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[Unittest] Add visualizer unittest (#27)

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* add visualizer unittest

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Haian Huang(深度眸) 2022-02-21 20:55:11 +08:00 committed by GitHub
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tests/test_visualizer/test_visualizer.py Normal file
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# Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import numpy as np
import pytest
import torch
from mmengine.visualizer import Visualizer
class TesVisualizer(TestCase):
def setUp(self):
"""Setup the demo image in every test method.
TestCase calls functions in this order: setUp() -> testMethod() ->
tearDown() -> cleanUp()
"""
self.image = np.random.randint(0, 256, size=(10, 10, 3))
def assert_img_equal(self, img, ref_img, ratio_thr=0.999):
assert img.shape == ref_img.shape
assert img.dtype == ref_img.dtype
area = ref_img.shape[0] * ref_img.shape[1]
diff = np.abs(img.astype('int32') - ref_img.astype('int32'))
assert np.sum(diff <= 1) / float(area) > ratio_thr
def test_init(self):
# test `scale` parameter
# `scale` must be greater than 0.
with pytest.raises(AssertionError):
Visualizer(scale=0)
visualizer = Visualizer(scale=2, image=self.image)
out_image = visualizer.get_image()
assert (20, 20, 3) == out_image.shape
def test_set_image(self):
visualizer = Visualizer()
visualizer.set_image(self.image)
assert self.assert_img_equal(self.image, visualizer.get_image())
# test grayscale image
visualizer.set_image(self.image[..., 0])
assert self.assert_img_equal(self.image[..., 0],
visualizer.get_image())
def test_get_image(self):
visualizer = Visualizer(image=self.image)
out_image = visualizer.get_image()
assert self.assert_img_equal(self.image, out_image)
def test_draw_bboxes(self):
visualizer = Visualizer(image=self.image)
# only support 4 or nx4 tensor and numpy
visualizer.draw_bboxes(torch.tensor([1, 1, 2, 2]))
# valid bbox
visualizer.draw_bboxes(torch.tensor([1, 1, 1, 2]))
bboxes = torch.tensor([[1, 1, 2, 2], [1, 2, 2, 2.5]])
visualizer.draw_bboxes(
bboxes, alpha=0.5, edge_color='b', line_style='-')
bboxes = bboxes.numpy()
visualizer.draw_bboxes(bboxes)
# test invalid bbox
with pytest.raises(AssertionError):
# x1 > x2
visualizer.draw_bboxes(torch.tensor([5, 1, 2, 2]))
# test out of bounds
with pytest.warns(
UserWarning,
match='Warning: The bbox is out of bounds,'
' the drawn bbox may not be in the image'):
visualizer.draw_bboxes(torch.tensor([1, 1, 20, 2]))
# test incorrect bbox format
with pytest.raises(AssertionError):
visualizer.draw_bboxes([1, 1, 2, 2])
def test_draw_texts(self):
visualizer = Visualizer(image=self.image)
# only support tensor and numpy
visualizer.draw_texts('text1', position=torch.tensor([5, 5]))
visualizer.draw_texts(['text1', 'text2'],
position=torch.tensor([[5, 5], [3, 3]]))
visualizer.draw_texts('text1', position=np.array([5, 5]))
visualizer.draw_texts(['text1', 'text2'],
position=np.array([[5, 5], [3, 3]]))
# test out of bounds
with pytest.warns(
UserWarning,
match='Warning: The text is out of bounds,'
' the drawn text may not be in the image'):
visualizer.draw_texts('text1', position=torch.tensor([15, 5]))
# test incorrect format
with pytest.raises(AssertionError):
visualizer.draw_texts('text', position=[5, 5])
# test length mismatch
with pytest.raises(AssertionError):
visualizer.draw_texts(['text1', 'text2'],
position=torch.tensor([5, 5]))
visualizer.draw_texts('text1', position=torch.tensor([[5, 5]]))
visualizer.draw_texts(
'text1', position=torch.tensor([[5, 5], [3, 3]]))
def test_draw_lines(self):
visualizer = Visualizer(image=self.image)
# only support tensor and numpy
visualizer.draw_lines(
x_datas=torch.tensor([1, 5]), y_datas=torch.tensor([2, 6]))
visualizer.draw_lines(
x_datas=np.array([1, 5, 4]), y_datas=np.array([2, 6, 6]))
# test out of bounds
with pytest.warns(
UserWarning,
match='Warning: The line is out of bounds,'
' the drawn line may not be in the image'):
visualizer.draw_lines(
x_datas=torch.tensor([12, 5]), y_datas=torch.tensor([2, 6]))
# test incorrect format
with pytest.raises(AssertionError):
visualizer.draw_texts('text', position=[5, 5])
# test length mismatch
with pytest.raises(AssertionError):
visualizer.draw_lines(
x_datas=torch.tensor([1, 5]), y_datas=torch.tensor([2, 6, 7]))
def test_draw_circles(self):
visualizer = Visualizer(image=self.image)
# only support tensor and numpy
visualizer.draw_circles(torch.tensor([1, 5]))
visualizer.draw_circles(np.array([1, 5]))
visualizer.draw_circles(
torch.tensor([[1, 5], [2, 6]]), radius=torch.tensor([1, 2]))
# test out of bounds
with pytest.warns(
UserWarning,
match='Warning: The circle is out of bounds,'
' the drawn circle may not be in the image'):
visualizer.draw_circles(torch.tensor([12, 5]))
visualizer.draw_circles(torch.tensor([1, 5]), radius=10)
# test incorrect format
with pytest.raises(AssertionError):
visualizer.draw_circles([1, 5])
# test length mismatch
with pytest.raises(AssertionError):
visualizer.draw_circles(
torch.tensor([[1, 5]]), radius=torch.tensor([1, 2]))
def test_draw_polygons(self):
visualizer = Visualizer(image=self.image)
# shape Nx2 or list[Nx2]
visualizer.draw_polygons(torch.tensor([[1, 1], [2, 2], [3, 4]]))
visualizer.draw_polygons(np.array([[1, 1], [2, 2], [3, 4]]))
visualizer.draw_polygons([
np.array([[1, 1], [2, 2], [3, 4]]),
torch.tensor([[1, 1], [2, 2], [3, 4]])
])
# test out of bounds
with pytest.warns(
UserWarning,
match='Warning: The polygon is out of bounds,'
' the drawn polygon may not be in the image'):
visualizer.draw_polygons(torch.tensor([[1, 1], [2, 2], [16, 4]]))
def test_draw_binary_masks(self):
binary_mask = np.random.randint(0, 2, size=(10, 10)).astype(np.bool)
visualizer = Visualizer(image=self.image)
visualizer.draw_binary_masks(binary_mask)
visualizer.draw_binary_masks(torch.from_numpy(binary_mask))
# test the error that the size of mask and image are different.
with pytest.raises(AssertionError):
binary_mask = np.random.randint(0, 2, size=(8, 10)).astype(np.bool)
visualizer.draw_binary_masks(binary_mask)
# test non binary mask error
binary_mask = np.random.randint(0, 2, size=(10, 10, 3)).astype(np.bool)
with pytest.raises(AssertionError):
visualizer.draw_binary_masks(binary_mask)
# test non bool error
binary_mask = np.random.randint(0, 2, size=(10, 10))
with pytest.raises(AssertionError):
visualizer.draw_binary_masks(binary_mask)
def test_draw_featmap(self):
visualizer = Visualizer()
# test tensor format
with pytest.raises(AssertionError, match='Input dimension must be 3'):
visualizer.draw_featmap(torch.randn(1, 1, 3, 3))
# test mode parameter
# mode only supports 'mean' and 'max'
with pytest.raises(AssertionError):
visualizer.draw_featmap(torch.randn(2, 3, 3), mode='min')
# test topk parameter
with pytest.raises(
AssertionError,
match='The input tensor channel dimension must be 1 or 3 '
'when topk is less than 1, but the channel '
'dimension you input is 6, you can use the '
'mode parameter or set topk greater than 0 to solve '
'the error'):
visualizer.draw_featmap(torch.randn(6, 3, 3), mode=None, topk=0)
visualizer.draw_featmap(torch.randn(6, 3, 3), mode='mean')
visualizer.draw_featmap(torch.randn(1, 3, 3), mode='mean')
visualizer.draw_featmap(torch.randn(6, 3, 3), mode='max')
visualizer.draw_featmap(torch.randn(6, 3, 3), mode='max', topk=10)
visualizer.draw_featmap(torch.randn(1, 3, 3), mode=None, topk=-1)
visualizer.draw_featmap(torch.randn(3, 3, 3), mode=None, topk=-1)
visualizer.draw_featmap(torch.randn(6, 3, 3), mode=None, topk=4)
visualizer.draw_featmap(torch.randn(6, 3, 3), mode=None, topk=8)
def test_chain_call(self):
visualizer = Visualizer(image=self.image)
binary_mask = np.random.randint(0, 2, size=(10, 10)).astype(np.bool)
visualizer.draw_bboxes(torch.tensor([1, 1, 2, 2])). \
draw_texts('test', torch.tensor([5, 5])). \
draw_lines(x_datas=torch.tensor([1, 5]),
y_datas=torch.tensor([2, 6])). \
draw_circles(torch.tensor([1, 5])). \
draw_polygons(torch.tensor([[1, 1], [2, 2], [3, 4]])). \
draw_binary_masks(binary_mask)
def test_register_task(self):
class DetVisualizer(Visualizer):
@Visualizer.register_task('instances')
def draw_instance(self, instances, data_type):
pass
assert len(Visualizer.task_dict) == 1
assert 'instances' in Visualizer.task_dict
# test registration of the same names.
with pytest.raises(
KeyError,
match=('"instances" is already registered in task_dict, '
'add "force=True" if you want to override it')):
class DetVisualizer1(Visualizer):
@Visualizer.register_task('instances')
def draw_instance1(self, instances, data_type):
pass
@Visualizer.register_task('instances')
def draw_instance2(self, instances, data_type):
pass
class DetVisualizer2(Visualizer):
@Visualizer.register_task('instances')
def draw_instance1(self, instances, data_type):
pass
@Visualizer.register_task('instances', force=True)
def draw_instance2(self, instances, data_type):
pass
det_visualizer = DetVisualizer2()
assert len(det_visualizer.task_dict) == 1
assert 'instances' in det_visualizer.task_dict
assert det_visualizer.task_dict[
'instances'].__name__ == 'draw_instance2'

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tests/test_visualizer/test_writer.py Normal file
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# Copyright (c) OpenMMLab. All rights reserved.
import random
import sys
from unittest.mock import MagicMock
import numpy as np
import pytest
import torch
from mmengine.data import BaseDataElement, BaseDataSample
from mmengine.visualizer import (VISUALIZERS, LocalWriter, TensorboardWriter,
WandbWriter)
def get_demo_datasample():
metainfo = dict(
img_id=random.randint(0, 100),
img_shape=(random.randint(400, 600), random.randint(400, 600)))
gt_instances = BaseDataElement(
data=dict(bboxes=torch.rand((5, 4)), labels=torch.rand((5, ))))
pred_instances = BaseDataElement(
data=dict(bboxes=torch.rand((5, 4)), scores=torch.rand((5, ))))
data = dict(gt_instances=gt_instances, pred_instances=pred_instances)
data_sample = BaseDataSample(data=data, metainfo=metainfo)
return data_sample
class TestLocalWriter:
def test_add_image(self):
image = np.random.randint(0, 256, size=(10, 10, 3))
data_sample = get_demo_datasample()
local_writer = LocalWriter(visuailzer=dict(type='Visualizer'))
local_writer.add_image('img', image)
local_writer.add_image('img', image, data_sample)
bboxes = np.array([[1, 1, 2, 2], [1, 1.5, 1, 2.5]])
local_writer.visualizer.draw_bboxes(bboxes)
local_writer.add_image('img', local_writer.visualizer.get_image())
visuailzer = VISUALIZERS.build(dict(type='Visualizer'))
local_writer = LocalWriter(visuailzer=visuailzer)
local_writer.add_image('img', image)
local_writer.add_image('img', image, data_sample)
# test `visuailzer` parameter
# `visuailzer` parameter which must be either Visualizer instance
# or valid dictionary.
with pytest.raises(AssertionError):
class A:
pass
LocalWriter(visuailzer=A())
with pytest.raises(AssertionError):
LocalWriter(visuailzer=dict(a='Visualizer'))
# test not visuailzer
# The visuailzer parameter must be set when
# the local_writer object is instantiated and
# the `add_image` method is called.
with pytest.raises(AssertionError):
local_writer = LocalWriter()
local_writer.add_image('img', image)
def test_add_scaler(self):
local_writer = LocalWriter()
local_writer.add_scaler('map', 0.9)
def test_add_hyperparams(self):
local_writer = LocalWriter()
local_writer.add_hyperparams('hyper', dict(lr=0.01))
class TestWandbWriter:
sys.modules['wandb'] = MagicMock()
def test_add_image(self):
image = np.random.randint(0, 256, size=(10, 10, 3))
data_sample = get_demo_datasample()
wandb_writer = WandbWriter()
assert not wandb_writer.visualizer
wandb_writer.add_image('img', image, data_sample)
wandb_writer = WandbWriter(visuailzer=dict(type='Visualizer'))
assert wandb_writer.visualizer
wandb_writer.add_image('img', image)
wandb_writer.add_image('img', image, data_sample)
wandb_writer.visualizer.set_image(image)
wandb_writer.add_image('img', wandb_writer.visualizer.get_image())
# TODO test file exist
def test_add_scaler(self):
wandb_writer = WandbWriter()
wandb_writer.add_scaler('map', 0.9)
def test_add_hyperparams(self):
wandb_writer = WandbWriter()
wandb_writer.add_hyperparams('hyper', dict(lr=0.01))
class TestTensorboardWriter:
sys.modules['torch.utils.tensorboard.SummaryWriter'] = MagicMock()
def test_add_image(self):
image = np.random.randint(0, 256, size=(10, 10, 3))
data_sample = get_demo_datasample()
tensorboard_writer = TensorboardWriter()
assert not tensorboard_writer.visualizer
tensorboard_writer.add_image('img', image, data_sample)
tensorboard_writer = TensorboardWriter(
visuailzer=dict(type='Visualizer'))
assert tensorboard_writer.visualizer
tensorboard_writer.add_image('img', image)
tensorboard_writer.add_image('img', image, data_sample)
tensorboard_writer.visualizer.set_image(image)
tensorboard_writer.add_image('img',
tensorboard_writer.visualizer.get_image())
# test no visualizer
# The visuailzer parameter must be set when
# the tensorboard_writer object is instantiated and
# the `add_image` method is called.
with pytest.raises(AssertionError):
tensorboard_writer = TensorboardWriter()
tensorboard_writer.add_image('img', image)
def test_add_scaler(self):
tensorboard_writer = TensorboardWriter()
tensorboard_writer.add_scaler('map', 0.9)
def test_add_hyperparams(self):
tensorboard_writer = TensorboardWriter()
tensorboard_writer.add_hyperparams('hyper', dict(lr=0.01))