refactor bbox_transform_inv

toshihiroryuu · Aug 24, 2017 · 64f76f2 · 64f76f2
1 parent e55e598
commit 64f76f2
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Showing 2 changed files with 16 additions and 16 deletions.
diff --git a/keras_rcnn/backend/common.py b/keras_rcnn/backend/common.py
@@ -195,23 +195,23 @@ def smooth_l1_loss(y_true, y_pred):
     return keras.backend.sum(x)
 
 
-def bbox_transform_inv(shifted, boxes):
+def bbox_transform_inv(boxes, deltas):
     def shape_zero():
-        x = keras.backend.int_shape(boxes)[-1]
+        x = keras.backend.int_shape(deltas)[-1]
 
         return keras.backend.zeros_like(x, dtype=keras.backend.floatx())
 
     def shape_non_zero():
-        a = shifted[:, 2] - shifted[:, 0] + 1.0
-        b = shifted[:, 3] - shifted[:, 1] + 1.0
+        a = boxes[:, 2] - boxes[:, 0] + 1.0
+        b = boxes[:, 3] - boxes[:, 1] + 1.0
 
-        ctr_x = shifted[:, 0] + 0.5 * a
-        ctr_y = shifted[:, 1] + 0.5 * b
+        ctr_x = boxes[:, 0] + 0.5 * a
+        ctr_y = boxes[:, 1] + 0.5 * b
 
-        dx = boxes[:, 0::4]
-        dy = boxes[:, 1::4]
-        dw = boxes[:, 2::4]
-        dh = boxes[:, 3::4]
+        dx = deltas[:, 0::4]
+        dy = deltas[:, 1::4]
+        dw = deltas[:, 2::4]
+        dh = deltas[:, 3::4]
 
         pred_ctr_x = dx * a[:, keras_rcnn.backend.newaxis] + ctr_x[:, keras_rcnn.backend.newaxis]
         pred_ctr_y = dy * b[:, keras_rcnn.backend.newaxis] + ctr_y[:, keras_rcnn.backend.newaxis]
@@ -220,11 +220,11 @@ def shape_non_zero():
         pred_h = keras.backend.exp(dh) * b[:, keras_rcnn.backend.newaxis]
 
 
-        indices = keras.backend.tile(keras.backend.arange(0, keras.backend.shape(boxes)[0]), [4])
+        indices = keras.backend.tile(keras.backend.arange(0, keras.backend.shape(deltas)[0]), [4])
         indices = keras.backend.reshape(indices, (-1, 1))
-        indices = keras.backend.tile(indices, [1, keras.backend.shape(boxes)[-1] // 4])
+        indices = keras.backend.tile(indices, [1, keras.backend.shape(deltas)[-1] // 4])
         indices = keras.backend.reshape(indices, (-1, 1))
-        indices_coords = keras.backend.tile(keras.backend.arange(0, keras.backend.shape(boxes)[1], step = 4), [keras.backend.shape(boxes)[0]])
+        indices_coords = keras.backend.tile(keras.backend.arange(0, keras.backend.shape(deltas)[1], step = 4), [keras.backend.shape(deltas)[0]])
         indices_coords = keras.backend.concatenate([indices_coords, indices_coords + 1, indices_coords + 2, indices_coords + 3], 0)
         indices = keras.backend.concatenate([indices, keras.backend.expand_dims(indices_coords)], axis=1)
 
@@ -233,11 +233,11 @@ def shape_non_zero():
                                              keras.backend.reshape(pred_ctr_y - 0.5 * pred_h, (-1,)),
                                              keras.backend.reshape(pred_ctr_x + 0.5 * pred_w, (-1,)),
                                              keras.backend.reshape(pred_ctr_y + 0.5 * pred_h, (-1,))], axis=0)
-        pred_boxes = keras_rcnn.backend.scatter_add_tensor(keras.backend.zeros_like(boxes), indices, updates)
+        pred_boxes = keras_rcnn.backend.scatter_add_tensor(keras.backend.zeros_like(deltas), indices, updates)
         return pred_boxes
 
 
-    zero_boxes = keras.backend.equal(keras.backend.shape(boxes)[0], 0)
+    zero_boxes = keras.backend.equal(keras.backend.shape(deltas)[0], 0)
 
     pred_boxes = keras.backend.switch(zero_boxes, shape_zero, shape_non_zero)
 

diff --git a/keras_rcnn/layers/box_regression.py b/keras_rcnn/layers/box_regression.py
@@ -5,7 +5,7 @@
 
 class BoxRegression(keras.engine.topology.Layer):
     """
-    Get final detections + labels by unscaling back to image space, performing box regression, choosing box coordinates, and removing extra detections via NMS
+    Get final detections + labels by unscaling back to image space, applying regression deltas, choosing box coordinates, and removing extra detections via NMS
     """
     def __init__(self, threshold = 0.05, test_nms = 0.5, **kwargs):
         self.threshold = threshold