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# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import pytest
import mmcv
try:
import torch
except ImportError:
torch = None
else:
import torch.nn as nn
def test_assert_dict_contains_subset():
dict_obj = {'a': 'test1', 'b': 2, 'c': (4, 6)}
# case 1
expected_subset = {'a': 'test1', 'b': 2, 'c': (4, 6)}
assert mmcv.assert_dict_contains_subset(dict_obj, expected_subset)
# case 2
expected_subset = {'a': 'test1', 'b': 2, 'c': (6, 4)}
assert not mmcv.assert_dict_contains_subset(dict_obj, expected_subset)
# case 3
expected_subset = {'a': 'test1', 'b': 2, 'c': None}
assert not mmcv.assert_dict_contains_subset(dict_obj, expected_subset)
# case 4
expected_subset = {'a': 'test1', 'b': 2, 'd': (4, 6)}
assert not mmcv.assert_dict_contains_subset(dict_obj, expected_subset)
# case 5
dict_obj = {
'a': 'test1',
'b': 2,
'c': (4, 6),
'd': np.array([[5, 3, 5], [1, 2, 3]])
}
expected_subset = {
'a': 'test1',
'b': 2,
'c': (4, 6),
'd': np.array([[5, 3, 5], [6, 2, 3]])
}
assert not mmcv.assert_dict_contains_subset(dict_obj, expected_subset)
# case 6
dict_obj = {'a': 'test1', 'b': 2, 'c': (4, 6), 'd': np.array([[1]])}
expected_subset = {'a': 'test1', 'b': 2, 'c': (4, 6), 'd': np.array([[1]])}
assert mmcv.assert_dict_contains_subset(dict_obj, expected_subset)
if torch is not None:
dict_obj = {
'a': 'test1',
'b': 2,
'c': (4, 6),
'd': torch.tensor([5, 3, 5])
}
# case 7
expected_subset = {'d': torch.tensor([5, 5, 5])}
assert not mmcv.assert_dict_contains_subset(dict_obj, expected_subset)
# case 8
expected_subset = {'d': torch.tensor([[5, 3, 5], [4, 1, 2]])}
assert not mmcv.assert_dict_contains_subset(dict_obj, expected_subset)
def test_assert_attrs_equal():
class TestExample:
a, b, c = 1, ('wvi', 3), [4.5, 3.14]
def test_func(self):
return self.b
# case 1
assert mmcv.assert_attrs_equal(TestExample, {
'a': 1,
'b': ('wvi', 3),
'c': [4.5, 3.14]
})
# case 2
assert not mmcv.assert_attrs_equal(TestExample, {
'a': 1,
'b': ('wvi', 3),
'c': [4.5, 3.14, 2]
})
# case 3
assert not mmcv.assert_attrs_equal(TestExample, {
'bc': 54,
'c': [4.5, 3.14]
})
# case 4
assert mmcv.assert_attrs_equal(TestExample, {
'b': ('wvi', 3),
'test_func': TestExample.test_func
})
if torch is not None:
class TestExample:
a, b = torch.tensor([1]), torch.tensor([4, 5])
# case 5
assert mmcv.assert_attrs_equal(TestExample, {
'a': torch.tensor([1]),
'b': torch.tensor([4, 5])
})
# case 6
assert not mmcv.assert_attrs_equal(TestExample, {
'a': torch.tensor([1]),
'b': torch.tensor([4, 6])
})
assert_dict_has_keys_data_1 = [({
'res_layer': 1,
'norm_layer': 2,
'dense_layer': 3
})]
assert_dict_has_keys_data_2 = [(['res_layer', 'dense_layer'], True),
(['res_layer', 'conv_layer'], False)]
@pytest.mark.parametrize('obj', assert_dict_has_keys_data_1)
@pytest.mark.parametrize('expected_keys, ret_value',
assert_dict_has_keys_data_2)
def test_assert_dict_has_keys(obj, expected_keys, ret_value):
assert mmcv.assert_dict_has_keys(obj, expected_keys) == ret_value
assert_keys_equal_data_1 = [(['res_layer', 'norm_layer', 'dense_layer'])]
assert_keys_equal_data_2 = [(['res_layer', 'norm_layer', 'dense_layer'], True),
(['res_layer', 'dense_layer', 'norm_layer'], True),
(['res_layer', 'norm_layer'], False),
(['res_layer', 'conv_layer', 'norm_layer'], False)]
@pytest.mark.parametrize('result_keys', assert_keys_equal_data_1)
@pytest.mark.parametrize('target_keys, ret_value', assert_keys_equal_data_2)
def test_assert_keys_equal(result_keys, target_keys, ret_value):
assert mmcv.assert_keys_equal(result_keys, target_keys) == ret_value
@pytest.mark.skipif(torch is None, reason='requires torch library')
def test_assert_is_norm_layer():
# case 1
assert not mmcv.assert_is_norm_layer(nn.Conv3d(3, 64, 3))
# case 2
assert mmcv.assert_is_norm_layer(nn.BatchNorm3d(128))
# case 3
assert mmcv.assert_is_norm_layer(nn.GroupNorm(8, 64))
# case 4
assert not mmcv.assert_is_norm_layer(nn.Sigmoid())
@pytest.mark.skipif(torch is None, reason='requires torch library')
def test_assert_params_all_zeros():
demo_module = nn.Conv2d(3, 64, 3)
nn.init.constant_(demo_module.weight, 0)
nn.init.constant_(demo_module.bias, 0)
assert mmcv.assert_params_all_zeros(demo_module)
nn.init.xavier_normal_(demo_module.weight)
nn.init.constant_(demo_module.bias, 0)
assert not mmcv.assert_params_all_zeros(demo_module)
demo_module = nn.Linear(2048, 400, bias=False)
nn.init.constant_(demo_module.weight, 0)
assert mmcv.assert_params_all_zeros(demo_module)
nn.init.normal_(demo_module.weight, mean=0, std=0.01)
assert not mmcv.assert_params_all_zeros(demo_module)
def test_check_python_script(capsys):
mmcv.utils.check_python_script('./tests/data/scripts/hello.py zz')
captured = capsys.readouterr().out
assert captured == 'hello zz!\n'
mmcv.utils.check_python_script('./tests/data/scripts/hello.py agent')
captured = capsys.readouterr().out
assert captured == 'hello agent!\n'
# Make sure that wrong cmd raises an error
with pytest.raises(SystemExit):
mmcv.utils.check_python_script('./tests/data/scripts/hello.py li zz')
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