pass test_predict

chainercv/links/model/vgg/vgg16.py

@@ -11,20 +11,22 @@
 from chainer.initializers import normal
 import chainer.links as L
 
+from chainercv.transforms import center_crop
 from chainercv.transforms import resize
 from chainercv.transforms import ten_crop
 
 
 # RGB order
 _imagenet_mean = np.array(
-    [[[123.68], [116.779], [103.939]]], dtype=np.float32)
+    [123.68, 116.779, 103.939], dtype=np.float32)[:, np.newaxis, np.newaxis]
 
 
 class VGG16Layers(chainer.Chain):
 
     def __init__(self, pretrained_model='auto', feature='prob',
                  initialW=None, initial_bias=None,
                  mean=_imagenet_mean):
+        self.mean = mean
         if pretrained_model:
             # As a sampling process is time-consuming,
             # we employ a zero initializer for faster computation.
@@ -158,15 +160,17 @@ def predict(self, imgs, do_ten_crop=True):
         imgs = [self._prepare(img) for img in imgs]
         if do_ten_crop:
             imgs = [ten_crop(img, (224, 224)) for img in imgs]
+        else:
+            imgs = [center_crop(img, (224, 224)) for img in imgs]
         imgs = self.xp.asarray(imgs).reshape(-1, 3, 224, 224)
 
         with chainer.function.no_backprop_mode():
             imgs = chainer.Variable(imgs)
             y = self(imgs).data
 
             if do_ten_crop:
-                n = y.data.shape[0] // 10
-                y_shape = y.data.shape[1:]
+                n = y.shape[0] // 10
+                y_shape = y.shape[1:]
                 y = y.reshape((n, 10) + y_shape)
                 y = self.xp.sum(y, axis=1) / 10
         return cuda.to_cpu(y)

tests/links_tests/model_tests/vgg_tests/test_vgg16.py

@@ -39,8 +39,9 @@ def test_call_gpu(self):
 
 
 @testing.parameterize(
-    {'feature': 'prob', 'shape': (2, 1000)},
-    {'feature': 'conv5_3', 'shape': (2, 512, 7, 7)}
+    {'feature': 'prob', 'shape': (2, 1000), 'do_ten_crop': False},
+    {'feature': 'prob', 'shape': (2, 1000), 'do_ten_crop': True},
+    {'feature': 'conv5_3', 'shape': (2, 512, 14, 14), 'do_ten_crop': False}
 )
 class TestVGG16LayersPredict(unittest.TestCase):
 
@@ -50,10 +51,7 @@ def setUp(self):
     def check_predict(self):
         x1 = np.random.uniform(0, 255, (3, 320, 240)).astype(np.float32)
         x2 = np.random.uniform(0, 255, (3, 320, 240)).astype(np.float32)
-        out = self.link.predict([x1, x2], oversample=False)
-        self.assertEqual(out.shape, self.shape)
-        self.assertEqual(out.dtype, np.float32)
-        result = self.link.predict([x1, x2], oversample=True)
+        out = self.link.predict([x1, x2], do_ten_crop=self.do_ten_crop)
         self.assertEqual(out.shape, self.shape)
         self.assertEqual(out.dtype, np.float32)