use scales (tuple of floats) in region_proposal_network

chainercv/links/model/faster_rcnn/faster_rcnn.py

@@ -112,10 +112,10 @@ def n_class(self):
         # Total number of classes including the background.
         return self.head.n_class
 
-    def __call__(self, x, scale=1.):
+    def __call__(self, x, scales):
         """Forward Faster R-CNN.
 
-        Scaling paramter :obj:`scale` is used by RPN to determine the
+        Scaling paramter :obj:`scales` is used by RPN to determine the
         threshold to select small objects, which are going to be
         rejected irrespective of their confidence scores.
 
@@ -132,8 +132,8 @@ def __call__(self, x, scale=1.):
 
         Args:
             x (~chainer.Variable): 4D image variable.
-            scale (float): Amount of scaling applied to the raw image
-                during preprocessing.
+            scales (tuple of floats): Amount of scaling applied to each input
+                image during preprocessing.
 
         Returns:
             Variable, Variable, array, array:
@@ -153,7 +153,7 @@ def __call__(self, x, scale=1.):
 
         h = self.extractor(x)
         rpn_locs, rpn_scores, rois, roi_indices, anchor =\
-            self.rpn(h, img_size, scale)
+            self.rpn(h, img_size, scales)
         roi_cls_locs, roi_scores = self.head(
             h, rois, roi_indices)
         return roi_cls_locs, roi_scores, rois, roi_indices
@@ -286,7 +286,7 @@ def predict(self, imgs):
                 img_var = chainer.Variable(self.xp.asarray(img[None]))
                 scale = img_var.shape[3] / size[1]
                 roi_cls_locs, roi_scores, rois, _ = self.__call__(
-                    img_var, scale=scale)
+                    img_var, scales=[scale])
             # We are assuming that batch size is 1.
             roi_cls_loc = roi_cls_locs.array
             roi_score = roi_scores.array

chainercv/links/model/faster_rcnn/faster_rcnn_train_chain.py

@@ -60,7 +60,7 @@ def __init__(self, faster_rcnn, rpn_sigma=3., roi_sigma=1.,
         self.loc_normalize_mean = faster_rcnn.loc_normalize_mean
         self.loc_normalize_std = faster_rcnn.loc_normalize_std
 
-    def __call__(self, imgs, bboxes, labels, scale):
+    def __call__(self, imgs, bboxes, labels, scales):
         """Forward Faster R-CNN and calculate losses.
 
         Here are notations used.
@@ -79,8 +79,8 @@ def __call__(self, imgs, bboxes, labels, scale):
                 the definition, which means that the range of the value
                 is :math:`[0, L - 1]`. :math:`L` is the number of foreground
                 classes.
-            scale (float or ~chainer.Variable): Amount of scaling applied to
-                the raw image during preprocessing.
+            scales (~chainer.Variable): Amount of scaling applied to
+                each input image during preprocessing.
 
         Returns:
             chainer.Variable:
@@ -93,9 +93,9 @@ def __call__(self, imgs, bboxes, labels, scale):
             bboxes = bboxes.array
         if isinstance(labels, chainer.Variable):
             labels = labels.array
-        if isinstance(scale, chainer.Variable):
-            scale = scale.array
-        scale = np.asscalar(cuda.to_cpu(scale))
+        if isinstance(scales, chainer.Variable):
+            scales = scales.array
+        scales = cuda.to_cpu(scales)
         n = bboxes.shape[0]
         if n != 1:
             raise ValueError('Currently only batch size 1 is supported.')
@@ -105,7 +105,7 @@ def __call__(self, imgs, bboxes, labels, scale):
 
         features = self.faster_rcnn.extractor(imgs)
         rpn_locs, rpn_scores, rois, roi_indices, anchor = self.faster_rcnn.rpn(
-            features, img_size, scale)
+            features, img_size, scales)
 
         # Since batch size is one, convert variables to singular form
         bbox = bboxes[0]

chainercv/links/model/faster_rcnn/region_proposal_network.py

@@ -67,7 +67,7 @@ def __init__(
             self.loc = L.Convolution2D(
                 mid_channels, n_anchor * 4, 1, 1, 0, initialW=initialW)
 
-    def __call__(self, x, img_size, scale=1.):
+    def __call__(self, x, img_size, scales):
         """Forward Region Proposal Network.
 
         Here are notations.
@@ -82,8 +82,8 @@ def __call__(self, x, img_size, scale=1.):
                 Its shape is :math:`(N, C, H, W)`.
             img_size (tuple of ints): A tuple :obj:`height, width`,
                 which contains image size after scaling.
-            scale (float): The amount of scaling done to the input images after
-                reading them from files.
+            scales (tuple of floats): The amount of scaling done to each input
+                image during preprocessing.
 
         Returns:
             (~chainer.Variable, ~chainer.Variable, array, array, array):
@@ -127,7 +127,7 @@ def __call__(self, x, img_size, scale=1.):
         for i in range(n):
             roi = self.proposal_layer(
                 rpn_locs[i].array, rpn_fg_scores[i].array, anchor, img_size,
-                scale=scale)
+                scale=scales[i])
             batch_index = i * self.xp.ones((len(roi),), dtype=np.int32)
             rois.append(roi)
             roi_indices.append(batch_index)

tests/links_tests/model_tests/faster_rcnn_tests/test_faster_rcnn.py

@@ -37,7 +37,8 @@ def check_call(self):
         xp = self.link.xp
 
         x1 = chainer.Variable(_random_array(xp, (1, 3, 600, 800)))
-        roi_cls_locs, roi_scores, rois, roi_indices = self.link(x1)
+        scales = chainer.Variable(xp.array([1.], dtype=np.float32))
+        roi_cls_locs, roi_scores, rois, roi_indices = self.link(x1, scales)
 
         self.assertIsInstance(roi_cls_locs, chainer.Variable)
         self.assertIsInstance(roi_cls_locs.array, xp.ndarray)

tests/links_tests/model_tests/faster_rcnn_tests/test_region_proposal_network.py

@@ -34,13 +34,14 @@ def setUp(self):
         )
         self.x = np.random.uniform(size=(self.B, C, H, W)).astype(np.float32)
         self.img_size = (H * feat_stride, W * feat_stride)
+        self.scales = np.ones((self.B, ), dtype=np.float32)
 
         chainer.config.train = self.train
 
-    def _check_call(self, x, img_size):
+    def _check_call(self, x, img_size, scales):
         _, _, H, W = x.shape
         rpn_locs, rpn_scores, rois, roi_indices, anchor = self.link(
-            chainer.Variable(x), img_size)
+            chainer.Variable(x), img_size, scales)
         self.assertIsInstance(rpn_locs, chainer.Variable)
         self.assertIsInstance(rpn_locs.array, type(x))
         self.assertIsInstance(rpn_scores, chainer.Variable)
@@ -74,13 +75,13 @@ def _check_call(self, x, img_size):
         self.assertEqual(anchor.shape, (A * H * W, 4))
 
     def test_call_cpu(self):
-        self._check_call(self.x, self.img_size)
+        self._check_call(self.x, self.img_size, self.scales)
 
     @attr.gpu
     def test_call_gpu(self):
         self.link.to_gpu()
         self._check_call(
-            chainer.backends.cuda.to_gpu(self.x), self.img_size)
+            chainer.backends.cuda.to_gpu(self.x), self.img_size, self.scales)
 
 
 testing.run_module(__name__, __file__)