add RoIAlign for faster RCNN model (#1368)

* add RoIAlign and affine_channel for faster RCNN model

* add affine_channel for faster RCNN model

fluid/faster_rcnn/config.py

@@ -144,6 +144,18 @@ _C.variances = [1., 1., 1., 1.]
 # stride of feature map
 _C.rpn_stride = [16.0, 16.0]
 
+# Use roi pool or roi align, 'RoIPool' or 'RoIAlign'
+_C.roi_func = 'RoIAlign'
+
+# sampling ratio for roi align
+_C.sampling_ratio = 0
+
+# pooled width and pooled height 
+_C.roi_resolution = 14
+
+# spatial scale 
+_C.spatial_scale = 1. / 16.
+
 #
 # SOLVER options
 #

fluid/faster_rcnn/models/model_builder.py

@@ -196,12 +196,21 @@ class FasterRCNN(object):
             pool_rois = self.rois
         else:
             pool_rois = self.rpn_rois
-        pool = fluid.layers.roi_pool(
-            input=roi_input,
-            rois=pool_rois,
-            pooled_height=14,
-            pooled_width=14,
-            spatial_scale=0.0625)
+        if cfg.roi_func == 'RoIPool':
+            pool = fluid.layers.roi_pool(
+                input=roi_input,
+                rois=pool_rois,
+                pooled_height=cfg.roi_resolution,
+                pooled_width=cfg.roi_resolution,
+                spatial_scale=cfg.spatial_scale)
+        elif cfg.roi_func == 'RoIAlign':
+            pool = fluid.layers.roi_align(
+                input=roi_input,
+                rois=pool_rois,
+                pooled_height=cfg.roi_resolution,
+                pooled_width=cfg.roi_resolution,
+                spatial_scale=cfg.spatial_scale,
+                sampling_ratio=cfg.sampling_ratio)
         rcnn_out = self.add_roi_box_head_func(pool)
         self.cls_score = fluid.layers.fc(input=rcnn_out,
                                          size=cfg.class_num,

fluid/faster_rcnn/models/resnet.py

@@ -89,8 +89,7 @@ def conv_affine_layer(input,
         default_initializer=Constant(0.))
     bias.stop_gradient = True
 
-    elt_mul = fluid.layers.elementwise_mul(x=conv, y=scale, axis=1)
-    out = fluid.layers.elementwise_add(x=elt_mul, y=bias, axis=1)
+    out = fluid.layers.affine_channel(x=conv, scale=scale, bias=bias)
     if act == 'relu':
         out = fluid.layers.relu(x=out)
     return out