[Improve] Update unittest

tests/test_accuracy.py

@@ -5,9 +5,9 @@
 from numpy.testing import assert_array_almost_equal, assert_array_equal
 
 from mmaction.core import (average_recall_at_avg_proposals, confusion_matrix,
-                           get_weighted_score, mean_class_accuracy,
-                           mmit_mean_average_precision, pairwise_temporal_iou,
-                           top_k_accuracy)
+                           get_weighted_score, mean_average_precision,
+                           mean_class_accuracy, mmit_mean_average_precision,
+                           pairwise_temporal_iou, top_k_accuracy)
 
 
 def gt_confusion_matrix(gt_labels, pred_labels, normalize=None):
@@ -235,3 +235,25 @@ def test_get_weighted_score():
         x * coeff_a + y * coeff_b for x, y in zip(score_a, score_b)
     ]
     assert np.all(np.isclose(np.array(ground_truth), np.array(weighted_score)))
+
+
+def test_mean_average_precision():
+
+    def content_for_unittest(scores, labels, result):
+        gt = mean_average_precision(scores, labels)
+        assert gt == result
+
+    scores = [
+        np.array([0.1, 0.2, 0.3, 0.4]),
+        np.array([0.2, 0.3, 0.4, 0.1]),
+        np.array([0.3, 0.4, 0.1, 0.2]),
+        np.array([0.4, 0.1, 0.2, 0.3])
+    ]
+
+    label1 = np.array([[1, 1, 0, 0], [1, 0, 1, 1], [1, 0, 1, 0], [1, 1, 0, 1]])
+    result1 = 2 / 3
+    label2 = np.array([[0, 1, 0, 1], [0, 1, 1, 0], [1, 0, 1, 0], [0, 0, 1, 1]])
+    result2 = np.mean([0.5, 0.5833333333333333, 0.8055555555555556, 1.0])
+
+    content_for_unittest(scores, label1, result1)
+    content_for_unittest(scores, label2, result2)

tests/test_models/test_inference.py

@@ -62,7 +62,7 @@ def test_inference_recognizer():
         inference_recognizer(model, 'demo/', label_path)
 
     for ops in model.cfg.data.test.pipeline:
-        if ops['type'] == 'TenCrop':
+        if ops['type'] in ('TenCrop', 'ThreeCrop'):
             # Use CenterCrop to reduce memory in order to pass CI
             ops['type'] = 'CenterCrop'
 

tests/test_runtime/test_apis_test.py

@@ -0,0 +1,106 @@
+import sys
+from unittest.mock import MagicMock, Mock, patch
+
+import pytest
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader, Dataset
+
+from mmaction.apis.test import (collect_results_cpu, multi_gpu_test,
+                                single_gpu_test)
+
+
+class OldStyleModel(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        self.conv = nn.Conv2d(3, 3, 1)
+        self.cnt = 0
+
+    def forward(self, return_loss, **kwargs):
+        result = [self.cnt]
+        self.cnt += 1
+        return result
+
+
+class Model(OldStyleModel):
+
+    def train_step(self):
+        pass
+
+    def val_step(self):
+        pass
+
+
+class ExampleDataset(Dataset):
+
+    def __init__(self):
+        self.index = 0
+        self.eval_result = [1, 4, 3, 7, 2, -3, 4, 6]
+
+    def __getitem__(self, idx):
+        results = dict(imgs=torch.tensor([1]))
+        return results
+
+    def __len__(self):
+        return len(self.eval_result)
+
+
+def test_single_gpu_test():
+    test_dataset = ExampleDataset()
+    loader = DataLoader(test_dataset, batch_size=1)
+    model = Model()
+
+    results = single_gpu_test(model, loader)
+    assert results == list(range(8))
+
+
+def mock_tensor_without_cuda(*args, **kwargs):
+    if 'device' not in kwargs:
+        return torch.Tensor(*args)
+    return torch.IntTensor(*args, device='cpu')
+
+
+@patch('mmaction.apis.test.collect_results_gpu',
+       Mock(return_value=list(range(8))))
+@patch('mmaction.apis.test.collect_results_cpu',
+       Mock(return_value=list(range(8))))
+def test_multi_gpu_test():
+    test_dataset = ExampleDataset()
+    loader = DataLoader(test_dataset, batch_size=1)
+    model = Model()
+
+    results = multi_gpu_test(model, loader)
+    assert results == list(range(8))
+
+    results = multi_gpu_test(model, loader, gpu_collect=False)
+    assert results == list(range(8))
+
+
+@patch('mmcv.runner.get_dist_info', Mock(return_value=(0, 1)))
+@patch('torch.distributed.broadcast', MagicMock)
+@patch('torch.distributed.barrier', Mock)
+@pytest.mark.skipif(
+    sys.version_info[:2] == (3, 8), reason='Not for python 3.8')
+def test_collect_results_cpu():
+
+    def content_for_unittest():
+        results_part = list(range(8))
+        size = 8
+
+        results = collect_results_cpu(results_part, size)
+        assert results == list(range(8))
+
+        results = collect_results_cpu(results_part, size, 'unittest')
+        assert results == list(range(8))
+
+    if not torch.cuda.is_available():
+        with patch(
+                'torch.full',
+                Mock(
+                    return_value=torch.full(
+                        (512, ), 32, dtype=torch.uint8, device='cpu'))):
+            with patch('torch.tensor', mock_tensor_without_cuda):
+                content_for_unittest()
+    else:
+        content_for_unittest()

tests/test_config.py → tests/test_runtime/test_config.py

@@ -10,7 +10,7 @@
 
 def _get_config_path():
     """Find the predefined recognizer config path."""
-    repo_dir = osp.dirname(osp.dirname(__file__))
+    repo_dir = osp.dirname(osp.dirname(osp.dirname(__file__)))
     config_dpath = osp.join(repo_dir, 'configs')
     if not osp.exists(config_dpath):
         raise Exception('Cannot find config path')
@@ -26,7 +26,7 @@ def _get_config_path():
 def test_config_build_recognizer():
     """Test that all mmaction models defined in the configs can be
     initialized."""
-    repo_dir = osp.dirname(osp.dirname(__file__))
+    repo_dir = osp.dirname(osp.dirname(osp.dirname(__file__)))
     config_dpath = osp.join(repo_dir, 'configs/recognition')
     if not osp.exists(config_dpath):
         raise Exception('Cannot find config path')
@@ -49,7 +49,7 @@ def test_config_build_recognizer():
 
 def _get_config_path_for_localizer():
     """Find the predefined localizer config path for localizer."""
-    repo_dir = osp.dirname(osp.dirname(__file__))
+    repo_dir = osp.dirname(osp.dirname(osp.dirname(__file__)))
     config_dpath = osp.join(repo_dir, 'configs/localization')
     if not osp.exists(config_dpath):
         raise Exception('Cannot find config path')

tests/test_eval_hook.py → tests/test_runtime/test_eval_hook.py

@@ -87,8 +87,6 @@ def val_step(self, x, optimizer, **kwargs):
     return runner
 
 
-@pytest.mark.skipif(
-    not torch.cuda.is_available(), reason='requires CUDA support')
 def test_eval_hook():
     with pytest.raises(TypeError):
         # `save_best` should be a boolean

tests/test_train.py → tests/test_runtime/test_train.py

@@ -71,6 +71,7 @@ def test_train_model():
         workflow=[('train', 1)],
         total_epochs=5,
         evaluation=dict(interval=1, key_indicator='acc'),
+        omnisource=False,
         data=dict(
             videos_per_gpu=1,
             workers_per_gpu=0,