.after_val_iter(runner, 1, self.data_batch, self.outputs)
hook = SegVisualizationHook(
draw=True, interval=1, show=True, wait_time=1)
hook.after_val_iter(runner, 1, self.data_batch, self.outputs)
def test_after_test_iter(self):
runner = Mock()
runner.iter = 3
hook = SegVisualizationHook(draw=True, interval=1)
hook.after_test_iter(runner, 1, self.data_batch, self.outputs)