COMPMID-556 - Fix examples
- Fixed data type issue in cl_sgemm
- Added support for NEON and OpenCL targets in graph examples. Before we
could run only OpenCL target
- Add auto_init() in NEDepthwiseVectorToTensorKernel
Change-Id: I4410ce6f4992b2375b980634fe55f1083cf3c471
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/112850
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: BSG Visual Compute Jenkins server to access repositories on http://mpd-gerrit.cambridge.arm.com <bsgcomp@arm.com>
diff --git a/src/runtime/CL/functions/CLConvolutionLayer.cpp b/src/runtime/CL/functions/CLConvolutionLayer.cpp
index 66548d1..d628bf9 100644
--- a/src/runtime/CL/functions/CLConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLConvolutionLayer.cpp
@@ -222,7 +222,10 @@
shape_im2col.set(0, mat_input_cols);
shape_im2col.set(1, mat_input_rows);
shape_im2col.set(2, 1);
- _input_im2col_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col));
+ // FIXME: input->clone() doesn't work with subtensors for grouped convolutions.
+ TensorInfo im2col_reshaped_info(shape_im2col, 1, dt, input->info()->fixed_point_position());
+ im2col_reshaped_info.set_quantization_info(input->info()->quantization_info());
+ _input_im2col_reshaped.allocator()->init(im2col_reshaped_info);
_memory_group.manage(&_input_im2col_reshaped);
// Create tensor (interleave) to prepare input tensor for GEMM
@@ -231,7 +234,10 @@
TensorShape shape_interleaved = shape_im2col;
shape_interleaved.set(0, shape_interleaved.x() * 4);
shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f));
- _input_interleaved_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_interleaved));
+ // FIXME: input->clone() doesn't work with subtensors for grouped convolutions.
+ TensorInfo interleaved_info(shape_interleaved, 1, dt, input->info()->fixed_point_position());
+ interleaved_info.set_quantization_info(input->info()->quantization_info());
+ _input_interleaved_reshaped.allocator()->init(interleaved_info);
_memory_group.manage(&_input_interleaved_reshaped);
}
@@ -241,8 +247,9 @@
shape_gemm.set(1, mat_input_rows);
const DataType gemm_data_type = _is_quantized ? DataType::S32 : dt;
// GEMM output should be S32 for acquiring raw integer accumulator without quantized postprocessing for quantized asymmetric input.
- TensorInfo info_gemm(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_gemm).set_data_type(gemm_data_type).set_quantization_info(
- output->info()->quantization_info()));
+ // FIXME: input->clone() doesn't work with subtensors for grouped convolutions.
+ TensorInfo info_gemm(shape_gemm, 1, gemm_data_type, input->info()->fixed_point_position());
+ info_gemm.set_quantization_info(output->info()->quantization_info());
_gemm_output.allocator()->init(info_gemm);
_memory_group.manage(&_gemm_output);