FeatureExtractionPredictor --> FeaturePredictor

chainercv/links/__init__.py

@@ -1,6 +1,6 @@
 from chainercv.links.connection.conv_2d_activ import Conv2DActiv  # NOQA
 
-from chainercv.links.model.feature_extraction_predictor import FeatureExtractionPredictor  # NOQA
+from chainercv.links.model.feature_predictor import FeaturePredictor  # NOQA
 from chainercv.links.model.pixelwise_softmax_classifier import PixelwiseSoftmaxClassifier  # NOQA
 from chainercv.links.model.sequential_feature_extractor import SequentialFeatureExtractor  # NOQA
 

chainercv/links/model/feature_extraction_predictor.py → chainercv/links/model/feature_predictor.py

@@ -10,7 +10,7 @@
 from chainercv.transforms import ten_crop
 
 
-class FeatureExtractionPredictor(chainer.Chain):
+class FeaturePredictor(chainer.Chain):
 
     """Wrapper that adds a prediction method to a feature extraction model.
 
@@ -23,9 +23,9 @@ class FeatureExtractionPredictor(chainer.Chain):
     Example:
 
         >>> from chainercv.links import VGG16
-        >>> from chainercv.links import FeatureExtractionPredictor
+        >>> from chainercv.links import FeaturePredictor
         >>> base_model = VGG16()
-        >>> model = FeatureExtractionPredictor(base_model, 224, 256)
+        >>> model = FeaturePredictor(base_model, 224, 256)
         >>> prob = model.predict([img])
         # Predicting multiple features
         >>> model.extractor.feature_names = ['conv5_3', 'fc7']
@@ -64,7 +64,7 @@ class FeatureExtractionPredictor(chainer.Chain):
     def __init__(self, extractor,
                  crop_size, scale_size=None,
                  crop='center', mean=None):
-        super(FeatureExtractionPredictor, self).__init__()
+        super(FeaturePredictor, self).__init__()
         self.scale_size = scale_size
         if isinstance(crop_size, int):
             crop_size = (crop_size, crop_size)

tests/links_tests/model_tests/test_feature_extraction_predictor.py → tests/links_tests/model_tests/test_feature_predictor.py

@@ -5,7 +5,7 @@
 from chainer import testing
 from chainer.testing import attr
 
-from chainercv.links import FeatureExtractionPredictor
+from chainercv.links import FeaturePredictor
 
 
 class DummyFeatureExtractor(chainer.Chain):
@@ -41,10 +41,10 @@ def __call__(self, x):
         ]
     )
 ))
-class TestFeatureExtractionPredictorPredict(unittest.TestCase):
+class TestFeaturePredictorPredict(unittest.TestCase):
 
     def setUp(self):
-        self.link = FeatureExtractionPredictor(
+        self.link = FeaturePredictor(
             DummyFeatureExtractor(
                 self.in_channels, self.shape_0, self.shape_1),
             crop_size=5, crop=self.crop)
@@ -81,11 +81,11 @@ def test_gpu(self):
     'in_channels': [1, 3],
     'mean': [None, np.float32(1)]
 }))
-class TestFeatureExtractionPredictor(unittest.TestCase):
+class TestFeaturePredictor(unittest.TestCase):
 
     def setUp(self):
 
-        self.link = FeatureExtractionPredictor(
+        self.link = FeaturePredictor(
             DummyFeatureExtractor(self.in_channels, (1,), None),
             crop_size=self.crop_size, scale_size=self.scale_size,
             crop=self.crop, mean=self.mean)