diff --git a/python/tvm/relay/op/contrib/tensorrt.py b/python/tvm/relay/op/contrib/tensorrt.py
index acd4f4740b2da..c8f6557601197 100644
--- a/python/tvm/relay/op/contrib/tensorrt.py
+++ b/python/tvm/relay/op/contrib/tensorrt.py
@@ -341,6 +341,11 @@ def batch_norm_annotate_fn(expr):  # pylint: disable=unused-variable
     if any([x.checked_type.dtype != "float32" for x in args]):
         logger.info("Only float32 inputs are supported for TensorRT.")
         return False
+    if len(args[0].checked_type.shape) == 5 and get_tensorrt_version() < (6, 0, 1):
+        logger.info("nn.batch_norm: TensorRT 6.0.1 or higher is required for rank 5 inputs.")
+    if len(args[0].checked_type.shape) > 5:
+        logger.info("nn.batch_norm: Input rank must be 5 or less.")
+        return False
     if int(attrs.axis) not in (1, 3):
         logger.info("nn.batch_norm: axis is %d but must be 1 or 3.", int(attrs.axis))
         return False
diff --git a/src/runtime/contrib/tensorrt/tensorrt_ops.cc b/src/runtime/contrib/tensorrt/tensorrt_ops.cc
index c3ff1c45f50e2..db853d837edff 100644
--- a/src/runtime/contrib/tensorrt/tensorrt_ops.cc
+++ b/src/runtime/contrib/tensorrt/tensorrt_ops.cc
@@ -386,8 +386,35 @@ class BatchNormOpConverter : public TensorRTOpConverter {
     const int axis = std::stoi(params->node.GetAttr<std::vector<std::string>>("axis")[0]);
     const bool scale = std::stoi(params->node.GetAttr<std::vector<std::string>>("scale")[0]);
     const bool center = std::stoi(params->node.GetAttr<std::vector<std::string>>("center")[0]);
-    ICHECK(axis == 1 || axis == 3);
-    const bool need_transpose = axis == 3;
+    auto input_dims = TrtDimsToVector(input->getDimensions());
+    const size_t min_rank = TRT_HAS_IMPLICIT_BATCH(params) ? 3 : 4;
+    const size_t max_rank = TRT_HAS_IMPLICIT_BATCH(params) ? 4 : 5;
+    ICHECK_LE(input_dims.size(), max_rank);
+    const bool need_reshape = input_dims.size() < min_rank;
+    const bool need_transpose = axis != 1;
+
+    // Reshape if needed
+    if (need_reshape) {
+      // Add dims of size 1 until rank is required_rank.
+      std::vector<int> new_shape(input_dims);
+      while (new_shape.size() < min_rank) new_shape.insert(new_shape.end(), 1);
+      input = Reshape(params, input, new_shape);
+    }
+
+    // Transpose if needed.
+    const int input_rank_with_batch =
+        input->getDimensions().nbDims + (TRT_HAS_IMPLICIT_BATCH(params) ? 1 : 0);
+    ICHECK(input_rank_with_batch == 4 || input_rank_with_batch == 5);
+    std::vector<int> transpose_order(input_rank_with_batch);
+    if (need_transpose) {
+      // Move axis dim to first dim after batch.
+      for (int i = 0; i < input_rank_with_batch; ++i) {
+        transpose_order[i] = i;
+      }
+      transpose_order[1] = axis;
+      transpose_order[axis] = 1;
+      input = Transpose(params, input, transpose_order);
+    }
 
     void* weight_scale_ptr = new float[gamma.count];
     nvinfer1::Weights weight_scale{nvinfer1::DataType::kFLOAT, weight_scale_ptr, gamma.count};
@@ -414,15 +441,23 @@ class BatchNormOpConverter : public TensorRTOpConverter {
         shift_ptr[i] += beta_ptr[i];
       }
     }
-    if (need_transpose) {
-      input = Transpose(params, input, {0, 3, 1, 2});
-    }
-    nvinfer1::IScaleLayer* scale_layer = params->network->addScale(
+
+#if TRT_VERSION_GE(6, 0, 1)
+    const int channel_dim = TRT_HAS_IMPLICIT_BATCH(params) ? 0 : 1;
+    nvinfer1::IScaleLayer* scale_layer = params->network->addScaleNd(
+        *input, nvinfer1::ScaleMode::kCHANNEL, weight_shift, weight_scale, power, channel_dim);
+#else
+    ICHECK_EQ(input->getDimensions().nbDims(), 3);
+    nvinfer1::IScaleLayer* scale_layer = params->network->addScaleNd(
         *input, nvinfer1::ScaleMode::kCHANNEL, weight_shift, weight_scale, power);
+#endif
     ICHECK(scale_layer != nullptr);
     auto output = scale_layer->getOutput(0);
     if (need_transpose) {
-      output = Transpose(params, output, {0, 2, 3, 1});
+      output = Transpose(params, output, transpose_order);
+    }
+    if (need_reshape) {
+      output = Reshape(params, output, input_dims);
     }
     params->outputs.push_back(output);
   }
diff --git a/tests/python/contrib/test_tensorrt.py b/tests/python/contrib/test_tensorrt.py
index aadfa13036550..9b62ee2c40872 100644
--- a/tests/python/contrib/test_tensorrt.py
+++ b/tests/python/contrib/test_tensorrt.py
@@ -710,6 +710,12 @@ def get_graph(x_shape, param_shape, axis=1, epsilon=1e-5):
 
     run_and_verify_func(get_graph((1, 64, 56, 56), (64,)))
     run_and_verify_func(get_graph((1, 56, 56, 64), (64,), axis=3, epsilon=1.001e-05))
+    run_and_verify_func(get_graph((1, 4, 8, 4), (8,), axis=2))
+    run_and_verify_func(get_graph((1, 8, 4, 4, 4), (8,), axis=1))
+    run_and_verify_func(get_graph((1, 4, 8, 4, 4), (8,), axis=2))
+    run_and_verify_func(get_graph((1, 4, 4, 4, 8), (8,), axis=4))
+    run_and_verify_func(get_graph((1, 8), (8,), axis=1))
+    run_and_verify_func(get_graph((1, 3, 8), (8,), axis=2))
 
 
 def test_unary():