COMPMID-1047 Extract Flatten function from Im2Col for NEON

Change-Id: I80f3aaadc8cae8c9ca1a5a239e79bda302b89bd8
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/144813
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>

1 files changed, 16 insertions, 16 deletions

diff --git a/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp b/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp
index f1606aa93e..60f6294394 100644
--- a/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp
+++ b/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp
@@ -74,8 +74,8 @@ Status NEFullyConnectedLayerReshapeWeights::validate(const ITensorInfo *input, c
 }
 
 NEFullyConnectedLayer::NEFullyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager)
-    : _memory_group(std::move(memory_manager)), _im2col_kernel(), _convert_weights(), _reshape_weights_function(), _mm_gemm(), _mm_gemmlowp(), _gemmlowp_output_stage(), _accumulate_biases_kernel(),
-      _im2col_output(), _gemmlowp_output(), _converted_weights_output(), _reshape_weights_output(), _original_weights(nullptr), _are_weights_converted(true), _are_weights_reshaped(false),
+    : _memory_group(std::move(memory_manager)), _flatten_kernel(), _convert_weights(), _reshape_weights_function(), _mm_gemm(), _mm_gemmlowp(), _gemmlowp_output_stage(), _accumulate_biases_kernel(),
+      _flatten_output(), _gemmlowp_output(), _converted_weights_output(), _reshape_weights_output(), _original_weights(nullptr), _are_weights_converted(true), _are_weights_reshaped(false),
       _is_fc_after_conv(false), _accumulate_biases(false), _is_quantized(false), _is_prepared(false)
 {
 }
@@ -112,19 +112,19 @@ void NEFullyConnectedLayer::configure_conv_fc(const ITensor *input, const ITenso
 
     // If the fully connected layer is called after a convolution layer, the input tensor must be linearized
 
-    // Initialize output tensor for im2col
-    TensorShape shape_im2col = compute_flatten_shape(input->info());
-    _im2col_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col));
+    // Initialize output tensor for flatten
+    TensorShape shape_flatten = compute_flatten_shape(input->info());
+    _flatten_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_flatten));
 
-    // Configure im2col kernel
-    _memory_group.manage(&_im2col_output);
-    _im2col_kernel.configure(input, &_im2col_output, Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false, Size2D(1U, 1U), 1, true);
+    // Configure flatten kernel
+    _memory_group.manage(&_flatten_output);
+    _flatten_kernel.configure(input, &_flatten_output);
 
     // Configure matrix multiply kernel
-    configure_mm(&_im2col_output, weights, output);
+    configure_mm(&_flatten_output, weights, output);
 
-    // Allocate the output tensor for im2col once all the configure methods have been called
-    _im2col_output.allocator()->allocate();
+    // Allocate the output tensor for flatten once all the configure methods have been called
+    _flatten_output.allocator()->allocate();
 }
 
 void NEFullyConnectedLayer::configure_fc_fc(const ITensor *input, const ITensor *weights, ITensor *output)
@@ -249,7 +249,7 @@ Status NEFullyConnectedLayer::validate(const ITensorInfo *input, const ITensorIn
     bool is_fc_after_conv = true;
     bool is_quantized     = is_data_type_quantized_asymmetric(input->data_type());
 
-    const ITensorInfo &im2col_input      = TensorInfo(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_flatten_shape(input)));
+    const ITensorInfo &flatten_input     = TensorInfo(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_flatten_shape(input)));
     const ITensorInfo &reshaped_weights  = TensorInfo(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_transposed_shape(*weights)));
     const ITensorInfo &converted_weights = weights_reshaped ? TensorInfo(weights->clone()->set_is_resizable(true).reset_padding()) : TensorInfo(*reshaped_weights.clone());
     const ITensorInfo &gemmlowp_output   = TensorInfo(output->clone()->set_is_resizable(true).reset_padding().set_data_type(DataType::S32));
@@ -307,9 +307,9 @@ Status NEFullyConnectedLayer::validate(const ITensorInfo *input, const ITensorIn
         // Fully Connected layer after a Convolution Layer without batches
         ARM_COMPUTE_RETURN_ERROR_ON((weights_to_use->dimension(1) != (input->dimension(0) * input->dimension(1) * input->dimension(2))));
 
-        // Validate im2col kernel
-        ARM_COMPUTE_RETURN_ON_ERROR(NEIm2ColKernel::validate(input, &im2col_input, Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false, Size2D(1U, 1U), 1, true));
-        input_to_use = &im2col_input;
+        // Validate flatten kernel
+        ARM_COMPUTE_RETURN_ON_ERROR(NEFlattenLayerKernel::validate(input, &flatten_input));
+        input_to_use = &flatten_input;
     }
     else
     {
@@ -337,7 +337,7 @@ void NEFullyConnectedLayer::run()
     // Linearize input if it comes from a convolutional layer
     if(_is_fc_after_conv)
     {
-        NEScheduler::get().schedule(&_im2col_kernel, Window::DimY);
+        NEScheduler::get().schedule(&_flatten_kernel, Window::DimY);
     }
 
     // Run matrix multiply