diff --git a/src/tim/vx/ops/deconv.cc b/src/tim/vx/ops/deconv.cc
index 66f2171..7d21bf5 100644
--- a/src/tim/vx/ops/deconv.cc
+++ b/src/tim/vx/ops/deconv.cc
@@ -57,7 +57,7 @@ DeConv2d::DeConv2d(Graph* graph, int32_t oc_count, PadType pad_type,
     group_(group) {
 
   // TODO(Sven): only support depthwise usage
-  assert(group != 1 && group == oc_count);
+  assert((group == 1U) || group == oc_count);
   this->impl()->node()->nn_param.deconv.ksize[0] = ksize_[0];
   this->impl()->node()->nn_param.deconv.ksize[1] = ksize_[1];
   this->impl()->node()->nn_param.deconv.stride[0] = stride_[0];
diff --git a/src/tim/vx/ops/deconv2d_test.cc b/src/tim/vx/ops/deconv2d_test.cc
index 8e6236f..f443f18 100644
--- a/src/tim/vx/ops/deconv2d_test.cc
+++ b/src/tim/vx/ops/deconv2d_test.cc
@@ -17,7 +17,7 @@ size_t element_count(const tim::vx::ShapeType& shape) {
 
 }  // namespace
 
-TEST(OP, deconv_group) {
+TEST(OP, deconv_depthwise_two_channel) {
     auto ctx = tim::vx::Context::Create();
     auto graph = ctx->CreateGraph();
 
@@ -82,5 +82,57 @@ TEST(OP, deconv_group) {
                         0.0f, 0.0f, 40.0f, 16.0f, 24.0f,
                         0.0f, 0.0f, 72.0f, 0.0f, 16.0f};
 
+    EXPECT_EQ(golden, output_data) << "Result mismatch";
+}
+
+TEST(OP, deconv_single_channel) {
+    auto ctx = tim::vx::Context::Create();
+    auto graph = ctx->CreateGraph();
+
+    tim::vx::ShapeType input_shape ({3, 3, 1, 1});  //whcn
+    tim::vx::ShapeType kernel_shape({3, 3, 1, 1});  //whc1 same as depthwise convolution
+    tim::vx::ShapeType output_shape({5, 5, 1, 1});  //whcn
+
+    tim::vx::TensorSpec input_spec  (tim::vx::DataType::FLOAT32, input_shape, tim::vx::TensorAttribute::INPUT);
+    tim::vx::TensorSpec kernel_spec (tim::vx::DataType::FLOAT32, kernel_shape, tim::vx::TensorAttribute::CONSTANT);
+    tim::vx::TensorSpec output_spec (tim::vx::DataType::FLOAT32, output_shape, tim::vx::TensorAttribute::OUTPUT);
+
+    auto input_tensor = graph->CreateTensor(input_spec);
+    auto output_tensor = graph->CreateTensor(output_spec);
+    auto kernel_tensor = graph->CreateTensor(kernel_spec);
+
+    std::vector<float> input_data = {
+        3.0f, 8.0f, 1.0f, 9.0f, 5.0f, 7.0f, 3.0f, 2.0f, 3.0f,
+    };
+
+    std::vector<float> kernel_data = {
+        9.0f, 0.0f, 3.0f, 0.0f, 0.0f, 0.0f, 1.0f, 0.0f, 2.0f,
+    };
+
+    std::vector<float> output_data(element_count(output_shape));
+
+    EXPECT_TRUE(input_tensor->CopyDataToTensor(input_data.data(), input_data.size()*4));
+    EXPECT_TRUE(kernel_tensor->CopyDataToTensor(kernel_data.data(), kernel_data.size()*4));
+
+    auto add = graph->CreateOperation<tim::vx::ops::DeConv2d>(
+        1,
+        tim::vx::PadType::SAME,
+        std::array<uint32_t, 2>({3, 3}),    /*ksize*/
+        std::array<uint32_t, 2>({1, 1}),    /*stride*/
+        std::array<uint32_t, 2>({1, 1}),    /*dilation*/
+        std::array<uint32_t, 4>({0, 0, 0, 0}), /*pad*/
+        1/*group*/);
+    (*add).BindInputs({input_tensor, kernel_tensor}).BindOutputs({output_tensor});
+
+    EXPECT_TRUE(graph->Compile());
+    EXPECT_TRUE(graph->Run());
+
+    EXPECT_TRUE(output_tensor->CopyDataFromTensor(output_data.data()));
+    std::vector<float> golden = {
+        27.0f, 72.0f, 18.0f, 24.0f, 3.0f,  81.0f, 45.0f, 90.0f, 15.0f,
+        21.0f, 30.0f, 26.0f, 43.0f, 22.0f, 11.0f, 9.0f,  5.0f,  25.0f,
+        10.0f, 14.0f, 3.0f,  2.0f,  9.0f,  4.0f,  6.0f,
+    };
+
     EXPECT_EQ(golden, output_data) << "Result mismatch";
 }
\ No newline at end of file