1 files changed, 171 insertions, 23 deletions

diff --git a/tests/validation/fixtures/dynamic_fusion/gpu/cl/DirectConv2dFixture.h b/tests/validation/fixtures/dynamic_fusion/gpu/cl/DirectConv2dFixture.h
index 488d449782..e0aecf5ed4 100644
--- a/tests/validation/fixtures/dynamic_fusion/gpu/cl/DirectConv2dFixture.h
+++ b/tests/validation/fixtures/dynamic_fusion/gpu/cl/DirectConv2dFixture.h
@@ -28,8 +28,9 @@
 #include "arm_compute/core/TensorInfo.h"
 #include "arm_compute/core/Types.h"
 
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "arm_compute/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.h"
-#include "arm_compute/dynamic_fusion/sketch/OperatorAttributes.h"
+#include "arm_compute/dynamic_fusion/sketch/attributes/Conv2dAttributes.h"
 #include "arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h"
 #include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuConv2d.h"
 #include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuOutput.h"
@@ -49,6 +50,36 @@ namespace test
 {
 namespace validation
 {
+namespace
+{
+template <typename U>
+void fill(U &&tensor, int i)
+{
+    switch(tensor.data_type())
+    {
+        case DataType::F16:
+        {
+            arm_compute::utils::uniform_real_distribution_16bit<half> distribution{ -1.0f, 1.0f };
+            library->fill(tensor, distribution, i);
+            break;
+        }
+        case DataType::F32:
+        {
+            std::uniform_real_distribution<float> distribution(-1.0f, 1.0f);
+            library->fill(tensor, distribution, i);
+            break;
+        }
+        default:
+            library->fill_tensor_uniform(tensor, i);
+    }
+}
+
+} // namespace
+
+/** General Conv2d fixture
+ *  Adapted from tests/validation/fixtures/ConvolutionLayerFixture.h
+ *  TODO: Parameterize to be fully backend agnostic: COMPMID-5760; remove Gpu from name
+ */
 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
 class DynamicFusionGpuConv2dValidationGenericFixture : public framework::Fixture
 {
@@ -74,28 +105,6 @@ public:
     }
 
 protected:
-    template <typename U>
-    void fill(U &&tensor, int i)
-    {
-        switch(tensor.data_type())
-        {
-            case DataType::F16:
-            {
-                arm_compute::utils::uniform_real_distribution_16bit<half> distribution{ -1.0f, 1.0f };
-                library->fill(tensor, distribution, i);
-                break;
-            }
-            case DataType::F32:
-            {
-                std::uniform_real_distribution<float> distribution(-1.0f, 1.0f);
-                library->fill(tensor, distribution, i);
-                break;
-            }
-            default:
-                library->fill_tensor_uniform(tensor, i);
-        }
-    }
-
     // Given input is in nchw format
     TensorType compute_target(TensorShape input_shape, TensorShape weights_shape, const TensorShape &bias_shape, Conv2dAttributes conv2d_attr)
     {
@@ -201,6 +210,145 @@ public:
                                                                                                          data_type, data_layout, quantization_info, quantization_info);
     }
 };
+
+/** Specific Conv2d method: Direct Conv2d fixture
+ *  Adapted from tests/validation/fixtures/DirectConvolutionLayerFixture.h
+ *  TODO: Parameterize to be fully backend agnostic: COMPMID-5760
+ */
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class DynamicFusionDirectConv2dValidationGenericFixture : public framework::Fixture
+{
+public:
+    using TBias = typename std::conditional < std::is_same<T, uint8_t>::value || std::is_same<T, int8_t>::value, int32_t, T >::type;
+
+    template <typename...>
+    void setup(TensorShape input_shape, int stride_x, int stride_y, int pad_x, int pad_y, unsigned int kernel_size, unsigned int num_kernels,
+               DataType data_type, QuantizationInfo quantization_info, DataLayout data_layout)
+    {
+        ARM_COMPUTE_ERROR_ON(data_layout != DataLayout::NHWC); // Dynamic fusion conv2d only supports NHWC layout
+
+        TensorShape         weights_shape(kernel_size, kernel_size, input_shape.z(), num_kernels);
+        const TensorShape   bias_shape(num_kernels);
+        const PadStrideInfo info(stride_x, stride_y, pad_x, pad_y, DimensionRoundingType::FLOOR);
+        const DataType      bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type;
+
+        const Conv2dAttributes conv2d_attr = convert_pad_stride_info_to_conv_attr(info, { 1U, 1U } /* dilation */);
+
+        TensorInfo input_info   = TensorInfo(input_shape, 1, data_type);
+        TensorInfo weights_info = TensorInfo(weights_shape, 1, data_type);
+
+        const TensorShape output_shape = misc::shape_calculator::compute_deep_convolution_shape(input_info, weights_info, info);
+
+        _target    = compute_target(input_shape, weights_shape, bias_shape, output_shape, conv2d_attr, data_type, bias_data_type, quantization_info, data_layout);
+        _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type, bias_data_type, quantization_info);
+    }
+
+protected:
+    TensorType compute_target(TensorShape input_shape, TensorShape weights_shape, const TensorShape &bias_shape, TensorShape output_shape, const Conv2dAttributes &conv2d_attr,
+                              DataType data_type, DataType bias_data_type, QuantizationInfo quantization_info, const DataLayout &data_layout)
+    {
+        ARM_COMPUTE_ERROR_ON(data_layout != DataLayout::NHWC);
+        ARM_COMPUTE_UNUSED(quantization_info);
+        // Dataset shapes are in NCHW layout
+        permute(input_shape, PermutationVector(2U, 0U, 1U));
+        permute(weights_shape, PermutationVector(2U, 0U, 1U));
+        permute(output_shape, PermutationVector(2U, 0U, 1U));
+
+        auto              cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
+        auto              gpu_ctx        = GpuWorkloadContext{ &cl_compile_ctx };
+        GpuWorkloadSketch sketch{ &gpu_ctx };
+
+        // Create sketch tensors
+        auto input_info  = sketch.create_tensor_info(TensorInfo(input_shape, 1, data_type, data_layout));
+        auto weight_info = sketch.create_tensor_info(TensorInfo(weights_shape, 1, data_type, data_layout));
+        auto bias_info   = sketch.create_tensor_info(TensorInfo(bias_shape, 1, bias_data_type, data_layout));
+        auto dst_info    = sketch.create_tensor_info();
+
+        ITensorInfo *ans_info = FunctionType::create_op(sketch, &input_info, &weight_info, &bias_info, conv2d_attr);
+        GpuOutput::create_op(sketch, ans_info, &dst_info);
+
+        // Configure runtime
+        ClWorkloadRuntime runtime;
+        runtime.configure(sketch);
+
+        for(auto &data : runtime.get_auxiliary_tensors())
+        {
+            auto       tensor      = data.first;
+            const auto aux_mem_req = data.second;
+            tensor->allocator()->init(*data.first->info(), aux_mem_req.alignment);
+            tensor->allocator()->allocate();
+        }
+        // Construct user tensors
+        TensorType t_input{};
+        TensorType t_weight{};
+        TensorType t_bias{};
+        TensorType t_dst{};
+
+        // Initialize user tensors
+        t_input.allocator()->init(input_info);
+        t_weight.allocator()->init(weight_info);
+        t_bias.allocator()->init(bias_info);
+        t_dst.allocator()->init(dst_info);
+
+        ARM_COMPUTE_ASSERT(t_input.info()->is_resizable());
+        ARM_COMPUTE_ASSERT(t_weight.info()->is_resizable());
+        ARM_COMPUTE_ASSERT(t_bias.info()->is_resizable());
+        ARM_COMPUTE_ASSERT(t_dst.info()->is_resizable());
+
+        // Allocate and fill user tensors
+        t_input.allocator()->allocate();
+        t_weight.allocator()->allocate();
+        t_bias.allocator()->allocate();
+        t_dst.allocator()->allocate();
+
+        ARM_COMPUTE_ASSERT(!t_input.info()->is_resizable());
+        ARM_COMPUTE_ASSERT(!t_weight.info()->is_resizable());
+        ARM_COMPUTE_ASSERT(!t_bias.info()->is_resizable());
+        ARM_COMPUTE_ASSERT(!t_dst.info()->is_resizable());
+
+        fill(AccessorType(t_input), 0);
+        fill(AccessorType(t_weight), 1);
+        fill(AccessorType(t_bias), 2);
+
+        // Run runtime
+        runtime.run({ &t_input, &t_weight, &t_bias, &t_dst });
+        return t_dst;
+    }
+
+    SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info,
+                                      DataType data_type, DataType bias_data_type, QuantizationInfo quantization_info)
+    {
+        // Create reference
+        SimpleTensor<T>     src{ input_shape, data_type, 1, quantization_info };
+        SimpleTensor<T>     weights{ weights_shape, data_type, 1, quantization_info };
+        SimpleTensor<TBias> bias{ bias_shape, bias_data_type, 1, quantization_info };
+
+        // Fill reference
+        fill(src, 0);
+        fill(weights, 1);
+        fill(bias, 2);
+
+        SimpleTensor<T> dst = reference::convolution_layer<T>(src, weights, bias, output_shape, info);
+        return dst;
+    }
+    TensorType      _target{};
+    SimpleTensor<T> _reference{};
+};
+
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class DynamicFusionDirectConv2dValidationFixture : public DynamicFusionDirectConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
+{
+public:
+    template <typename...>
+    void setup(TensorShape input_shape, int stride_x, int stride_y, int pad_x, int pad_y, unsigned int kernel_size, unsigned int num_kernels, DataType data_type,
+               DataLayout data_layout)
+    {
+        DynamicFusionDirectConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, stride_x, stride_y, pad_x, pad_y, kernel_size, num_kernels, data_type,
+                                                                                                            QuantizationInfo(),
+                                                                                                            data_layout);
+    }
+};
+
 } // namespace validation
 } // namespace test
 } // namespace arm_compute