mirror of https://github.com/open-mmlab/mmcv.git
Add common testing function of MM repos (#743)
* add testing function add unittest for check_dict add unittest for the function in testing * polish docstring of testing.py rename some function * remove in is_all_zeros * modify the comment of check_dict * modify the testing.py according to feedback * add test about numpy for function dict_contains_subset * applying unified stylepull/795/head
congee
GitHub
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daab369e99
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905c9b43b8
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@ -9,6 +9,9 @@ from .path import (check_file_exist, fopen, is_filepath, mkdir_or_exist,
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scandir, symlink)
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from .progressbar import (ProgressBar, track_iter_progress,
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track_parallel_progress, track_progress)
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from .testing import (assert_attrs_equal, assert_dict_contains_subset,
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assert_dict_has_keys, assert_is_norm_layer,
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assert_keys_equal, assert_params_all_zeros)
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from .timer import Timer, TimerError, check_time
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from .version_utils import digit_version, get_git_hash
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@ -23,7 +26,9 @@ except ImportError:
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'mkdir_or_exist', 'symlink', 'scandir', 'ProgressBar',
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'track_progress', 'track_iter_progress', 'track_parallel_progress',
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'Timer', 'TimerError', 'check_time', 'deprecated_api_warning',
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'digit_version', 'get_git_hash', 'import_modules_from_strings'
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'digit_version', 'get_git_hash', 'import_modules_from_strings',
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'assert_dict_contains_subset', 'assert_attrs_equal',
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'assert_dict_has_keys', 'assert_keys_equal'
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]
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else:
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from .env import collect_env
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@ -49,5 +54,8 @@ else:
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'_InstanceNorm', '_MaxPoolNd', 'get_build_config', 'BuildExtension',
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'CppExtension', 'CUDAExtension', 'DataLoader', 'PoolDataLoader',
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'TORCH_VERSION', 'deprecated_api_warning', 'digit_version',
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'get_git_hash', 'import_modules_from_strings', 'jit', 'skip_no_elena'
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'get_git_hash', 'import_modules_from_strings', 'jit', 'skip_no_elena',
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'assert_dict_contains_subset', 'assert_attrs_equal',
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'assert_dict_has_keys', 'assert_keys_equal', 'assert_is_norm_layer',
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'assert_params_all_zeros'
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]
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@ -0,0 +1,121 @@
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# Copyright (c) Open-MMLab.
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from collections.abc import Iterable
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from typing import Any, Dict, List
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def _any(judge_result):
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"""Since built-in ``any`` works only when the element of iterable is not
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iterable, implement the function."""
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if not isinstance(judge_result, Iterable):
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return judge_result
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try:
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for element in judge_result:
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if _any(element):
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return True
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except TypeError:
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# Maybe encouter the case: torch.tensor(True) | torch.tensor(False)
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if judge_result:
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return True
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return False
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def assert_dict_contains_subset(dict_obj: Dict[Any, Any],
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expected_subset: Dict[Any, Any]) -> bool:
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"""Check if the dict_obj contains the expected_subset.
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Args:
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dict_obj (Dict[Any, Any]): Dict object to be checked.
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expected_subset (Dict[Any, Any]): Subset expected to be contained in
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dict_obj.
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Returns:
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bool: Whether the dict_obj contains the expected_subset.
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"""
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for key, value in expected_subset.items():
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if key not in dict_obj.keys() or _any(dict_obj[key] != value):
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return False
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return True
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def assert_attrs_equal(obj: Any, expected_attrs: Dict[str, Any]) -> bool:
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"""Check if attribute of class object is correct.
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Args:
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obj (object): Class object to be checked.
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expected_attrs (Dict[str, Any]): Dict of the expected attrs.
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Returns:
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bool: Whether the attribute of class object is correct.
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"""
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for attr, value in expected_attrs.items():
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if not hasattr(obj, attr) or _any(getattr(obj, attr) != value):
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return False
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return True
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def assert_dict_has_keys(obj: Dict[str, Any],
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expected_keys: List[str]) -> bool:
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"""Check if the obj has all the expected_keys.
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Args:
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obj (Dict[str, Any]): Object to be checked.
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expected_keys (List[str]): Keys expected to contained in the keys of
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the obj.
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Returns:
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bool: Whether the obj has the expected keys.
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"""
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return set(expected_keys).issubset(set(obj.keys()))
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def assert_keys_equal(result_keys: List[str], target_keys: List[str]) -> bool:
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"""Check if target_keys is equal to result_keys.
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Args:
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result_keys (List[str]): Result keys to be checked.
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target_keys (List[str]): Target keys to be checked.
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Returns:
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bool: Whether target_keys is equal to result_keys.
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"""
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return set(result_keys) == set(target_keys)
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def assert_is_norm_layer(module) -> bool:
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"""Check if the module is a norm layer.
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Args:
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module (nn.Module): The module to be checked.
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Returns:
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bool: Whether the module is a norm layer.
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"""
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from .parrots_wrapper import _BatchNorm, _InstanceNorm
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from torch.nn import GroupNorm, LayerNorm
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norm_layer_candidates = (_BatchNorm, _InstanceNorm, GroupNorm, LayerNorm)
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return isinstance(module, norm_layer_candidates)
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def assert_params_all_zeros(module) -> bool:
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"""Check if the parameters of the module is all zeros.
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Args:
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module (nn.Module): The module to be checked.
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Returns:
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bool: Whether the parameters of the module is all zeros.
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"""
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weight_data = module.weight.data
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is_weight_zero = weight_data.allclose(
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weight_data.new_zeros(weight_data.size()))
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if hasattr(module, 'bias') and module.bias is not None:
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bias_data = module.bias.data
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is_bias_zero = bias_data.allclose(
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bias_data.new_zeros(bias_data.size()))
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else:
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is_bias_zero = True
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return is_weight_zero and is_bias_zero
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@ -0,0 +1,182 @@
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import numpy as np
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import pytest
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import mmcv
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try:
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import torch
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except ImportError:
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torch = None
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else:
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import torch.nn as nn
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def test_assert_dict_contains_subset():
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dict_obj = {'a': 'test1', 'b': 2, 'c': (4, 6)}
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# case 1
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expected_subset = {'a': 'test1', 'b': 2, 'c': (4, 6)}
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assert mmcv.assert_dict_contains_subset(dict_obj, expected_subset)
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# case 2
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expected_subset = {'a': 'test1', 'b': 2, 'c': (6, 4)}
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assert not mmcv.assert_dict_contains_subset(dict_obj, expected_subset)
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# case 3
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expected_subset = {'a': 'test1', 'b': 2, 'c': None}
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assert not mmcv.assert_dict_contains_subset(dict_obj, expected_subset)
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# case 4
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expected_subset = {'a': 'test1', 'b': 2, 'd': (4, 6)}
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assert not mmcv.assert_dict_contains_subset(dict_obj, expected_subset)
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# case 5
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dict_obj = {
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'a': 'test1',
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'b': 2,
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'c': (4, 6),
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'd': np.array([[5, 3, 5], [1, 2, 3]])
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}
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expected_subset = {
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'a': 'test1',
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'b': 2,
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'c': (4, 6),
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'd': np.array([[5, 3, 5], [6, 2, 3]])
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}
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assert not mmcv.assert_dict_contains_subset(dict_obj, expected_subset)
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# case 6
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dict_obj = {'a': 'test1', 'b': 2, 'c': (4, 6), 'd': np.array([[1]])}
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expected_subset = {'a': 'test1', 'b': 2, 'c': (4, 6), 'd': np.array([[1]])}
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assert mmcv.assert_dict_contains_subset(dict_obj, expected_subset)
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if torch is not None:
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dict_obj = {
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'a': 'test1',
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'b': 2,
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'c': (4, 6),
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'd': torch.tensor([5, 3, 5])
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}
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# case 7
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expected_subset = {'d': torch.tensor([5, 5, 5])}
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assert not mmcv.assert_dict_contains_subset(dict_obj, expected_subset)
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# case 8
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expected_subset = {'d': torch.tensor([[5, 3, 5], [4, 1, 2]])}
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assert not mmcv.assert_dict_contains_subset(dict_obj, expected_subset)
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def test_assert_attrs_equal():
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class TestExample(object):
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a, b, c = 1, ('wvi', 3), [4.5, 3.14]
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def test_func(self):
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return self.b
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# case 1
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assert mmcv.assert_attrs_equal(TestExample, {
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'a': 1,
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'b': ('wvi', 3),
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'c': [4.5, 3.14]
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})
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# case 2
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assert not mmcv.assert_attrs_equal(TestExample, {
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'a': 1,
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'b': ('wvi', 3),
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'c': [4.5, 3.14, 2]
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})
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# case 3
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assert not mmcv.assert_attrs_equal(TestExample, {
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'bc': 54,
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'c': [4.5, 3.14]
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})
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# case 4
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assert mmcv.assert_attrs_equal(TestExample, {
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'b': ('wvi', 3),
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'test_func': TestExample.test_func
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})
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if torch is not None:
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class TestExample(object):
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a, b = torch.tensor([1]), torch.tensor([4, 5])
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# case 5
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assert mmcv.assert_attrs_equal(TestExample, {
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'a': torch.tensor([1]),
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'b': torch.tensor([4, 5])
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})
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# case 6
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assert not mmcv.assert_attrs_equal(TestExample, {
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'a': torch.tensor([1]),
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'b': torch.tensor([4, 6])
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})
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assert_dict_has_keys_data_1 = [({
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'res_layer': 1,
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'norm_layer': 2,
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'dense_layer': 3
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})]
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assert_dict_has_keys_data_2 = [(['res_layer', 'dense_layer'], True),
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(['res_layer', 'conv_layer'], False)]
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@pytest.mark.parametrize('obj', assert_dict_has_keys_data_1)
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@pytest.mark.parametrize('expected_keys, ret_value',
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assert_dict_has_keys_data_2)
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def test_assert_dict_has_keys(obj, expected_keys, ret_value):
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assert mmcv.assert_dict_has_keys(obj, expected_keys) == ret_value
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assert_keys_equal_data_1 = [(['res_layer', 'norm_layer', 'dense_layer'])]
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assert_keys_equal_data_2 = [(['res_layer', 'norm_layer', 'dense_layer'], True),
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(['res_layer', 'dense_layer', 'norm_layer'], True),
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(['res_layer', 'norm_layer'], False),
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(['res_layer', 'conv_layer', 'norm_layer'], False)]
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@pytest.mark.parametrize('result_keys', assert_keys_equal_data_1)
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@pytest.mark.parametrize('target_keys, ret_value', assert_keys_equal_data_2)
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def test_assert_keys_equal(result_keys, target_keys, ret_value):
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assert mmcv.assert_keys_equal(result_keys, target_keys) == ret_value
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@pytest.mark.skipif(torch is None, reason='requires torch library')
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def test_assert_is_norm_layer():
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# case 1
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assert not mmcv.assert_is_norm_layer(nn.Conv3d(3, 64, 3))
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# case 2
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assert mmcv.assert_is_norm_layer(nn.BatchNorm3d(128))
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# case 3
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assert mmcv.assert_is_norm_layer(nn.GroupNorm(8, 64))
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# case 4
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assert not mmcv.assert_is_norm_layer(nn.Sigmoid())
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@pytest.mark.skipif(torch is None, reason='requires torch library')
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def test_assert_params_all_zeros():
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demo_module = nn.Conv2d(3, 64, 3)
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nn.init.constant_(demo_module.weight, 0)
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nn.init.constant_(demo_module.bias, 0)
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assert mmcv.assert_params_all_zeros(demo_module)
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nn.init.xavier_normal_(demo_module.weight)
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nn.init.constant_(demo_module.bias, 0)
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assert not mmcv.assert_params_all_zeros(demo_module)
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demo_module = nn.Linear(2048, 400, bias=False)
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nn.init.constant_(demo_module.weight, 0)
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assert mmcv.assert_params_all_zeros(demo_module)
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nn.init.normal_(demo_module.weight, mean=0, std=0.01)
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assert not mmcv.assert_params_all_zeros(demo_module)
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