arm_compute v18.08
diff --git a/tests/validation/reference/Im2Col.cpp b/tests/validation/reference/Im2Col.cpp
index 5685b60..0c41d88 100644
--- a/tests/validation/reference/Im2Col.cpp
+++ b/tests/validation/reference/Im2Col.cpp
@@ -23,8 +23,6 @@
*/
#include "Im2Col.h"
-#include "Permute.h"
-
#include "arm_compute/core/Types.h"
#include "tests/validation/Helpers.h"
#include "tests/validation/reference/Utils.h"
@@ -38,36 +36,46 @@
namespace reference
{
template <typename T>
-void im2col_nchw(const SimpleTensor<T> &src, SimpleTensor<T> &dst, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias)
+void im2col_nchw(const SimpleTensor<T> &src, SimpleTensor<T> &dst, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, unsigned int num_groups)
{
ARM_COMPUTE_ERROR_ON(src.data_layout() != DataLayout::NCHW);
- // Create reference
- const int pad_x = conv_info.pad().first;
- const int pad_y = conv_info.pad().second;
const int stride_x = conv_info.stride().first;
const int stride_y = conv_info.stride().second;
const int kernel_width = kernel_dims.width;
const int kernel_height = kernel_dims.height;
+ const int pad_x = conv_info.pad().first;
+ const int pad_y = conv_info.pad().second;
const int src_width = src.shape().x();
const int src_height = src.shape().y();
- const int src_depth = src.shape().z();
+ const int src_channels = src.shape().z();
const int batches = src.shape().total_size_upper(3);
+ const int dst_height = dst.shape().y();
const int pad_val = is_data_type_quantized_asymmetric(src.data_type()) ? src.quantization_info().offset : 0;
+ int dst_idx = 0;
- int dst_idx = 0;
+ // Compute width and height of the convolved tensors
+ std::pair<unsigned int, unsigned int> convolved_dims = scaled_dimensions(src_width, src_height, kernel_dims.width, kernel_dims.height, conv_info);
+
for(int b = 0; b < batches; ++b)
{
- for(int y = -pad_y; y <= (src_height + pad_y - kernel_height); y += stride_y)
+ for(int g = 0; g < static_cast<int>(num_groups); ++g)
{
- for(int x = -pad_x; x <= (src_width + pad_x - kernel_width); x += stride_x)
+ const int first_group_ch = g * (src_channels / num_groups);
+ const int last_group_ch = (g + 1) * (src_channels / num_groups);
+
+ for(int yo = 0; yo < dst_height; ++yo)
{
- for(int z = 0; z < src_depth; ++z)
+ // Compute input spatial coordinates
+ const int xi = (yo % convolved_dims.first) * stride_x;
+ const int yi = (yo / convolved_dims.first) * stride_y;
+
+ for(int ci = first_group_ch; ci < last_group_ch; ++ci)
{
- for(int patch_y = y; patch_y < (y + kernel_height); ++patch_y)
+ for(int yk = 0; yk < kernel_height; ++yk)
{
- for(int patch_x = x; patch_x < (x + kernel_width); ++patch_x)
+ for(int xk = 0; xk < kernel_width; ++xk)
{
- dst[dst_idx++] = tensor_elem_at(src, Coordinates(patch_x, patch_y, z, b), BorderMode::CONSTANT, static_cast<T>(pad_val));
+ dst[dst_idx++] = tensor_elem_at(src, Coordinates(xi + xk - pad_x, yi + yk - pad_y, ci, b), BorderMode::CONSTANT, static_cast<T>(pad_val));
}
}
}
@@ -97,11 +105,15 @@
const int batches = src.shape().total_size_upper(3);
const int pad_val = is_data_type_quantized_asymmetric(src.data_type()) ? src.quantization_info().offset : 0;
int dst_idx = 0;
+
+ const int lasty = src_height + (kernel_height > 1 ? pad_y : 0) - kernel_height;
+ const int lastx = src_width + (kernel_width > 1 ? pad_x : 0) - kernel_width;
+
for(int b = 0; b < batches; ++b)
{
- for(int y = -pad_y; y <= (src_height + pad_y - kernel_height); y += stride_y)
+ for(int y = -pad_y; y <= lasty; y += stride_y)
{
- for(int x = -pad_x; x <= (src_width + pad_x - kernel_width); x += stride_x)
+ for(int x = -pad_x; x <= lastx; x += stride_x)
{
for(int z = 0; z < src_depth; ++z)
{
@@ -124,18 +136,74 @@
}
template <typename T>
-void im2col(const SimpleTensor<T> &src, SimpleTensor<T> &dst, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias)
+void im2col_nhwc_channel_first(const SimpleTensor<T> &src, SimpleTensor<T> &dst, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias)
+{
+ ARM_COMPUTE_ERROR_ON(src.data_layout() != DataLayout::NHWC);
+ const int stride_x = conv_info.stride().first;
+ const int stride_y = conv_info.stride().second;
+ const int kernel_width = kernel_dims.width;
+ const int kernel_height = kernel_dims.height;
+ const int pad_x = conv_info.pad().first;
+ const int pad_y = conv_info.pad().second;
+ const int src_width = src.shape().y();
+ const int src_height = src.shape().z();
+ const int src_channels = src.shape().x();
+ const int batches = src.shape().total_size_upper(3);
+ const int dst_width = has_bias ? dst.shape().x() - 1 : dst.shape().x();
+ const int dst_height = dst.shape().y();
+ const int pad_val = is_data_type_quantized_asymmetric(src.data_type()) ? src.quantization_info().offset : 0;
+
+ // Compute width and height of the convolved tensors
+ std::pair<unsigned int, unsigned int> convolved_dims = scaled_dimensions(src_width, src_height, kernel_dims.width, kernel_dims.height, conv_info);
+
+ for(int b = 0; b < batches; ++b)
+ {
+ for(int yo = 0; yo < dst_height; ++yo)
+ {
+ // Compute input spatial coordinates
+ const int xi = (yo % convolved_dims.first) * stride_x;
+ const int yi = (yo / convolved_dims.first) * stride_y;
+
+ for(int ci = 0; ci < src_channels; ++ci)
+ {
+ for(int yk = 0; yk < kernel_height; ++yk)
+ {
+ for(int xk = 0; xk < kernel_width; ++xk)
+ {
+ dst[ci + (xk + yk * kernel_width) * src_channels + yo * dst.shape().x() + b * dst.shape().x() * dst.shape().y()] = tensor_elem_at(src, Coordinates(ci, xi + xk - pad_x, yi + yk - pad_y, b),
+ BorderMode::CONSTANT, static_cast<T>(pad_val));
+ }
+ }
+ }
+
+ if(has_bias)
+ {
+ dst[dst_width + yo * dst.shape().x() + b * dst.shape().x() * dst.shape().y()] = static_cast<T>(1);
+ }
+ }
+ }
+}
+
+template <typename T>
+void im2col(const SimpleTensor<T> &src, SimpleTensor<T> &dst, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const unsigned int num_groups, bool channels_first_output_nhwc)
{
switch(src.data_layout())
{
case DataLayout::NCHW:
{
- im2col_nchw(src, dst, kernel_dims, conv_info, has_bias);
+ im2col_nchw(src, dst, kernel_dims, conv_info, has_bias, num_groups);
break;
}
case DataLayout::NHWC:
{
- im2col_nhwc(src, dst, kernel_dims, conv_info, has_bias);
+ if(channels_first_output_nhwc)
+ {
+ im2col_nhwc_channel_first(src, dst, kernel_dims, conv_info, has_bias);
+ }
+ else
+ {
+ im2col_nhwc(src, dst, kernel_dims, conv_info, has_bias);
+ }
break;
}
default:
@@ -146,9 +214,12 @@
}
}
-template void im2col(const SimpleTensor<uint8_t> &src, SimpleTensor<uint8_t> &dst, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias);
-template void im2col(const SimpleTensor<half> &src, SimpleTensor<half> &dst, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias);
-template void im2col(const SimpleTensor<float> &src, SimpleTensor<float> &dst, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias);
+template void im2col(const SimpleTensor<uint8_t> &src, SimpleTensor<uint8_t> &dst, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, unsigned int num_groups,
+ bool channels_first_output_nhwc);
+template void im2col(const SimpleTensor<half> &src, SimpleTensor<half> &dst, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, unsigned int num_groups,
+ bool channels_first_output_nhwc);
+template void im2col(const SimpleTensor<float> &src, SimpleTensor<float> &dst, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, unsigned int num_groups,
+ bool channels_first_output_nhwc);
} // namespace reference
} // namespace validation
} // namespace test