yolo reorg op for relay (apache#1941)

docs/langref/relay_op.rst

@@ -135,6 +135,7 @@ This level enables additional math and transform operators.
    tvm.relay.vision.multibox_prior
    tvm.relay.vision.multibox_transform_loc
    tvm.relay.vision.nms
+   tvm.relay.vision.yolo_reorg
 
 
 **Level 10: Temporary Operators**
@@ -251,6 +252,7 @@ Level 5 Definitions
 .. autofunction:: tvm.relay.vision.multibox_prior
 .. autofunction:: tvm.relay.vision.multibox_transform_loc
 .. autofunction:: tvm.relay.vision.nms
+.. autofunction:: tvm.relay.vision.yolo_reorg
 
 
 Level 10 Definitions

include/tvm/relay/attrs/vision.h

@@ -98,6 +98,17 @@ struct ROIAlignAttrs : public tvm::AttrsNode<ROIAlignAttrs> {
   }
 };
 
+/*! \brief Attributes used in yolo reorg operators */
+struct YoloReorgAttrs : public tvm::AttrsNode<YoloReorgAttrs> {
+  Integer stride;
+
+  TVM_DECLARE_ATTRS(YoloReorgAttrs, "relay.attrs.YoloReorgAttrs") {
+    TVM_ATTR_FIELD(stride)
+      .set_default(1)
+      .describe("Stride value for yolo reorg");
+  }
+};
+
 }  // namespace relay
 }  // namespace tvm
 #endif  // TVM_RELAY_ATTRS_VISION_H_

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

@@ -5,5 +5,7 @@
 from .multibox import *
 from .nms import *
 from .rcnn import *
+from .yolo import *
 from . import _multibox
 from . import _rcnn
+from . import _yolo

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

@@ -0,0 +1,9 @@
+#pylint: disable=invalid-name, unused-argument
+"""Backend compiler related feature registration"""
+from __future__ import absolute_import
+from ..op import  register_schedule, register_pattern
+from ..op import schedule_injective, OpPattern
+
+# reorg
+register_pattern("vision.yolo_reorg", OpPattern.INJECTIVE)
+register_schedule("vision.yolo_reorg", schedule_injective)

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

@@ -0,0 +1,34 @@
+"""Yolo operations."""
+from . import _make
+
+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
+    ----------
+    data : relay.Expr
+        The input data tensor.
+
+    stride : int
+        The stride value for reorganisation.
+
+    Returns
+    -------
+    ret : relay.Expr
+        The computed result.
+    """
+    return _make.yolo_reorg(data, stride)

src/relay/op/vision/yolo.cc

@@ -0,0 +1,78 @@
+/*!
+ *  Copyright (c) 2018 by Contributors
+ * \file yolo.cc
+ * \brief Yolo related operators
+ */
+#include <tvm/relay/op.h>
+#include <tvm/relay/attrs/vision.h>
+#include <topi/vision/reorg.h>
+#include <vector>
+#include "../op_common.h"
+#include "../type_relations.h"
+
+namespace tvm {
+namespace relay {
+
+TVM_REGISTER_NODE_TYPE(YoloReorgAttrs);
+
+/*!
+* \brief YoloReorgRel Output type and shape relation evaluation function.
+* \param num_inputs Number of input types in the args.
+* \param attrs The additional attributes of the operator.
+* \param reporter The reporter to report solution to.
+* \return false if This relation cannot be resolved. true if this relation has been resolved.
+*/
+bool YoloReorgRel(const Array<Type>& types,
+                  int num_inputs,
+                  const Attrs& attrs,
+                  const TypeReporter& reporter) {
+  CHECK_EQ(types.size(), 2);
+  const auto* data = types[0].as<TensorTypeNode>();
+  if (data == nullptr) return false;
+
+  const YoloReorgAttrs* param = attrs.as<YoloReorgAttrs>();
+  CHECK(param != nullptr);
+
+  CHECK(data->shape.size() == 4) << "Yolo reorg supports only 4 dimension.";
+  std::vector<IndexExpr>&& oshape = AsVector(data->shape);
+  oshape[1] = oshape[1] * param->stride * param->stride;
+  oshape[2] = oshape[2] / param->stride;
+  oshape[3] = oshape[3] / param->stride;
+  reporter->Assign(types[1], TensorTypeNode::make(oshape, data->dtype));
+  return true;
+}
+
+Expr MakeYoloReorg(Expr data,
+                   Integer stride) {
+  auto attrs = make_node<YoloReorgAttrs>();
+  attrs->stride = stride;
+  static const Op& op = Op::Get("vision.yolo_reorg");
+  return CallNode::make(op, {data}, Attrs(attrs), {});
+}
+
+
+TVM_REGISTER_API("relay.op.vision._make.yolo_reorg")
+.set_body([](const TVMArgs& args, TVMRetValue* rv) {
+  runtime::detail::unpack_call<Expr, 2>(MakeYoloReorg, args, rv);
+});
+
+
+RELAY_REGISTER_OP("vision.yolo_reorg")
+.describe(R"doc("Yolo reorg operation. This layer reorganize the output.
+Its function is mostly shape transform.")doc" TVM_ADD_FILELINE)
+.add_argument("data", "Tensor", "The input tensor.")
+.set_num_inputs(1)
+.set_support_level(5)
+.set_attrs_type_key("relay.attrs.YoloReorgAttrs")
+.add_type_rel("YoloReorg", YoloReorgRel)
+.set_attr<FTVMCompute>("FTVMCompute", [](const Attrs& attrs,
+                                         const Array<Tensor>& inputs,
+                                         const Type& out_type,
+                                         const Target& target) {
+  const auto* params = attrs.as<YoloReorgAttrs>();
+  CHECK(params != nullptr);
+  return Array<Tensor>{ topi::vision::reorg(inputs[0], params->stride) };
+});
+
+}  // namespace relay
+}  // namespace tvm

tests/python/relay/test_op_level5.py

@@ -7,7 +7,6 @@
 from tvm.relay.testing import ctx_list
 import topi.testing
 
-
 def test_resize_infer_type():
     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), "int8"))
@@ -307,10 +306,46 @@ def verify_roi_align(data_shape, rois_shape, pooled_size, spatial_scale, sample_
     verify_roi_align((4, 4, 16, 16), (32, 5), pooled_size=7, spatial_scale=0.5, sample_ratio=2)
 
 
+def test_yolo_reorg_infer_shape():
+    def verify_yolo_reorg(shape, stride, out_shape):
+        x = relay.var("x", relay.TensorType(shape, "float32"))
+        z = relay.vision.yolo_reorg(x, stride=stride)
+        zz = relay.ir_pass.infer_type(z)
+        assert "stride=" in z.astext()
+        assert zz.checked_type == relay.ty.TensorType(out_shape, "float32")
+
+    n, c, h, w = tvm.var("n"), tvm.var("c"), tvm.var("h"), tvm.var("w")
+    verify_yolo_reorg((n, c, 20, 20), 10, (n, c*10*10, 2, 2))
+    verify_yolo_reorg((n, c, h, w), 2, (n, c*2*2, h/2, w/2))
+
+def test_yolo_reorg():
+    def verify_yolo_reorg(shape, stride):
+        x_data = np.random.uniform(low=-1, high=1, size=shape).astype("float32")
+        ref_res = topi.testing.reorg_python(x_data, stride)
+
+        x = relay.var("x", relay.TensorType(shape, "float32"))
+        z = relay.vision.yolo_reorg(x, stride=stride)
+        zz = relay.ir_pass.infer_type(z)
+        assert "stride=" in z.astext()
+        assert zz.checked_type == relay.ty.TensorType(ref_res.shape, "float32")
+
+        func = relay.Function([x], z)
+
+        for target, ctx in ctx_list():
+            for kind in ["graph", "debug"]:
+                intrp = relay.create_executor(kind, ctx=ctx, target=target)
+                op_res = intrp.evaluate(func)(x_data)
+                tvm.testing.assert_allclose(op_res.asnumpy(), ref_res, rtol=1e-5)
+
+    verify_yolo_reorg((1, 100, 20, 20), 10)
+    verify_yolo_reorg((1, 4, 6, 6), 2)
+
 if __name__ == "__main__":
     test_resize_infer_type()
     test_resize()
     test_multibox_prior()
     test_multibox_transform_loc()
     test_nms()
     test_roi_align()
+    test_yolo_reorg_infer_shape()
+    test_yolo_reorg()