[ConvertLayout] Resize operator

src/relay/op/image/resize.cc

@@ -33,6 +33,23 @@ namespace relay {
 
 TVM_REGISTER_NODE_TYPE(ResizeAttrs);
 
+Array<Array<Layout> > ResizeInferCorrectLayout(const Attrs& attrs,
+                                               const Array<Layout>& new_in_layouts,
+                                               const Array<Layout>& old_in_layouts,
+                                               const Array<tvm::relay::Type>& old_in_types) {
+  // NOTE: Discard "const" qualifier here.
+  ResizeAttrs* params = const_cast<ResizeAttrs*>(attrs.as<ResizeAttrs>());
+
+  if (new_in_layouts.defined() && new_in_layouts[0].defined()) {
+    // Set the resize with the new layout.
+    ICHECK_EQ(new_in_layouts.size(), 1);
+    params->layout = new_in_layouts[0].name();
+  }
+
+  Layout inferred_layout(params->layout);
+  return Array<Array<Layout> >{{inferred_layout}, {inferred_layout}};
+}
+
 bool ResizeRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
                const TypeReporter& reporter) {
   ICHECK_EQ(types.size(), 2);
@@ -98,6 +115,7 @@ RELAY_REGISTER_OP("image.resize")
     .add_argument("data", "Tensor", "The input tensor.")
     .set_support_level(5)
     .add_type_rel("Resize", ResizeRel)
+    .set_attr<FInferCorrectLayout>("FInferCorrectLayout", ResizeInferCorrectLayout)
     .set_attr<TOpPattern>("TOpPattern", kInjective);
 
 TVM_REGISTER_NODE_TYPE(Resize3dAttrs);

tests/python/relay/test_pass_convert_op_layout.py

@@ -244,6 +244,43 @@ def expected():
     assert tvm.ir.structural_equal(a, b), "Actual = \n" + str(a)
 
 
+def test_conv_resize_convert_layout():
+    def before():
+        x = relay.var("x", shape=(1, 56, 56, 64))
+        bias = relay.var("bias", shape=(64,))
+        weight = relay.var("weight", shape=(3, 3, 64, 64))
+        y = relay.nn.conv2d(
+            x,
+            weight,
+            channels=64,
+            kernel_size=(3, 3),
+            padding=(1, 1),
+            data_layout="NHWC",
+            kernel_layout="HWIO",
+        )
+        y = relay.image.resize(y, size=(56, 56), layout="NHWC")
+        y = relay.Function(analysis.free_vars(y), y)
+        return y
+
+    def expected():
+        x = relay.var("x", shape=(1, 56, 56, 64))
+        bias = relay.var("bias", shape=(64,))
+        weight = relay.var("weight", shape=(3, 3, 64, 64))
+        x = relay.layout_transform(x, "NHWC", "NCHW")
+        weight = relay.layout_transform(weight, "HWIO", "OIHW")
+        y = relay.nn.conv2d(x, weight, channels=64, kernel_size=(3, 3), padding=(1, 1))
+        y = relay.image.resize(y, size=(56, 56), layout="NCHW")
+        y = relay.layout_transform(y, "NCHW", "NHWC")
+        y = relay.Function(analysis.free_vars(y), y)
+        return y
+
+    a = before()
+    a = run_opt_pass(a, transform.ConvertLayout({"nn.conv2d": ["NCHW", "default"]}))
+    b = run_opt_pass(expected(), transform.InferType())
+
+    assert tvm.ir.structural_equal(a, b), "Actual = \n" + str(a)
+
+
 def test_conv_concat_convert_layout():
     def before():
         x = relay.var("x", shape=(1, 56, 56, 64))