diff --git a/python/tvm/relay/op/contrib/tensorrt.py b/python/tvm/relay/op/contrib/tensorrt.py
index bda71468d9e2..db9684d02ac9 100644
--- a/python/tvm/relay/op/contrib/tensorrt.py
+++ b/python/tvm/relay/op/contrib/tensorrt.py
@@ -140,7 +140,11 @@ def partition_for_tensorrt(
             RemoveDropoutPass(),
             transform.RemoveUnusedFunctions(),
             transform.ConvertLayout(
-                {"nn.conv2d": ["NCHW", "default"], "nn.conv3d": ["NCDHW", "default"]}
+                {
+                    "nn.conv2d": ["NCHW", "default"],
+                    "nn.conv3d": ["NCDHW", "default"],
+                    "nn.conv2d_transpose": ["NCHW", "default"],
+                }
             ),
             transform.FoldConstant(),
             transform.AnnotateTarget("tensorrt"),
diff --git a/src/runtime/contrib/tensorrt/tensorrt_builder.cc b/src/runtime/contrib/tensorrt/tensorrt_builder.cc
index 4060b240cf8e..ee47e67001f3 100644
--- a/src/runtime/contrib/tensorrt/tensorrt_builder.cc
+++ b/src/runtime/contrib/tensorrt/tensorrt_builder.cc
@@ -91,10 +91,6 @@ void TensorRTBuilder::AddInput(int nid, uint32_t entry_id, const JSONGraphNode&
 void TensorRTBuilder::AddConstant(int nid, const DLTensor* data) {
   nvinfer1::Weights weight = GetDLTensorAsWeights(data, kDLCPU);
   std::vector<int> shape(data->shape, data->shape + data->ndim);
-  // Remove batch dim when not in explicit batch mode.
-  if (use_implicit_batch_ && shape.size() > 1 && shape[0] == 1) {
-    shape.erase(shape.begin());
-  }
   node_output_map_[nid] = {TensorRTOpInput(weight, shape)};
 }
 
@@ -212,8 +208,18 @@ nvinfer1::Weights TensorRTBuilder::GetDLTensorAsWeights(const DLTensor* dptr,
 
 nvinfer1::ITensor* TensorRTBuilder::GetInputAsTensor(const TensorRTOpInput& input) {
   if (input.type == kTensor) return input.tensor;
-  auto dims = VectorToTrtDims(input.weight_shape);
-  return network_->addConstant(dims, input.weight)->getOutput(0);
+  auto shape = input.weight_shape;
+  // Remove batch dim when not in explicit batch mode.
+  // Example:
+  // x = Relay dims (1, 32, 224, 224) which becomes TRT Dims (32, 224, 224)
+  // y = Relay dims (1, 32)
+  // z = add(x, y)
+  // y needs to have TRT dims (32,), otherwise broadcasting will result in z having
+  // TRT Dims(1, 32, 224, 224) when it should be (32, 224, 224).
+  if (use_implicit_batch_ && shape.size() > 1 && shape[0] == 1) {
+    shape.erase(shape.begin());
+  }
+  return network_->addConstant(VectorToTrtDims(shape), input.weight)->getOutput(0);
 }
 
 void TensorRTBuilder::CleanUp() {
diff --git a/tests/python/contrib/test_tensorrt.py b/tests/python/contrib/test_tensorrt.py
index 9b62ee2c4087..bd8d92eedb4c 100644
--- a/tests/python/contrib/test_tensorrt.py
+++ b/tests/python/contrib/test_tensorrt.py
@@ -385,6 +385,7 @@ def get_graph(
     run_and_verify_func(
         get_graph((1, 3, 16, 16), (3, 8, 7, 7), 3, [2, 2, 3, 3], [2, 2], [1, 1], 24)
     )
+    run_and_verify_func(get_graph((1, 3, 16, 16), (1, 3, 1, 1), channels=1))
 
 
 def test_conv2d_nhwc():
@@ -456,6 +457,7 @@ def get_graph(x_shape=(1, 16), k_shape=(32, 16)):
         return f, {"x": x_shape, "kernel": k_shape}, ["kernel"]
 
     run_and_verify_func(get_graph())
+    run_and_verify_func(get_graph(k_shape=(1, 16)))
 
 
 def test_bias_add():