Port NEFullyConnectedLayer to memory injecting interface
Resolves: COMPMID-4501
Change-Id: Ib61b3d06974009e501b3fb86467735427e13a94a
Signed-off-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5931
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
diff --git a/src/runtime/cpu/operators/CpuWinogradConv2d.cpp b/src/runtime/cpu/operators/CpuWinogradConv2d.cpp
index bf105d5..a734e17 100644
--- a/src/runtime/cpu/operators/CpuWinogradConv2d.cpp
+++ b/src/runtime/cpu/operators/CpuWinogradConv2d.cpp
@@ -71,164 +71,164 @@
}
}
-inline Status validate_kernel_3x3(const Size2D input_dims, const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
- const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
+inline Status validate_kernel_3x3(const Size2D input_dims, const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
+ const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
{
- ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F16, DataType::F32);
- if(input->data_type() == DataType::F32)
+ if(src->data_type() == DataType::F32)
{
if(input_dims.width > 4 && input_dims.height > 4)
{
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 4, 4, 3, 3>::validate(input, input0, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 4, 4, 3, 3>::validate(src, input0, winograd_info)));
ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 4, 4, 3, 3>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 4, 4, 3, 3>::validate(batched_mm_output, biases, output, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 4, 4, 3, 3>::validate(batched_mm_output, biases, dst, winograd_info)));
}
else
{
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 2, 2, 3, 3>::validate(input, input0, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 2, 2, 3, 3>::validate(src, input0, winograd_info)));
ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 2, 2, 3, 3>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 2, 2, 3, 3>::validate(batched_mm_output, biases, output, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 2, 2, 3, 3>::validate(batched_mm_output, biases, dst, winograd_info)));
}
}
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
- else if(input->data_type() == DataType::F16)
+ else if(src->data_type() == DataType::F16)
{
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<__fp16, 4, 4, 3, 3>::validate(input, input0, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<__fp16, 4, 4, 3, 3>::validate(src, input0, winograd_info)));
ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<__fp16, 4, 4, 3, 3>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<__fp16, 4, 4, 3, 3>::validate(batched_mm_output, biases, output, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<__fp16, 4, 4, 3, 3>::validate(batched_mm_output, biases, dst, winograd_info)));
}
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
if(act_info.enabled())
{
- CpuActivation::validate(output, nullptr, act_info);
+ CpuActivation::validate(dst, nullptr, act_info);
}
return Status{};
}
-inline Status validate_kernel_5x5(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
- const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
+inline Status validate_kernel_5x5(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
+ const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
{
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 2, 2, 5, 5>::validate(input, input0, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 2, 2, 5, 5>::validate(src, input0, winograd_info)));
ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 2, 2, 5, 5>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 2, 2, 5, 5>::validate(batched_mm_output, biases, output, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 2, 2, 5, 5>::validate(batched_mm_output, biases, dst, winograd_info)));
if(act_info.enabled())
{
- CpuActivation::validate(output, nullptr, act_info);
+ CpuActivation::validate(dst, nullptr, act_info);
}
return Status{};
}
-inline Status validate_kernel_3x1(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
- const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
+inline Status validate_kernel_3x1(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
+ const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 1, 6, 1, 3>::validate(input, input0, winograd_info)));
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32);
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 1, 6, 1, 3>::validate(src, input0, winograd_info)));
ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 1, 6, 1, 3>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 1, 6, 1, 3>::validate(batched_mm_output, biases, output, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 1, 6, 1, 3>::validate(batched_mm_output, biases, dst, winograd_info)));
if(act_info.enabled())
{
- CpuActivation::validate(output, nullptr, act_info);
+ CpuActivation::validate(dst, nullptr, act_info);
}
return Status{};
}
-inline Status validate_kernel_1x3(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
- const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
+inline Status validate_kernel_1x3(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
+ const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 6, 1, 3, 1>::validate(input, input0, winograd_info)));
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32);
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 6, 1, 3, 1>::validate(src, input0, winograd_info)));
ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 6, 1, 3, 1>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 6, 1, 3, 1>::validate(batched_mm_output, biases, output, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 6, 1, 3, 1>::validate(batched_mm_output, biases, dst, winograd_info)));
if(act_info.enabled())
{
- CpuActivation::validate(output, nullptr, act_info);
+ CpuActivation::validate(dst, nullptr, act_info);
}
return Status{};
}
-inline Status validate_kernel_5x1(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
- const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
+inline Status validate_kernel_5x1(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
+ const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 1, 4, 1, 5>::validate(input, input0, winograd_info)));
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32);
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 1, 4, 1, 5>::validate(src, input0, winograd_info)));
ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 1, 4, 1, 5>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 1, 4, 1, 5>::validate(batched_mm_output, biases, output, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 1, 4, 1, 5>::validate(batched_mm_output, biases, dst, winograd_info)));
if(act_info.enabled())
{
- CpuActivation::validate(output, nullptr, act_info);
+ CpuActivation::validate(dst, nullptr, act_info);
}
return Status{};
}
-inline Status validate_kernel_1x5(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
- const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
+inline Status validate_kernel_1x5(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
+ const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 4, 1, 5, 1>::validate(input, input0, winograd_info)));
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32);
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 4, 1, 5, 1>::validate(src, input0, winograd_info)));
ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 4, 1, 5, 1>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 4, 1, 5, 1>::validate(batched_mm_output, biases, output, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 4, 1, 5, 1>::validate(batched_mm_output, biases, dst, winograd_info)));
if(act_info.enabled())
{
- CpuActivation::validate(output, nullptr, act_info);
+ CpuActivation::validate(dst, nullptr, act_info);
}
return Status{};
}
-inline Status validate_kernel_7x1(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
- const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
+inline Status validate_kernel_7x1(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
+ const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 1, 2, 1, 7>::validate(input, input0, winograd_info)));
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32);
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 1, 2, 1, 7>::validate(src, input0, winograd_info)));
ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 1, 2, 1, 7>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 1, 2, 1, 7>::validate(batched_mm_output, biases, output, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 1, 2, 1, 7>::validate(batched_mm_output, biases, dst, winograd_info)));
if(act_info.enabled())
{
- CpuActivation::validate(output, nullptr, act_info);
+ CpuActivation::validate(dst, nullptr, act_info);
}
return Status{};
}
-inline Status validate_kernel_1x7(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
- const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
+inline Status validate_kernel_1x7(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
+ const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 2, 1, 7, 1>::validate(input, input0, winograd_info)));
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32);
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 2, 1, 7, 1>::validate(src, input0, winograd_info)));
ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 2, 1, 7, 1>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 2, 1, 7, 1>::validate(batched_mm_output, biases, output, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 2, 1, 7, 1>::validate(batched_mm_output, biases, dst, winograd_info)));
if(act_info.enabled())
{
- CpuActivation::validate(output, nullptr, act_info);
+ CpuActivation::validate(dst, nullptr, act_info);
}
return Status{};
}
-inline Tensor4DShape internal_get_input_shape(const ITensorInfo *input)
+inline Tensor4DShape internal_get_input_shape(const ITensorInfo *src)
{
- const DataLayout data_layout = input->data_layout();
- const int in_width = input->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH));
- const int in_height = input->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT));
- const int in_channels = input->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL));
- const int in_batches = input->dimension(3);
+ const DataLayout data_layout = src->data_layout();
+ const int in_width = src->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH));
+ const int in_height = src->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT));
+ const int in_channels = src->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL));
+ const int in_batches = src->dimension(3);
return Tensor4DShape{ in_batches, in_height, in_width, in_channels };
}
-Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info)
+Status validate_arguments(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const PadStrideInfo &conv_info)
{
- ARM_COMPUTE_UNUSED(output);
- ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input);
+ ARM_COMPUTE_UNUSED(dst);
+ ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(src);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.stride().first != 1 || conv_info.stride().second != 1, "Winograd layer only supports unit strides.");
if(biases != nullptr)
{
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, biases);
ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
}
- return ICpuWinogradConv2dTransformWeightsKernel::validate(input, weights);
+ return ICpuWinogradConv2dTransformWeightsKernel::validate(src, weights);
}
Size2D winograd_output_tile(const Size2D &input_dims, const Size2D &kernel_dims, DataType data_type)
{
@@ -647,20 +647,20 @@
_aux_mem[OutputWorkspace] = MemoryInfo(offset_int_vec(OutputWorkspace), MemoryLifetime::Persistent, output_workspace_size);
}
-Status CpuWinogradConv2d::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output,
+Status CpuWinogradConv2d::validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst,
const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info, bool enable_fast_math)
{
- ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
- ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info));
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, weights, dst);
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, weights, biases, dst, conv_info));
// Get indices for the width and height
- const size_t idx_width = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH);
- const size_t idx_height = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);
+ const size_t idx_width = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::WIDTH);
+ const size_t idx_height = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::HEIGHT);
// Input shape, kernel size and output tile
- const Size2D input_dims = Size2D(input->dimension(idx_width), input->dimension(idx_height));
+ const Size2D input_dims = Size2D(src->dimension(idx_width), src->dimension(idx_height));
const Size2D kernel_size = Size2D(weights->dimension(idx_width), weights->dimension(idx_height));
- const DataType data_type = input->data_type();
+ const DataType data_type = src->data_type();
const Size2D output_tile = winograd_output_tile(input_dims, kernel_size, data_type);
// Check if the Winograd configuration requires fast math
@@ -674,11 +674,11 @@
kernel_size,
input_dims,
conv_info,
- input->data_layout());
+ src->data_layout());
// Validate input transform
- const TensorShape input0_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, winograd_info);
- const TensorInfo input0 = input->clone()->set_tensor_shape(input0_shape);
+ const TensorShape input0_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*src, winograd_info);
+ const TensorInfo input0 = src->clone()->set_tensor_shape(input0_shape);
// Validate filter transform
const TensorShape input1_shape = misc::shape_calculator::compute_winograd_filter_transform_shape(*weights, winograd_info);
const TensorInfo input1 = weights->clone()->set_tensor_shape(input1_shape);
@@ -696,7 +696,7 @@
ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != conv_info.pad_left(), "Only SAME or VALID padding supported");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != conv_info.pad_bottom(), "Only SAME or VALID padding supported");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != conv_info.pad_left(), "Only SAME or VALID padding supported");
- return validate_kernel_3x3(input_dims, input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
+ return validate_kernel_3x3(input_dims, src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
}
else if(kernel_size == Size2D(5, 5))
{
@@ -707,49 +707,49 @@
ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != conv_info.pad_left(), "Only SAME or VALID padding supported");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != conv_info.pad_bottom(), "Only SAME or VALID padding supported");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != conv_info.pad_left(), "Only SAME or VALID padding supported");
- return validate_kernel_5x5(input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
+ return validate_kernel_5x5(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
}
if(kernel_size == Size2D(3, 1))
{
ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 1, "Only SAME or VALID padding supported");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 1, "Only SAME or VALID padding supported");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_bottom() != 0, "Only SAME or VALID padding supported");
- return validate_kernel_3x1(input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
+ return validate_kernel_3x1(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
}
else if(kernel_size == Size2D(1, 3))
{
ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 1, "Only SAME or VALID padding supported");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 1, "Only SAME or VALID padding supported");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_right() != 0, "Only SAME or VALID padding supported");
- return validate_kernel_1x3(input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
+ return validate_kernel_1x3(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
}
else if(kernel_size == Size2D(5, 1))
{
ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 2, "Only SAME or VALID padding supported");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 2, "Only SAME or VALID padding supported");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_bottom() != 0, "Only SAME or VALID padding supported");
- return validate_kernel_5x1(input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
+ return validate_kernel_5x1(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
}
else if(kernel_size == Size2D(1, 5))
{
ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 2, "Only SAME or VALID padding supported");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 2, "Only SAME or VALID padding supported");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_right() != 0, "Only SAME or VALID padding supported");
- return validate_kernel_1x5(input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
+ return validate_kernel_1x5(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
}
else if(kernel_size == Size2D(7, 1))
{
ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 3, "Only SAME or VALID padding supported");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 3, "Only SAME or VALID padding supported");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_bottom() != 0, "Only SAME or VALID padding supported");
- return validate_kernel_7x1(input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
+ return validate_kernel_7x1(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
}
else if(kernel_size == Size2D(1, 7))
{
ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 3, "Only SAME or VALID padding supported");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 3, "Only SAME or VALID padding supported");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_right() != 0, "Only SAME or VALID padding supported");
- return validate_kernel_1x7(input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
+ return validate_kernel_1x7(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
}
else
{