Review comments

docs/langref/relay_op.rst

@@ -132,9 +132,7 @@ This level enables additional math and transform operators.
    :nosignatures:
 
    tvm.relay.image.resize
-   tvm.relay.vision.yolo_regorg
-   tvm.relay.vision.yolo_region
-   tvm.relay.vision.yolov3_yolo
+   tvm.relay.vision.yolo_reorg
 
 
 **Level 10: Temporary Operators**
@@ -238,9 +236,7 @@ Level 4 Definitions
 Level 5 Definitions
 -------------------
 .. autofunction:: tvm.relay.image.resize
-   autofunction:: tvm.relay.vision.yolo_regorg
-   autofunction:: tvm.relay.vision.yolo_region
-   autofunction:: tvm.relay.vision.yolov3_yolo
+   autofunction:: tvm.relay.vision.yolo_reorg
 
 
 Level 10 Definitions

python/tvm/relay/op/vision/yolo.py

@@ -1,9 +1,22 @@
 """Yolo operations."""
 from . import _make
 
-def yolo_reorg(data, stride=1):
-    """Yolo reorg operation. This layer reorganize the output based on the stride value.
-    Its function is mostly shape transform.
+def yolo_reorg(data, stride):
+    """Yolo reorg operation used in darknet models.
+    This layer shuffles the input tensor values based on the stride value.
+    Along with the shuffling, it does the shape transform.
+    If '(n, c, h, w)' is the data shape and 's' is stride, output shape is '(n, c*s*s, h/s, w/s)'
+    Example: data(1, 4, 2, 2) = [[[[ 0  1] [ 2  3]]
+                                  [[ 4  5] [ 6  7]]
+                                  [[ 8  9] [10 11]]
+                                  [[12 13] [14 15]]]]
+             stride = 2
+             ret(1, 16, 1, 1) = [[[[ 0]]  [[ 2]]  [[ 8]]  [[10]]
+                                  [[ 1]]  [[ 3]]  [[ 9]]  [[11]]
+                                  [[ 4]]  [[ 6]]  [[12]]  [[14]]
+                                  [[ 5]]  [[ 7]]  [[13]]  [[15]]]]
+
+    Note: stride=1 has no significance for reorg operation.
 
     Parameters
     ----------
@@ -19,35 +32,3 @@ def yolo_reorg(data, stride=1):
         The computed result.
     """
     return _make.yolo_reorg(data, stride)
-
-
-def yolo_region(data):
-    """Yolo region operation used for detection.
-
-    Parameters
-    ----------
-    data : relay.Expr
-        The input data tensor.
-
-    Returns
-    -------
-    ret : relay.Expr
-        The computed result.
-    """
-    return _make.yolo_region(data)
-
-
-def yolov3_yolo(data):
-    """Yolo operation used for detection
-
-    Parameters
-    ----------
-    data : relay.Expr
-        The input data tensor.
-
-    Returns
-    -------
-    ret : relay.Expr
-        The computed result.
-    """
-    return _make.yolov3_yolo(data)

src/relay/op/vision/yolo.cc

@@ -65,47 +65,5 @@ Its function is mostly shape transform.")doc" TVM_ADD_FILELINE)
 .set_attrs_type_key("relay.attrs.YoloReorgAttrs")
 .add_type_rel("YoloReorg", YoloReorgRel);
 
-
-Expr MakeYoloRegion(Expr data) {
-  static const Op& op = Op::Get("vision.yolo_region");
-  return CallNode::make(op, {data}, Attrs(), {});
-}
-
-
-TVM_REGISTER_API("relay.op.vision._make.yolo_region")
-.set_body([](const TVMArgs& args, TVMRetValue* rv) {
-  runtime::detail::unpack_call<Expr, 1>(MakeYoloRegion, args, rv);
-});
-
-
-RELAY_REGISTER_OP("vision.yolo_region")
-.describe(R"doc("Yolo region operation used for detection."
-)doc" TVM_ADD_FILELINE)
-.add_argument("data", "Tensor", "The input tensor.")
-.set_num_inputs(1)
-.set_support_level(5)
-.add_type_rel("Identity", IdentityRel);
-
-
-Expr MakeYolov3Yolo(Expr data) {
-  static const Op& op = Op::Get("vision.yolov3_yolo");
-  return CallNode::make(op, {data}, Attrs(), {});
-}
-
-
-TVM_REGISTER_API("relay.op.vision._make.yolov3_yolo")
-.set_body([](const TVMArgs& args, TVMRetValue* rv) {
-  runtime::detail::unpack_call<Expr, 1>(MakeYolov3Yolo, args, rv);
-});
-
-
-RELAY_REGISTER_OP("vision.yolov3_yolo")
-.describe(R"doc("Yolov3 operation used for detection."
-)doc" TVM_ADD_FILELINE)
-.add_argument("data", "Tensor", "The input tensor.")
-.set_num_inputs(1)
-.set_support_level(5)
-.add_type_rel("Identity", IdentityRel);
-
 }  // namespace relay
 }  // namespace tvm

tests/python/relay/test_op_level5.py

@@ -72,38 +72,20 @@ def test_nms():
         (n, num_anchors, 6), "float32")
 def test_yolo_reorg():
     n, c, h, w = tvm.var("n"), tvm.var("c"), tvm.var("h"), tvm.var("w")
-    x = relay.var("x", relay.TensorType((n, c, h, w), "float32"))
-    z = relay.vision.yolo_reorg(x)
+    x = relay.var("x", relay.TensorType((n, c, 20, 20), "float32"))
+    z = relay.vision.yolo_reorg(x, stride=10)
     zz = relay.ir_pass.infer_type(z)
-    assert zz.checked_type == relay.ty.TensorType((n, c, h, w), "float32")
+    assert "stride=10" in z.astext()
+    assert zz.checked_type == relay.ty.TensorType((n, c*10*10, 2, 2), "float32")
 
     x = relay.var("x", relay.TensorType((n, c, h, w), "float32"))
     z = relay.vision.yolo_reorg(x, stride=2)
     assert "stride=2" in z.astext()
     zz = relay.ir_pass.infer_type(z)
     assert zz.checked_type == relay.ty.TensorType((n, c*2*2, h/2, w/2), "float32")
 
-
-def test_yolo_region():
-    n, c, h, w = tvm.var("n"), tvm.var("c"), tvm.var("h"), tvm.var("w")
-    x = relay.var("x", relay.TensorType((n, c, h, w), "float32"))
-    z = relay.vision.yolo_region(x)
-    zz = relay.ir_pass.infer_type(z)
-    assert zz.checked_type == relay.ty.TensorType((n, c, h, w), "float32")
-
-
-def test_yolov3_yolo():
-    n, c, h, w = tvm.var("n"), tvm.var("c"), tvm.var("h"), tvm.var("w")
-    x = relay.var("x", relay.TensorType((n, c, h, w), "float32"))
-    z = relay.vision.yolov3_yolo(x)
-    zz = relay.ir_pass.infer_type(z)
-    assert zz.checked_type == relay.ty.TensorType((n, c, h, w), "float32")
-
-
 if __name__ == "__main__":
     test_resize_infer_type()
     test_multibox_prior()
     test_nms()
     test_yolo_reorg()
-    test_yolo_region()
-    test_yolov3_yolo()