COMPMID-1803: Add mobilenetv2 qasymm8 example

Change-Id: Ic9f3d3b4ed452efd1bea4d79d235350c8e521bc9
Reviewed-on: https://review.mlplatform.org/312
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Giuseppe Rossini <giuseppe.rossini@arm.com>
Reviewed-by: Anthony Barbier <Anthony.barbier@arm.com>

1 files changed, 271 insertions, 58 deletions

diff --git a/examples/graph_mobilenet_v2.cpp b/examples/graph_mobilenet_v2.cpp
index 8c3433cac2..2f15d0b0ea 100644
--- a/examples/graph_mobilenet_v2.cpp
+++ b/examples/graph_mobilenet_v2.cpp
@@ -62,18 +62,76 @@ public:
         }
 
         // Checks
-        ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "QASYMM8 not supported for this graph");
+        ARM_COMPUTE_EXIT_ON_MSG(common_params.data_type == DataType::QASYMM8 && common_params.target == Target::NEON, "QASYMM8 NEON not supported for this graph");
 
         // Print parameter values
         std::cout << common_params << std::endl;
 
-        // Create model path
-        std::string model_path = "/cnn_data/mobilenet_v2_1.0_224_model/";
-
         // Create input descriptor
         const TensorShape tensor_shape     = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, common_params.data_layout);
         TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
 
+        // Set graph hints
+        graph << common_params.target
+              << DepthwiseConvolutionMethod::Optimized3x3 // FIXME(COMPMID-1073): Add heuristics to automatically call the optimized 3x3 method
+              << common_params.fast_math_hint;
+
+        // Create core graph
+        if(arm_compute::is_data_type_float(common_params.data_type))
+        {
+            create_graph_float(input_descriptor);
+        }
+        else
+        {
+            create_graph_qasymm8(input_descriptor);
+        }
+        // Create common tail
+        graph << ReshapeLayer(TensorShape(1001U)).set_name("Predictions/Reshape")
+              << SoftmaxLayer().set_name("Predictions/Softmax")
+              << OutputLayer(get_output_accessor(common_params, 5));
+
+        // Finalize graph
+        GraphConfig config;
+        config.num_threads = common_params.threads;
+        config.use_tuner   = common_params.enable_tuner;
+        config.tuner_file  = common_params.tuner_file;
+
+        graph.finalize(common_params.target, config);
+
+        return true;
+    }
+
+    void do_run() override
+    {
+        // Run graph
+        graph.run();
+    }
+
+private:
+    CommandLineParser  cmd_parser;
+    CommonGraphOptions common_opts;
+    CommonGraphParams  common_params;
+    Stream             graph;
+
+private:
+    enum class IsResidual
+    {
+        Yes,
+        No
+    };
+
+    enum class HasExpand
+    {
+        Yes,
+        No
+    };
+
+private:
+    void create_graph_float(TensorDescriptor &input_descriptor)
+    {
+        // Create model path
+        const std::string model_path = "/cnn_data/mobilenet_v2_1.0_224_model/";
+
         // Create a preprocessor object
         std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>();
 
@@ -86,11 +144,7 @@ public:
             data_path += model_path;
         }
 
-        // Create graph
-        graph << common_params.target
-              << DepthwiseConvolutionMethod::Optimized3x3 // FIXME(COMPMID-1073): Add heuristics to automatically call the optimized 3x3 method
-              << common_params.fast_math_hint
-              << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor), false))
+        graph << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor), false))
               << ConvolutionLayer(3U, 3U, 32U,
                                   get_weights_accessor(data_path, "Conv_weights.npy", DataLayout::NCHW),
                                   std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
@@ -105,23 +159,23 @@ public:
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
               .set_name("Conv/Relu6");
 
-        get_expanded_conv(data_path, "expanded_conv", 32U, 16U, PadStrideInfo(1, 1, 1, 1));
-        get_expanded_conv(data_path, "expanded_conv_1", 16U, 24U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), true);
-        get_expanded_conv(data_path, "expanded_conv_2", 24U, 24U, PadStrideInfo(1, 1, 1, 1), true, true);
-        get_expanded_conv(data_path, "expanded_conv_3", 24U, 32U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), true);
-        get_expanded_conv(data_path, "expanded_conv_4", 32U, 32U, PadStrideInfo(1, 1, 1, 1), true, true);
-        get_expanded_conv(data_path, "expanded_conv_5", 32U, 32U, PadStrideInfo(1, 1, 1, 1), true, true);
-        get_expanded_conv(data_path, "expanded_conv_6", 32U, 64U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), true);
-        get_expanded_conv(data_path, "expanded_conv_7", 64U, 64U, PadStrideInfo(1, 1, 1, 1), true, true);
-        get_expanded_conv(data_path, "expanded_conv_8", 64U, 64U, PadStrideInfo(1, 1, 1, 1), true, true);
-        get_expanded_conv(data_path, "expanded_conv_9", 64U, 64U, PadStrideInfo(1, 1, 1, 1), true, true);
-        get_expanded_conv(data_path, "expanded_conv_10", 64U, 96U, PadStrideInfo(1, 1, 1, 1), true);
-        get_expanded_conv(data_path, "expanded_conv_11", 96U, 96U, PadStrideInfo(1, 1, 1, 1), true, true);
-        get_expanded_conv(data_path, "expanded_conv_12", 96U, 96U, PadStrideInfo(1, 1, 1, 1), true, true);
-        get_expanded_conv(data_path, "expanded_conv_13", 96U, 160U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), true);
-        get_expanded_conv(data_path, "expanded_conv_14", 160U, 160U, PadStrideInfo(1, 1, 1, 1), true, true);
-        get_expanded_conv(data_path, "expanded_conv_15", 160U, 160U, PadStrideInfo(1, 1, 1, 1), true, true);
-        get_expanded_conv(data_path, "expanded_conv_16", 160U, 320U, PadStrideInfo(1, 1, 1, 1), true);
+        get_expanded_conv_float(data_path, "expanded_conv", 32U, 16U, PadStrideInfo(1, 1, 1, 1));
+        get_expanded_conv_float(data_path, "expanded_conv_1", 16U, 24U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), HasExpand::Yes);
+        get_expanded_conv_float(data_path, "expanded_conv_2", 24U, 24U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes);
+        get_expanded_conv_float(data_path, "expanded_conv_3", 24U, 32U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), HasExpand::Yes);
+        get_expanded_conv_float(data_path, "expanded_conv_4", 32U, 32U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes);
+        get_expanded_conv_float(data_path, "expanded_conv_5", 32U, 32U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes);
+        get_expanded_conv_float(data_path, "expanded_conv_6", 32U, 64U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), HasExpand::Yes);
+        get_expanded_conv_float(data_path, "expanded_conv_7", 64U, 64U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes);
+        get_expanded_conv_float(data_path, "expanded_conv_8", 64U, 64U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes);
+        get_expanded_conv_float(data_path, "expanded_conv_9", 64U, 64U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes);
+        get_expanded_conv_float(data_path, "expanded_conv_10", 64U, 96U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes);
+        get_expanded_conv_float(data_path, "expanded_conv_11", 96U, 96U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes);
+        get_expanded_conv_float(data_path, "expanded_conv_12", 96U, 96U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes);
+        get_expanded_conv_float(data_path, "expanded_conv_13", 96U, 160U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), HasExpand::Yes);
+        get_expanded_conv_float(data_path, "expanded_conv_14", 160U, 160U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes);
+        get_expanded_conv_float(data_path, "expanded_conv_15", 160U, 160U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes);
+        get_expanded_conv_float(data_path, "expanded_conv_16", 160U, 320U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes);
 
         graph << ConvolutionLayer(1U, 1U, 1280U,
                                   get_weights_accessor(data_path, "Conv_1_weights.npy", DataLayout::NCHW),
@@ -141,44 +195,20 @@ public:
                                   get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy", DataLayout::NCHW),
                                   get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_biases.npy"),
                                   PadStrideInfo(1, 1, 0, 0))
-              .set_name("Logits/Conv2d_1c_1x1")
-              << ReshapeLayer(TensorShape(1001U)).set_name("Predictions/Reshape")
-              << SoftmaxLayer().set_name("Predictions/Softmax")
-              << OutputLayer(get_output_accessor(common_params, 5));
-
-        // Finalize graph
-        GraphConfig config;
-        config.num_threads = common_params.threads;
-        config.use_tuner   = common_params.enable_tuner;
-        config.tuner_file  = common_params.tuner_file;
-
-        graph.finalize(common_params.target, config);
-
-        return true;
+              .set_name("Logits/Conv2d_1c_1x1");
     }
 
-    void do_run() override
-    {
-        // Run graph
-        graph.run();
-    }
-
-private:
-    CommandLineParser  cmd_parser;
-    CommonGraphOptions common_opts;
-    CommonGraphParams  common_params;
-    Stream             graph;
-
-    void get_expanded_conv(const std::string &data_path, std::string &&param_path,
-                           unsigned int input_channels, unsigned int output_channels,
-                           PadStrideInfo dwc_pad_stride_info,
-                           bool has_expand = false, bool is_residual = false, unsigned int expansion_size = 6)
+    void get_expanded_conv_float(const std::string &data_path, std::string &&param_path,
+                                 unsigned int input_channels, unsigned int output_channels,
+                                 PadStrideInfo dwc_pad_stride_info,
+                                 HasExpand has_expand = HasExpand::No, IsResidual is_residual = IsResidual::No,
+                                 unsigned int expansion_size = 6)
     {
         std::string total_path = param_path + "_";
         SubStream   left(graph);
 
         // Add expand node
-        if(has_expand)
+        if(has_expand == HasExpand::Yes)
         {
             left << ConvolutionLayer(1U, 1U, input_channels * expansion_size,
                                      get_weights_accessor(data_path, total_path + "expand_weights.npy", DataLayout::NCHW),
@@ -221,7 +251,190 @@ private:
                                         0.0010000000474974513)
              .set_name(param_path + "/project/BatchNorm");
 
-        if(is_residual)
+        if(is_residual == IsResidual::Yes)
+        {
+            // Add residual node
+            SubStream right(graph);
+            graph << EltwiseLayer(std::move(left), std::move(right), EltwiseOperation::Add).set_name(param_path + "/add");
+        }
+        else
+        {
+            graph.forward_tail(left.tail_node());
+        }
+    }
+
+    void create_graph_qasymm8(TensorDescriptor &input_descriptor)
+    {
+        // Create model path
+        const std::string model_path = "/cnn_data/mobilenet_v2_1.0_224_quantized_model";
+
+        // Get trainable parameters data path
+        std::string data_path = common_params.data_path;
+
+        // Add model path to data path
+        if(!data_path.empty())
+        {
+            data_path += model_path;
+        }
+
+        const QuantizationInfo in_quant_info  = QuantizationInfo(0.0078125f, 128);
+        const QuantizationInfo mid_quant_info = QuantizationInfo(0.023528477177023888f, 128);
+
+        const std::vector<QuantizationInfo> conv_weights_quant_info =
+        {
+            QuantizationInfo(0.03396892547607422f, 122),  // Conv
+            QuantizationInfo(0.005167067516595125f, 125), // Conv1
+            QuantizationInfo(0.0016910821432247758f, 113) // Conv2d_1c_1x1
+        };
+
+        // Pointwise expand convolution quantization info
+        const std::vector<QuantizationInfo> pwc_q =
+        {
+            QuantizationInfo(0.254282623529f, 129),        // expand_0 (Dummy)
+            QuantizationInfo(0.009758507832884789f, 127),  // expand_1
+            QuantizationInfo(0.0036556976847350597f, 144), // expand_2
+            QuantizationInfo(0.0029988749884068966f, 104), // expand_3
+            QuantizationInfo(0.0019244228024035692f, 128), // expand_4
+            QuantizationInfo(0.0013649158645421267f, 135), // expand_5
+            QuantizationInfo(0.0019170437008142471f, 127), // expand_6
+            QuantizationInfo(0.0015538912266492844f, 125), // expand_7
+            QuantizationInfo(0.0014702979242429137f, 134), // expand_8
+            QuantizationInfo(0.0013733493397012353f, 127), // expand_9
+            QuantizationInfo(0.0016282502328976989f, 131), // expand_10
+            QuantizationInfo(0.0016309921629726887f, 134), // expand_11
+            QuantizationInfo(0.0018258779309689999f, 138), // expand_12
+            QuantizationInfo(0.0013828007504343987f, 123), // expand_13
+            QuantizationInfo(0.0020222084131091833f, 135), // expand_14
+            QuantizationInfo(0.04281935095787048f, 102),   // expand_15
+            QuantizationInfo(0.002046825597062707f, 135)   // expand_16
+        };
+        // Depthwise expand convolution quantization info
+        const std::vector<QuantizationInfo> dwc_q =
+        {
+            QuantizationInfo(0.3436955213546753f, 165),   // expand_0
+            QuantizationInfo(0.020969120785593987f, 109), // expand_1
+            QuantizationInfo(0.16981913149356842f, 52),   // expand_2
+            QuantizationInfo(0.017202870920300484f, 143), // expand_3
+            QuantizationInfo(0.06525065749883652f, 118),  // expand_4
+            QuantizationInfo(0.07909784466028214f, 95),   // expand_5
+            QuantizationInfo(0.010087885893881321f, 127), // expand_6
+            QuantizationInfo(0.06092711538076401f, 110),  // expand_7
+            QuantizationInfo(0.052407849580049515f, 133), // expand_8
+            QuantizationInfo(0.04077887907624245f, 155),  // expand_9
+            QuantizationInfo(0.031107846647500992f, 143), // expand_10
+            QuantizationInfo(0.07080810517072678f, 66),   // expand_11
+            QuantizationInfo(0.07448793947696686f, 159),  // expand_12
+            QuantizationInfo(0.01525793131440878f, 92),   // expand_13
+            QuantizationInfo(0.04166752099990845f, 147),  // expand_14
+            QuantizationInfo(0.04281935095787048f, 102),  // expand_15
+            QuantizationInfo(0.16456253826618195, 201)    // expand_16
+        };
+        // Project convolution quantization info
+        const std::vector<QuantizationInfo> prwc_q =
+        {
+            QuantizationInfo(0.03737175464630127f, 140),  // expand_0
+            QuantizationInfo(0.0225360207259655f, 156),   // expand_1
+            QuantizationInfo(0.02740888111293316f, 122),  // expand_2
+            QuantizationInfo(0.016844693571329117f, 111), // expand_3
+            QuantizationInfo(0.019062912091612816f, 146), // expand_4
+            QuantizationInfo(0.018293123692274094f, 128), // expand_5
+            QuantizationInfo(0.014601286500692368f, 147), // expand_6
+            QuantizationInfo(0.016782939434051514f, 124), // expand_7
+            QuantizationInfo(0.012898261658847332f, 125), // expand_8
+            QuantizationInfo(0.019561484456062317f, 144), // expand_9
+            QuantizationInfo(0.007436311338096857f, 129), // expand_10
+            QuantizationInfo(0.00838223285973072f, 136),  // expand_11
+            QuantizationInfo(0.023982593789696693f, 154), // expand_12
+            QuantizationInfo(0.009447949007153511f, 140), // expand_13
+            QuantizationInfo(0.00789870135486126f, 139),  // expand_14
+            QuantizationInfo(0.03697410225868225f, 131),  // expand_15
+            QuantizationInfo(0.008009289391338825f, 111)  // expand_16
+        };
+
+        graph << InputLayer(input_descriptor.set_quantization_info(in_quant_info),
+                            get_weights_accessor(data_path, common_params.image))
+              << ConvolutionLayer(
+                  3U, 3U, 32U,
+                  get_weights_accessor(data_path, "Conv_weights.npy"),
+                  get_weights_accessor(data_path, "Conv_bias.npy"),
+                  PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR),
+                  1, conv_weights_quant_info.at(0), mid_quant_info)
+              .set_name("Conv")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name("Conv/Relu6")
+              << DepthwiseConvolutionLayer(3U, 3U,
+                                           get_weights_accessor(data_path, "expanded_conv_depthwise_depthwise_weights.npy"),
+                                           get_weights_accessor(data_path, "expanded_conv_depthwise_depthwise_biases.npy"),
+                                           PadStrideInfo(1, 1, 1, 1), dwc_q.at(0))
+              .set_name("expanded_conv/depthwise/depthwise")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name("expanded_conv/depthwise/Relu6")
+              << ConvolutionLayer(1U, 1U, 16U,
+                                  get_weights_accessor(data_path, "expanded_conv_project_weights.npy"),
+                                  get_weights_accessor(data_path, "expanded_conv_project_biases.npy"),
+                                  PadStrideInfo(1, 1, 0, 0), 1, prwc_q.at(0))
+              .set_name("expanded_conv/project/Conv2D");
+
+        get_expanded_conv_qasymm8(data_path, "expanded_conv_1", IsResidual::No, 96U, 24U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL),
+                                  pwc_q.at(1), dwc_q.at(1), prwc_q.at(1));
+        get_expanded_conv_qasymm8(data_path, "expanded_conv_2", IsResidual::Yes, 144U, 24U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(2), dwc_q.at(2), prwc_q.at(2));
+        get_expanded_conv_qasymm8(data_path, "expanded_conv_3", IsResidual::No, 144U, 32U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL),
+                                  pwc_q.at(3), dwc_q.at(3), prwc_q.at(3));
+        get_expanded_conv_qasymm8(data_path, "expanded_conv_4", IsResidual::Yes, 192U, 32U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(4), dwc_q.at(4), prwc_q.at(4));
+        get_expanded_conv_qasymm8(data_path, "expanded_conv_5", IsResidual::Yes, 192U, 32U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(5), dwc_q.at(5), prwc_q.at(5));
+        get_expanded_conv_qasymm8(data_path, "expanded_conv_6", IsResidual::No, 192U, 64U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL),
+                                  pwc_q.at(6), dwc_q.at(6), prwc_q.at(6));
+        get_expanded_conv_qasymm8(data_path, "expanded_conv_7", IsResidual::Yes, 384U, 64U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(7), dwc_q.at(7), prwc_q.at(7));
+        get_expanded_conv_qasymm8(data_path, "expanded_conv_8", IsResidual::Yes, 384U, 64U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(8), dwc_q.at(8), prwc_q.at(8));
+        get_expanded_conv_qasymm8(data_path, "expanded_conv_9", IsResidual::Yes, 384U, 64U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(9), dwc_q.at(9), prwc_q.at(9));
+        get_expanded_conv_qasymm8(data_path, "expanded_conv_10", IsResidual::No, 384U, 96U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(10), dwc_q.at(10), prwc_q.at(10));
+        get_expanded_conv_qasymm8(data_path, "expanded_conv_11", IsResidual::Yes, 576U, 96U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(11), dwc_q.at(11), prwc_q.at(11));
+        get_expanded_conv_qasymm8(data_path, "expanded_conv_12", IsResidual::Yes, 576U, 96U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(12), dwc_q.at(12), prwc_q.at(12));
+        get_expanded_conv_qasymm8(data_path, "expanded_conv_13", IsResidual::No, 576U, 160U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL),
+                                  pwc_q.at(13), dwc_q.at(13), prwc_q.at(13));
+        get_expanded_conv_qasymm8(data_path, "expanded_conv_14", IsResidual::Yes, 960U, 160U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(14), dwc_q.at(14), prwc_q.at(14));
+        get_expanded_conv_qasymm8(data_path, "expanded_conv_15", IsResidual::Yes, 960U, 160U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(15), dwc_q.at(15), prwc_q.at(15));
+        get_expanded_conv_qasymm8(data_path, "expanded_conv_16", IsResidual::No, 960U, 320U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(16), dwc_q.at(16), prwc_q.at(16));
+
+        graph << ConvolutionLayer(1U, 1U, 1280U,
+                                  get_weights_accessor(data_path, "Conv_1_weights.npy"),
+                                  get_weights_accessor(data_path, "Conv_1_biases.npy"),
+                                  PadStrideInfo(1, 1, 0, 0), 1, conv_weights_quant_info.at(1))
+              .set_name("Conv_1")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name("Conv_1/Relu6")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("Logits/AvgPool")
+              << ConvolutionLayer(1U, 1U, 1001U,
+                                  get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy"),
+                                  get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_biases.npy"),
+                                  PadStrideInfo(1, 1, 0, 0), 1, conv_weights_quant_info.at(2))
+              .set_name("Logits/Conv2d_1c_1x1");
+    }
+
+    void get_expanded_conv_qasymm8(const std::string &data_path, std::string &&param_path, IsResidual is_residual,
+                                   unsigned int input_channels, unsigned int output_channels,
+                                   PadStrideInfo           dwc_pad_stride_info,
+                                   const QuantizationInfo &pwi, const QuantizationInfo &dwi, const QuantizationInfo &pji)
+    {
+        std::string total_path = param_path + "_";
+
+        SubStream left(graph);
+        left << ConvolutionLayer(1U, 1U, input_channels,
+                                 get_weights_accessor(data_path, total_path + "project_weights.npy"),
+                                 get_weights_accessor(data_path, total_path + "project_biases.npy"),
+                                 PadStrideInfo(1, 1, 0, 0), 1, pwi)
+             .set_name(param_path + "/Conv2D")
+             << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name(param_path + "/Conv2D/Relu6")
+             << DepthwiseConvolutionLayer(3U, 3U,
+                                          get_weights_accessor(data_path, total_path + "depthwise_depthwise_weights.npy"),
+                                          get_weights_accessor(data_path, total_path + "depthwise_depthwise_biases.npy"),
+                                          dwc_pad_stride_info, dwi)
+             .set_name(param_path + "/depthwise/depthwise")
+             << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name(param_path + "/depthwise/Relu6")
+             << ConvolutionLayer(1U, 1U, output_channels,
+                                 get_weights_accessor(data_path, total_path + "project_weights.npy"),
+                                 get_weights_accessor(data_path, total_path + "project_biases.npy"),
+                                 PadStrideInfo(1, 1, 0, 0), 1, pji)
+             .set_name(param_path + "/project/Conv2D");
+
+        if(is_residual == IsResidual::Yes)
         {
             // Add residual node
             SubStream right(graph);