[GitHub] [incubator-tvm] vinx13 commented on a change in pull request #4297: [TOPI][OP] Support Faster-RCNN Proposal OP on CPU

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vinx13 commented on a change in pull request #4297: [TOPI][OP] Support Faster-RCNN Proposal OP on CPU
URL: https://github.com/apache/incubator-tvm/pull/4297#discussion_r345583973
 
 

 ##########
 File path: topi/python/topi/vision/rcnn/proposal.py
 ##########
 @@ -60,6 +61,261 @@ def reg_iou(x1, y1, x2, y2, dx1, dy1, dx2, dy2):
     pred_y2 = y2 + dy2
     return pred_x1, pred_y1, pred_x2, pred_y2
 
+def predict_bbox_ir(cls_prob_buf, bbox_pred_buf, im_info_buf, out_buf, scales, ratios,
+                    feature_stride, rpn_min_size, iou_loss):
+    """Predict bounding boxes based on anchors, scores and deltas.
+
+    Parameters
+    ----------
+    cls_prob_buf : tvm.schedule.Buffer
+        4-D with shape [batch, 2 * num_anchors, height, width]
+
+    bbox_pred_buf : tvm.schedule.Buffer
+        4-D with shape [batch, 4 * num_anchors, height, width]
+
+    im_info_buf : tvm.schedule.Buffer
+        2-D with shape [batch, 3]
+
+    out_buf : tvm.schedule.Buffer
+        3-D with shape [batch, num_bbox, 5]
+        The last dimension is in format of [w_start, h_start, w_end, h_end, score]
+
+    scales : list/tuple of float
+        Scales of anchor windoes.
+
+    ratios : list/tuple of float
+        Ratios of anchor windoes.
+
+    feature_stride : int
+        The size of the receptive field each unit in the convolution layer of the rpn, for example
+        the product of all stride's prior to this layer.
+
+    rpn_min_size : int
+        Minimum height or width in proposal.
+
+    iou_loss : bool
+        Usage of IoU loss.
+
+    Returns
+    -------
+    stmt : Stmt
+        The result IR statement.
+    """
+    batch, num_anchors, height, width = get_const_tuple(cls_prob_buf.shape)
+    num_anchors //= 2
+    ib = tvm.ir_builder.create()
+
+    p_score = ib.buffer_ptr(cls_prob_buf)
+    p_delta = ib.buffer_ptr(bbox_pred_buf)
+    p_im_info = ib.buffer_ptr(im_info_buf)
+    p_out = ib.buffer_ptr(out_buf)
+
+    idxm = tvm.indexmod
+    idxd = tvm.indexdiv
+
+    with ib.for_range(0, batch * height * width) as tid:
 
 Review comment:
   I met these issue too when I trying to migrate proposal CUDA op to hybrid, I think it’s okay to use ir builder for the time being

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