delete relay adaptive pooling changes

Author: markrogersjr

include/tvm/relay/attrs/nn.h

@@ -332,6 +332,22 @@ struct GlobalPool2DAttrs : public tvm::AttrsNode<GlobalPool2DAttrs> {
   }
 };
 
+/*! \brief Attributes for adaptive pool operator */
+struct AdaptivePool2DAttrs : public tvm::AttrsNode<AdaptivePool2DAttrs> {
+  Array<IndexExpr> output_size;
+  std::string layout;
+
+  TVM_DECLARE_ATTRS(AdaptivePool2DAttrs, "relay.attrs.AdaptivePool2DAttrs") {
+    TVM_ATTR_FIELD(output_size).set_default(Array<IndexExpr>({}))
+      .describe("Output height and width.");
+    TVM_ATTR_FIELD(layout).set_default("NCHW")
+      .describe("Dimension ordering of data and weight. Can be 'NCHW', 'NHWC', etc."
+                  "'N', 'C', 'H', 'W' stands for batch, channel, height, and width"
+                  "dimensions respectively. Convolution is applied on the 'H' and"
+                  "'W' dimensions.");
+  }
+};
+
 
 /*! \brief Attributes for dense operator */
 struct DenseAttrs : public tvm::AttrsNode<DenseAttrs> {

python/tvm/relay/op/nn/_nn.py

@@ -247,7 +247,7 @@ def schedule_avg_pool2d(attrs, outs, target):
 def schedule_global_max_pool2d(_, outs, target):
     """Schedule definition of global_max_pool2d"""
     with target:
-        return topi.generic.schedule_global_pool(outs)
+        return topi.generic.schedule_adaptive_pool(outs)
 
 
 reg.register_pattern("nn.global_max_pool2d", OpPattern.OUT_ELEMWISE_FUSABLE)
@@ -258,7 +258,7 @@ def schedule_global_max_pool2d(_, outs, target):
 def schedule_global_avg_pool2d(_, outs, target):
     """Schedule definition of global_avg_pool2d"""
     with target:
-        return topi.generic.schedule_global_pool(outs)
+        return topi.generic.schedule_adaptive_pool(outs)
 
 
 reg.register_pattern("nn.global_avg_pool2d", OpPattern.OUT_ELEMWISE_FUSABLE)

src/relay/op/nn/pooling.cc

@@ -72,7 +72,6 @@ bool Pool2DRel(const Array<Type>& types,
 
   CHECK(data != nullptr);
   const auto dshape = data->shape;
-  CHECK_NE(dshape.size(), 0);
   CHECK_GE(dshape.size(), 2U)
       << "Pool2D only support input >= 2-D: input must have height and width";
   const auto param = attrs.as<AttrType>();
@@ -284,7 +283,6 @@ bool GlobalPool2DRel(const Array<Type>& types,
   const auto* data = types[0].as<TensorTypeNode>();
   if (data == nullptr) { return false; }
   const auto dshape = data->shape;
-  CHECK_NE(dshape.size(), 0);
   CHECK_GE(dshape.size(), 2U)
       << "Pool2D only support input >= 2-D: input must have height and width";
   const auto param = attrs.as<GlobalPool2DAttrs>();

tests/python/relay/test_op_level10.py

@@ -251,6 +251,7 @@ def test_adaptive_pool2d():
 
 
 if __name__ == "__main__":
+    test_adaptive_pool2d()
     test_collapse_sum_like()
     test_broadcast_to_like()
     test_slice_like()

tests/python/relay/test_op_level2.py

@@ -316,7 +316,6 @@ def test_avg_pool2d_no_count_pad():
         op_res1 = intrp1.evaluate(func)(data)
         tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-5, atol=1e-5)
 
-
 def test_flatten_infer_type():
     d1, d2, d3, d4 = tvm.var("d1"), tvm.var("d2"), tvm.var("d3"), tvm.var("d4")
     x = relay.var("x", relay.TensorType((d1, d2, d3, d4), "float32"))