COMPMID-1051 - Fix validate method in NEGEMMConvolutionLayer
Change-Id: I10e8e1267a09246cac77e677f1c087bb1d80a61b
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/127517
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
index c339947..7f25c2e 100644
--- a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
@@ -466,55 +466,6 @@
optimised_kernel = true;
}
- // Reshape weights if needed
- if(optimised_kernel)
- {
- if(are_weights_reshaped)
- {
- mat_weights_cols = weights_info.num_kernels();
- mat_weights_rows = weights->dimension(1);
- }
- else
- {
- TensorShape reshaped_weights_shape{ mat_weights_cols, mat_weights_rows };
-
- // Create tensor to store the reshaped weights
- reshaped_weights->set_tensor_shape(get_reshaped_weights_shape_conv(weights, append_bias, is_fully_connected_convolution));
- ARM_COMPUTE_RETURN_ON_ERROR(NEConvolutionLayerReshapeWeights::validate(weights, biases, reshaped_weights.get(), !is_fully_connected_convolution /* 1xW transpose */));
- weights = reshaped_weights.get();
- }
- }
- else
- {
- if(are_weights_reshaped)
- {
- const unsigned int transpose_width = 16 / input->element_size();
- mat_weights_cols = weights_info.num_kernels();
- mat_weights_rows = weights->dimension(0) / transpose_width + (append_bias ? 1 : 0);
- }
- else
- {
- TensorShape reshaped_weights_shape;
-
- if(is_fully_connected_convolution || is_quantized)
- {
- reshaped_weights_shape = TensorShape{ mat_weights_cols, mat_weights_rows };
- }
- else
- {
- // Create tensor to store transposed weights
- const float transpose_width = 16.0f / input->element_size();
- reshaped_weights_shape = TensorShape{ mat_weights_rows *static_cast<unsigned int>(transpose_width),
- static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width)) };
- }
-
- // Create tensor to store the reshaped weights
- reshaped_weights->set_tensor_shape(get_reshaped_weights_shape_conv(weights, append_bias, is_fully_connected_convolution));
- ARM_COMPUTE_RETURN_ON_ERROR(NEConvolutionLayerReshapeWeights::validate(weights, biases, reshaped_weights.get(), !is_fully_connected_convolution /* 1xW transpose */));
- weights = reshaped_weights.get();
- }
- }
-
// Validate im2col
const unsigned int mat_input_cols = mat_weights_rows;
const unsigned int mat_input_rows = conv_w * conv_h;
@@ -531,19 +482,52 @@
shape_gemm.set(1, mat_input_rows);
TensorInfo gemm_output_info = input->clone()->set_tensor_shape(shape_gemm);
- // Validate GEMM interleave and multiply
- if(is_interleaved)
+ // Reshape weights if needed
+ if(optimised_kernel)
{
- TensorShape shape_interleaved = shape_im2col;
- shape_interleaved.set(0, shape_interleaved.x() * 4);
- shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f));
- TensorInfo input_interleaved_info = input->clone()->set_tensor_shape(shape_interleaved);
- ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMInterleave4x4Kernel::validate(&im2_col_info, &input_interleaved_info));
- ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(&input_interleaved_info, weights, &gemm_output_info, 1.f, is_interleaved, GEMMReshapeInfo()));
+ ARM_COMPUTE_RETURN_ERROR_ON(are_weights_reshaped);
+
+ // Create tensor to store the reshaped weights
+ reshaped_weights->set_tensor_shape(get_reshaped_weights_shape_conv(weights, append_bias, is_fully_connected_convolution));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEConvolutionLayerReshapeWeights::validate(weights, biases, reshaped_weights.get(), !is_fully_connected_convolution /* 1xW transpose */));
}
else
{
- ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(&im2_col_info, weights, &gemm_output_info, 1.f, is_interleaved, GEMMReshapeInfo()));
+ TensorShape reshaped_weights_shape;
+
+ if(is_fully_connected_convolution || is_quantized)
+ {
+ reshaped_weights_shape = TensorShape{ mat_weights_cols, mat_weights_rows };
+ }
+ else
+ {
+ // Create tensor to store transposed weights
+ const float transpose_width = 16.0f / input->element_size();
+ reshaped_weights_shape = TensorShape{ mat_weights_rows *static_cast<unsigned int>(transpose_width),
+ static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width)) };
+ }
+
+ // Create tensor to store the reshaped weights
+ reshaped_weights->set_tensor_shape(get_reshaped_weights_shape_conv(weights, append_bias, is_fully_connected_convolution));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEConvolutionLayerReshapeWeights::validate(weights, biases, reshaped_weights.get(), !is_fully_connected_convolution /* 1xW transpose */));
+ weights = reshaped_weights.get();
+
+ // Validate GEMM interleave and multiply
+ if(is_interleaved)
+ {
+ TensorShape shape_interleaved = shape_im2col;
+ shape_interleaved.set(0, shape_interleaved.x() * 4);
+ shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f));
+ TensorInfo input_interleaved_info = input->clone()->set_tensor_shape(shape_interleaved);
+ ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMInterleave4x4Kernel::validate(&im2_col_info, &input_interleaved_info));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(&input_interleaved_info, weights, &gemm_output_info, 1.f, is_interleaved, GEMMReshapeInfo(shape_im2col[1], // m
+ weights->tensor_shape()[0], // n
+ shape_im2col[0]) /* k */));
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(&im2_col_info, weights, &gemm_output_info, 1.f, is_interleaved, GEMMReshapeInfo()));
+ }
}
ARM_COMPUTE_RETURN_ON_ERROR(NECol2ImKernel::validate(&gemm_output_info, output, Size2D(conv_w, conv_h)));