1 files changed, 64 insertions, 0 deletions
diff --git a/src/runtime/NEON/functions/NEDepthwiseConvolution.cpp b/src/runtime/NEON/functions/NEDepthwiseConvolution.cpp
index fd8d419fa1..e12bc07464 100644
--- a/src/runtime/NEON/functions/NEDepthwiseConvolution.cpp
+++ b/src/runtime/NEON/functions/NEDepthwiseConvolution.cpp
@@ -59,4 +59,68 @@ void NEDepthwiseConvolution3x3::run()
     {
         NEScheduler::get().schedule(&_bias_kernel, Window::DimX);
     }
+}
+
+NEDepthwiseConvolution::NEDepthwiseConvolution()
+    : _im2col_kernel(), _weights_reshape_kernel(), _v2mm_kernel(), _vector_to_tensor_kernel(), _input_reshaped(), _weights_reshaped(), _v2mm_output()
+{
+}
+
+void NEDepthwiseConvolution::configure(ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
+    ARM_COMPUTE_ERROR_ON(input->info()->dimension(2) != weights->info()->dimension(2));
+
+    const size_t weights_w = weights->info()->dimension(0);
+    const size_t weights_h = weights->info()->dimension(1);
+    const size_t weights_z = weights->info()->dimension(2);
+
+    bool has_bias = (biases != nullptr);
+
+    unsigned int conv_w = 0;
+    unsigned int conv_h = 0;
+    std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), weights_w, weights_h, conv_info);
+
+    // Set up intermediate tensors
+    const size_t patch_size = weights_w * weights_h + ((has_bias) ? 1 : 0);
+    const size_t conv_size  = conv_w * conv_h;
+
+    // Im2Col configuration
+    TensorShape shape_im2col = input->info()->tensor_shape();
+    shape_im2col.set(0, patch_size);
+    shape_im2col.set(1, conv_size);
+    shape_im2col.set(2, weights_z);
+    const TensorInfo info_im2col(shape_im2col, 1, input->info()->data_type(), input->info()->fixed_point_position());
+    _input_reshaped.allocator()->init(info_im2col);
+    _im2col_kernel.configure(input, &_input_reshaped, Size2D(weights_w, weights_h), conv_info, has_bias);
+
+    // Weights reshape configuration
+    const TensorShape shape_weights_reshape(patch_size, weights_z);
+    const TensorInfo  info_weights_reshape(shape_weights_reshape, 1, weights->info()->data_type(), weights->info()->fixed_point_position());
+    _weights_reshaped.allocator()->init(info_weights_reshape);
+    _weights_reshape_kernel.configure(weights, &_weights_reshaped, biases);
+
+    // GEMV configuration
+    TensorShape shape_v2mm_out = input->info()->tensor_shape();
+    shape_v2mm_out.set(0, conv_size * weights_z);
+    shape_v2mm_out.set(1, 1);
+    shape_v2mm_out.set(2, 1);
+    const TensorInfo info_v2mm_out(shape_v2mm_out, 1, input->info()->data_type(), input->info()->fixed_point_position());
+    _v2mm_output.allocator()->init(info_v2mm_out);
+    _v2mm_kernel.configure(&_input_reshaped, &_weights_reshaped, &_v2mm_output);
+    _vector_to_tensor_kernel.configure(&_v2mm_output, output, conv_w, conv_h);
+
+    // Allocate intermediate tensors
+    _input_reshaped.allocator()->allocate();
+    _weights_reshaped.allocator()->allocate();
+    _v2mm_output.allocator()->allocate();
+}
+
+void NEDepthwiseConvolution::run()
+{
+    NEScheduler::get().schedule(&_im2col_kernel, Window::DimX);
+    NEScheduler::get().schedule(&_weights_reshape_kernel, Window::DimX);
+    NEScheduler::get().schedule(&_v2mm_kernel, Window::DimX);
+    NEScheduler::get().schedule(&_vector_to_tensor_kernel, Window::DimX);
 }
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