Add unittest for infernce (open-mmlab#18)

tests/test_inference.py

@@ -0,0 +1,50 @@
+import mmcv
+import pytest
+import torch
+import torch.nn as nn
+
+from mmaction.apis import inference_recognizer, init_recognizer
+
+config_file = 'configs/recognition/tsn/tsn_r50_video_inference_1x1x3_100e_kinetics400_rgb.py'  # noqa: E501
+label_path = 'demo/label_map.txt'
+video_path = 'demo/demo.mp4'
+
+
+def test_init_recognizer():
+    with pytest.raises(TypeError):
+        init_recognizer(dict(config_file=None))
+
+    if torch.cuda.is_available():
+        device = 'cuda:0'
+    else:
+        device = 'cpu'
+
+    model = init_recognizer(config_file, None, device)
+
+    config = mmcv.Config.fromfile(config_file)
+    config.model.backbone.pretrained = None
+
+    isinstance(model, nn.Module)
+    if torch.cuda.is_available():
+        assert next(model.parameters()).is_cuda is True
+    else:
+        assert next(model.parameters()).is_cuda is False
+    assert model.cfg.model.backbone.pretrained is None
+
+
+def test_inference_recognizer():
+    if torch.cuda.is_available():
+        device = 'cuda:0'
+    else:
+        device = 'cpu'
+    model = init_recognizer(config_file, None, device)
+
+    for ops in model.cfg.data.test.pipeline:
+        if ops['type'] == 'TenCrop':
+            # Use CenterCrop to reduce memory in order to pass CI
+            ops['type'] = 'CenterCrop'
+
+    top5_label = inference_recognizer(model, video_path, label_path)
+    scores = [item[1] for item in top5_label]
+    assert len(top5_label) == 5
+    assert scores == sorted(scores, reverse=True)