[Refactor] Refactor unittest (open-mmlab#321)

* Refactor unit tests folder structure.

* Remove label smooth and Vit test in `test_classifiers.py`

* Rename test_utils in dataset to test_dataset_utils

* Split test_models/test_utils/test_utils.py to multiple sub files.

* Add unit tests of classifiers and heads

* Use patch context manager.

* Add unit test of `is_tracing`, and add warning in `is_tracing` if torch
verison is smaller than 1.6.0

mmcls/models/utils/helpers.py

@@ -1,19 +1,26 @@
 import collections.abc
+import warnings
+from distutils.version import LooseVersion
 from itertools import repeat
 
 import torch
 
 
 def is_tracing() -> bool:
-    if hasattr(torch.jit, 'is_tracing'):
+    if LooseVersion(torch.__version__) >= LooseVersion('1.6.0'):
         on_trace = torch.jit.is_tracing()
         # In PyTorch 1.6, torch.jit.is_tracing has a bug.
         # Refers to https://github.com/pytorch/pytorch/issues/42448
         if isinstance(on_trace, bool):
             return on_trace
         else:
             return torch._C._is_tracing()
-    return False
+    else:
+        warnings.warn(
+            'torch.jit.is_tracing is only supported after v1.6.0. '
+            'Therefore is_tracing returns False automatically. Please '
+            'set on_trace manually if you are using trace.', UserWarning)
+        return False
 
 
 # From PyTorch internals

tests/test_dataset.py → tests/test_data/test_datasets/test_common.py

@@ -1,18 +1,11 @@
-import bisect
-import math
-import random
-import string
 import tempfile
-from collections import defaultdict
 from unittest.mock import MagicMock, patch
 
 import numpy as np
 import pytest
 import torch
 
-from mmcls.datasets import (DATASETS, BaseDataset, ClassBalancedDataset,
-                            ConcatDataset, MultiLabelDataset, RepeatDataset)
-from mmcls.datasets.utils import check_integrity, rm_suffix
+from mmcls.datasets import DATASETS, BaseDataset, MultiLabelDataset
 
 
 @pytest.mark.parametrize(
@@ -45,7 +38,6 @@ def test_datasets_override_default(dataset_name):
         pipeline=[],
         classes=('bus', 'car'),
         test_mode=True)
-    assert dataset.CLASSES != original_classes
     assert dataset.CLASSES == ('bus', 'car')
 
     # Test setting classes as a list
@@ -54,7 +46,6 @@ def test_datasets_override_default(dataset_name):
         pipeline=[],
         classes=['bus', 'car'],
         test_mode=True)
-    assert dataset.CLASSES != original_classes
     assert dataset.CLASSES == ['bus', 'car']
 
     # Test setting classes through a file
@@ -68,7 +59,6 @@ def test_datasets_override_default(dataset_name):
         test_mode=True)
     tmp_file.close()
 
-    assert dataset.CLASSES != original_classes
     assert dataset.CLASSES == ['bus', 'car']
 
     # Test overriding not a subset
@@ -77,7 +67,6 @@ def test_datasets_override_default(dataset_name):
         pipeline=[],
         classes=['foo'],
         test_mode=True)
-    assert dataset.CLASSES != original_classes
     assert dataset.CLASSES == ['foo']
 
     # Test default behavior
@@ -258,92 +247,3 @@ def test_dataset_evaluation():
     assert 'CR' in eval_results.keys()
     assert 'OF1' in eval_results.keys()
     assert 'CF1' not in eval_results.keys()
-
-
-@patch.multiple(BaseDataset, __abstractmethods__=set())
-def test_dataset_wrapper():
-    BaseDataset.CLASSES = ('foo', 'bar')
-    BaseDataset.__getitem__ = MagicMock(side_effect=lambda idx: idx)
-    dataset_a = BaseDataset(data_prefix='', pipeline=[], test_mode=True)
-    len_a = 10
-    cat_ids_list_a = [
-        np.random.randint(0, 80, num).tolist()
-        for num in np.random.randint(1, 20, len_a)
-    ]
-    dataset_a.data_infos = MagicMock()
-    dataset_a.data_infos.__len__.return_value = len_a
-    dataset_a.get_cat_ids = MagicMock(
-        side_effect=lambda idx: cat_ids_list_a[idx])
-    dataset_b = BaseDataset(data_prefix='', pipeline=[], test_mode=True)
-    len_b = 20
-    cat_ids_list_b = [
-        np.random.randint(0, 80, num).tolist()
-        for num in np.random.randint(1, 20, len_b)
-    ]
-    dataset_b.data_infos = MagicMock()
-    dataset_b.data_infos.__len__.return_value = len_b
-    dataset_b.get_cat_ids = MagicMock(
-        side_effect=lambda idx: cat_ids_list_b[idx])
-
-    concat_dataset = ConcatDataset([dataset_a, dataset_b])
-    assert concat_dataset[5] == 5
-    assert concat_dataset[25] == 15
-    assert concat_dataset.get_cat_ids(5) == cat_ids_list_a[5]
-    assert concat_dataset.get_cat_ids(25) == cat_ids_list_b[15]
-    assert len(concat_dataset) == len(dataset_a) + len(dataset_b)
-    assert concat_dataset.CLASSES == BaseDataset.CLASSES
-
-    repeat_dataset = RepeatDataset(dataset_a, 10)
-    assert repeat_dataset[5] == 5
-    assert repeat_dataset[15] == 5
-    assert repeat_dataset[27] == 7
-    assert repeat_dataset.get_cat_ids(5) == cat_ids_list_a[5]
-    assert repeat_dataset.get_cat_ids(15) == cat_ids_list_a[5]
-    assert repeat_dataset.get_cat_ids(27) == cat_ids_list_a[7]
-    assert len(repeat_dataset) == 10 * len(dataset_a)
-    assert repeat_dataset.CLASSES == BaseDataset.CLASSES
-
-    category_freq = defaultdict(int)
-    for cat_ids in cat_ids_list_a:
-        cat_ids = set(cat_ids)
-        for cat_id in cat_ids:
-            category_freq[cat_id] += 1
-    for k, v in category_freq.items():
-        category_freq[k] = v / len(cat_ids_list_a)
-
-    mean_freq = np.mean(list(category_freq.values()))
-    repeat_thr = mean_freq
-
-    category_repeat = {
-        cat_id: max(1.0, math.sqrt(repeat_thr / cat_freq))
-        for cat_id, cat_freq in category_freq.items()
-    }
-
-    repeat_factors = []
-    for cat_ids in cat_ids_list_a:
-        cat_ids = set(cat_ids)
-        repeat_factor = max({category_repeat[cat_id] for cat_id in cat_ids})
-        repeat_factors.append(math.ceil(repeat_factor))
-    repeat_factors_cumsum = np.cumsum(repeat_factors)
-    repeat_factor_dataset = ClassBalancedDataset(dataset_a, repeat_thr)
-    assert repeat_factor_dataset.CLASSES == BaseDataset.CLASSES
-    assert len(repeat_factor_dataset) == repeat_factors_cumsum[-1]
-    for idx in np.random.randint(0, len(repeat_factor_dataset), 3):
-        assert repeat_factor_dataset[idx] == bisect.bisect_right(
-            repeat_factors_cumsum, idx)
-
-
-def test_dataset_utils():
-    # test rm_suffix
-    assert rm_suffix('a.jpg') == 'a'
-    assert rm_suffix('a.bak.jpg') == 'a.bak'
-    assert rm_suffix('a.bak.jpg', suffix='.jpg') == 'a.bak'
-    assert rm_suffix('a.bak.jpg', suffix='.bak.jpg') == 'a'
-
-    # test check_integrity
-    rand_file = ''.join(random.sample(string.ascii_letters, 10))
-    assert not check_integrity(rand_file, md5=None)
-    assert not check_integrity(rand_file, md5=2333)
-    tmp_file = tempfile.NamedTemporaryFile()
-    assert check_integrity(tmp_file.name, md5=None)
-    assert not check_integrity(tmp_file.name, md5=2333)

tests/test_data/test_datasets/test_dataset_utils.py

@@ -0,0 +1,21 @@
+import random
+import string
+import tempfile
+
+from mmcls.datasets.utils import check_integrity, rm_suffix
+
+
+def test_dataset_utils():
+    # test rm_suffix
+    assert rm_suffix('a.jpg') == 'a'
+    assert rm_suffix('a.bak.jpg') == 'a.bak'
+    assert rm_suffix('a.bak.jpg', suffix='.jpg') == 'a.bak'
+    assert rm_suffix('a.bak.jpg', suffix='.bak.jpg') == 'a'
+
+    # test check_integrity
+    rand_file = ''.join(random.sample(string.ascii_letters, 10))
+    assert not check_integrity(rand_file, md5=None)
+    assert not check_integrity(rand_file, md5=2333)
+    tmp_file = tempfile.NamedTemporaryFile()
+    assert check_integrity(tmp_file.name, md5=None)
+    assert not check_integrity(tmp_file.name, md5=2333)