COMPMID-935 - Implementing Convolution with Winograd on OpenCL (part 2)
Implemented Winograd Filter Transform 3x3 on OpenCL
Change-Id: I8f2b2dd938c5c000ef7ce392a37fb7b8b4202a4e
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/122708
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>
diff --git a/tests/validation/reference/Winograd.cpp b/tests/validation/reference/Winograd.cpp
index 371bb63..3ed55fb 100644
--- a/tests/validation/reference/Winograd.cpp
+++ b/tests/validation/reference/Winograd.cpp
@@ -26,6 +26,8 @@
#include "tests/validation/Helpers.h"
#include "tests/validation/reference/Utils.h"
+#include "arm_compute/core/Types.h"
+
namespace arm_compute
{
namespace test
@@ -108,6 +110,87 @@
}
}
}
+
+template <typename T>
+void winograd_filter_transform3x3(const SimpleTensor<T> &in, SimpleTensor<T> &out)
+{
+ // Simple tensor for the 3x3 input tile
+ SimpleTensor<T> input_tile{ TensorShape(3u, 3u), in.data_type(), 1 };
+
+ // Simple tensor for the transformation matrix
+ SimpleTensor<T> trans_matrix{ TensorShape(3u, 4u), in.data_type(), 1 };
+
+ // Simple tensor for the transformation matrix transpose
+ SimpleTensor<T> trans_matrix_transposed{ TensorShape(4u, 3u), in.data_type(), 1 };
+
+ // Simple tensor for the 4x3 temporary tile
+ SimpleTensor<T> tmp_tile{ TensorShape(3u, 4u), in.data_type(), 1 };
+
+ // Simple tensor for the 4x4 output tile
+ SimpleTensor<T> output_tile{ TensorShape(4u, 4u), in.data_type(), 1 };
+
+ // Initialize transformation matrix
+ // 1 | 0 | 0
+ // 0.5 | 0.5 | 0.5
+ // 0.5 |-0.5 | 0.5
+ // 0 | 0 | 1
+ trans_matrix[0 + 0 * 3] = 1.0f;
+ trans_matrix[1 + 0 * 3] = 0.0f;
+ trans_matrix[2 + 0 * 3] = 0.0f;
+ trans_matrix[0 + 1 * 3] = 0.5f;
+ trans_matrix[1 + 1 * 3] = 0.5f;
+ trans_matrix[2 + 1 * 3] = 0.5f;
+ trans_matrix[0 + 2 * 3] = 0.5f;
+ trans_matrix[1 + 2 * 3] = -0.5f;
+ trans_matrix[2 + 2 * 3] = 0.5f;
+ trans_matrix[0 + 3 * 3] = 0.0f;
+ trans_matrix[1 + 3 * 3] = 0.0f;
+ trans_matrix[2 + 3 * 3] = 1.0f;
+
+ // Transpose the transformation matrix
+ transpose_matrix(trans_matrix, trans_matrix_transposed);
+
+ const int num_channels = in.shape()[2];
+ const int num_filters = in.shape()[3];
+ const int num_batches = in.shape().total_size() / (9 * num_channels * num_filters);
+
+ for(int n = 0; n < num_batches; ++n)
+ {
+ for(int w = 0; w < num_filters; ++w)
+ {
+ for(int z = 0; z < num_channels; ++z)
+ {
+ // Load the 3x3 tile from the input tensor
+ get_tile(in, input_tile, Coordinates(0, 0, z, w, n));
+
+ // First transformation
+ matrix_multiply(trans_matrix, input_tile, tmp_tile);
+
+ // Second transformation
+ matrix_multiply(tmp_tile, trans_matrix_transposed, output_tile);
+
+ // Store the 4x4 output tile across the 16 channels
+ const int output_offset = w + z * num_filters;
+ out[output_offset + 0 * num_filters * num_channels] = output_tile[0 + 0 * 4];
+ out[output_offset + 1 * num_filters * num_channels] = output_tile[1 + 0 * 4];
+ out[output_offset + 2 * num_filters * num_channels] = output_tile[2 + 0 * 4];
+ out[output_offset + 3 * num_filters * num_channels] = output_tile[3 + 0 * 4];
+ out[output_offset + 4 * num_filters * num_channels] = output_tile[0 + 1 * 4];
+ out[output_offset + 5 * num_filters * num_channels] = output_tile[1 + 1 * 4];
+ out[output_offset + 6 * num_filters * num_channels] = output_tile[2 + 1 * 4];
+ out[output_offset + 7 * num_filters * num_channels] = output_tile[3 + 1 * 4];
+ out[output_offset + 8 * num_filters * num_channels] = output_tile[0 + 2 * 4];
+ out[output_offset + 9 * num_filters * num_channels] = output_tile[1 + 2 * 4];
+ out[output_offset + 10 * num_filters * num_channels] = output_tile[2 + 2 * 4];
+ out[output_offset + 11 * num_filters * num_channels] = output_tile[3 + 2 * 4];
+ out[output_offset + 12 * num_filters * num_channels] = output_tile[0 + 3 * 4];
+ out[output_offset + 13 * num_filters * num_channels] = output_tile[1 + 3 * 4];
+ out[output_offset + 14 * num_filters * num_channels] = output_tile[2 + 3 * 4];
+ out[output_offset + 15 * num_filters * num_channels] = output_tile[3 + 3 * 4];
+ }
+ }
+ }
+}
} // namespace
template <typename T>
@@ -130,7 +213,29 @@
return dst;
}
+template <typename T>
+SimpleTensor<T> winograd_filter_transform(const SimpleTensor<T> &in, const TensorShape &output_shape)
+{
+ ARM_COMPUTE_ERROR_ON_MSG(in.data_layout() != DataLayout::NCHW, "Only supported NCHW data format");
+
+ // Create reference
+ SimpleTensor<T> out{ output_shape, in.data_type(), 1 };
+
+ switch(in.shape()[0])
+ {
+ case 3:
+ winograd_filter_transform3x3(in, out);
+ break;
+ default:
+ ARM_COMPUTE_ERROR("Only supported 3x3 kernel");
+ break;
+ }
+
+ return out;
+}
+
template SimpleTensor<float> winograd_input_transform(const SimpleTensor<float> &src, const TensorShape &dst_shape, const PadStrideInfo &conv_info, const Size2D &kernel_dims);
+template SimpleTensor<float> winograd_filter_transform(const SimpleTensor<float> &in, const TensorShape &output_shape);
} // namespace reference
} // namespace validation
} // namespace test