[dev] rcnn support bs>1 (#3174)

* rcnn bs>1

* delete redundant comments

ppdet/modeling/layers.py

@@ -342,7 +342,11 @@ class RCNNBox(object):
         origin_shape = paddle.floor(im_shape / scale_factor + 0.5)
         scale_list = []
         origin_shape_list = []
-        for idx, roi_per_im in enumerate(roi):
+        
+        batch_size = paddle.slice(paddle.shape(im_shape), [0], [0], [1])
+        # bbox_pred.shape: [N, C*4]
+        for idx in range(batch_size):
+            roi_per_im = roi[idx]
             rois_num_per_im = rois_num[idx]
             expand_im_shape = paddle.expand(im_shape[idx, :],
                                             [rois_num_per_im, 2])

ppdet/modeling/post_process.py

@@ -35,7 +35,7 @@ __all__ = [
 
 
 @register
-class BBoxPostProcess(object):
+class BBoxPostProcess(nn.Layer):
     __shared__ = ['num_classes']
     __inject__ = ['decode', 'nms']
 
@@ -44,8 +44,14 @@ class BBoxPostProcess(object):
         self.num_classes = num_classes
         self.decode = decode
         self.nms = nms
+        self.fake_bboxes = paddle.to_tensor(
+            np.array(
+                [[-1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]],
+                dtype='float32'))
+        self.fake_bbox_num = paddle.to_tensor(np.array([1], dtype='int32'))
+
 
-    def __call__(self, head_out, rois, im_shape, scale_factor):
+    def forward(self, head_out, rois, im_shape, scale_factor):
         """
         Decode the bbox and do NMS if needed. 
 
@@ -90,10 +96,8 @@ class BBoxPostProcess(object):
         """
 
         if bboxes.shape[0] == 0:
-            bboxes = paddle.to_tensor(
-                np.array(
-                    [[-1, 0.0, 0.0, 0.0, 0.0, 0.0]], dtype='float32'))
-            bbox_num = paddle.to_tensor(np.array([1], dtype='int32'))
+            bboxes = self.fake_bboxes
+            bbox_num = self.fake_bbox_num
 
         origin_shape = paddle.floor(im_shape / scale_factor + 0.5)
 

ppdet/modeling/proposal_generator/rpn_head.py

@@ -133,21 +133,14 @@ class RPNHead(nn.Layer):
 
         anchors = self.anchor_generator(rpn_feats)
 
-        # TODO: Fix batch_size > 1 when testing.
-        if self.training:
-            batch_size = inputs['im_shape'].shape[0]
-        else:
-            batch_size = 1
-
-        rois, rois_num = self._gen_proposal(scores, deltas, anchors, inputs,
-                                            batch_size)
+        rois, rois_num = self._gen_proposal(scores, deltas, anchors, inputs)
         if self.training:
             loss = self.get_loss(scores, deltas, anchors, inputs)
             return rois, rois_num, loss
         else:
             return rois, rois_num, None
 
-    def _gen_proposal(self, scores, bbox_deltas, anchors, inputs, batch_size):
+    def _gen_proposal(self, scores, bbox_deltas, anchors, inputs):
         """
         scores (list[Tensor]): Multi-level scores prediction
         bbox_deltas (list[Tensor]): Multi-level deltas prediction
@@ -161,6 +154,7 @@ class RPNHead(nn.Layer):
         # Get 'topk' of them as final output
         bs_rois_collect = []
         bs_rois_num_collect = []
+        batch_size = paddle.slice(paddle.shape(im_shape), [0], [0], [1])
 
         # Generate proposals for each level and each batch.
         # Discard batch-computing to avoid sorting bbox cross different batches.