Support fuse bn into ConvTranspose.

Signed-off-by: wenyuchi.wyc <[email protected]>

Author: wenyuchi.wyc

onnxoptimizer/passes/fuse_bn_into_conv.h

@@ -47,7 +47,7 @@ struct FuseBNIntoConv final : public PredicateBasedPass {
     return "fuse_bn_into_conv";
   }
 
-  bool modify_conv(Node* conv, Node* bn, Graph& graph) {
+  bool modify_conv(Node* conv, Node* bn, Graph& graph, const bool is_conv) {
     const auto& bn_inputs = bn->inputs();
     const auto& conv_inputs = conv->inputs();
 
@@ -123,10 +123,9 @@ struct FuseBNIntoConv final : public PredicateBasedPass {
     Node* unsqueeze = graph.create(kUnsqueeze, 1);
     unsqueeze->insertAfter(scale);
     unsqueeze->addInput(scale->output());
-    std::vector<int64_t> insert_dims;
-    for (int i = 1; i < conv_W.sizes().size(); ++i) {
-      insert_dims.push_back(i);
-    }
+    std::vector<int64_t> insert_dims(conv_W.sizes().size());
+    std::iota(insert_dims.begin(), insert_dims.end(), 0);
+    insert_dims.erase(insert_dims.begin() + (is_conv ? 0 : 1));
     if (getOpsetVersion(graph) > 11) {
       Tensor shape_s_t;
       shape_s_t.elem_type() = ONNX_NAMESPACE::TensorProto_DataType_INT64;
@@ -181,7 +180,8 @@ struct FuseBNIntoConv final : public PredicateBasedPass {
   }
 
   bool patternMatchPredicate(Node* n) override {
-    return CheckKind(n, kBatchNormalization, 0, kConv) &&
+    return (CheckKind(n, kBatchNormalization, 0, kConv) ||
+           CheckKind(n, kBatchNormalization, 0, kConvTranspose)) &&
            GetValueFromAttrWithDefault(n, "training_mode", (int64_t)0) == 0 &&
            n->input(0)->uses().size() == 1 && n->outputs().size() == 1 &&
            IsConstantTensor(n, 1) && IsConstantTensor(n, 2) &&
@@ -190,10 +190,12 @@ struct FuseBNIntoConv final : public PredicateBasedPass {
   }
   bool runTransform(Node* n, Graph& graph,
                     NodeDestroyType& destroy_current) override {
+    const bool is_conv = CheckKind(n, kBatchNormalization, 0, kConv);
+
     Node* bn = n;
     Node* conv = PrevNode(n, 0);
     auto origInput = bn->inputs()[0];
-    if (!modify_conv(conv, bn, graph)) {
+    if (!modify_conv(conv, bn, graph, is_conv)) {
       destroy_current = NodeDestroyType::DestroyZero;
       return false;
     }

onnxoptimizer/test/optimizer_test.py

@@ -3063,6 +3063,46 @@ def test_fuse_bn_into_conv_simple(self):  # type: () -> None
             )
             optimized_model = self._optimized(graph, ["fuse_bn_into_conv"]) # noqa
 
+    def test_fuse_bn_into_conv_transpose_simple(self):  # type: () -> None
+        for (tensor_type, np_type) in [(TensorProto.FLOAT, np.float32)]:
+            conv = helper.make_node("ConvTranspose", ["X", "W", "B"], ["Y"])
+            bn = helper.make_node(
+                "BatchNormalization", ["Y", "scale", "b", "mean", "var"], ["Z"]
+            )
+
+            W = np.random.randn(64, 64, 2, 2).astype(np_type) + 2
+            B = np.random.randn(64,).astype(np_type) + 2
+            scale = np.random.randn(64,).astype(np_type) + 2
+            b = np.random.randn(64,).astype(np_type) + 2
+            mean = np.random.randn(64,).astype(np_type) + 2
+            var = np.abs(np.random.randn(64,).astype(np_type)) + 2
+
+            initializers = [
+                helper.make_tensor(
+                    name, tensor_type, npa.shape, npa.tobytes(), raw=True
+                )
+                for name, npa in [
+                    ("W", W),
+                    ("B", B),
+                    ("scale", scale),
+                    ("b", b),
+                    ("mean", mean),
+                    ("var", var),
+                ]
+            ]
+            graph = helper.make_graph(
+                [conv, bn],
+                "test",
+                [helper.make_tensor_value_info("X", tensor_type, (1, 64, 160, 160))],
+                [helper.make_tensor_value_info("Z", tensor_type, (1, 64, 320, 320))],
+                initializer=initializers,
+                value_info=[
+                    helper.make_tensor_value_info("Y", tensor_type, (1, 64, 320, 320))
+                ],
+            )
+
+            optimized_model = self._optimized(graph, ["fuse_bn_into_conv"])
+
     def _internal_test_deadend_elimination(self, fixed):  # type: (bool) -> None
         softmax = helper.make_node("Softmax", ["X"], ["Y"], axis=2)
         log = helper.make_node("Log", ["Y"], ["Z"])