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-rw-r--r--tests/validation/TensorOperations.h223
1 files changed, 0 insertions, 223 deletions
diff --git a/tests/validation/TensorOperations.h b/tests/validation/TensorOperations.h
index f5be139dcf..e68a344112 100644
--- a/tests/validation/TensorOperations.h
+++ b/tests/validation/TensorOperations.h
@@ -1071,229 +1071,6 @@ void batch_normalization_layer(const Tensor<T> &in, Tensor<T> &out, const Tensor
}
}
-// Pooling layer
-template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type * = nullptr>
-void pooling_layer(const Tensor<T> &in, Tensor<T> &out, PoolingLayerInfo pool_info)
-{
- const int pool_size = pool_info.pool_size();
- PoolingType type = pool_info.pool_type();
- int pool_stride_x = 0;
- int pool_stride_y = 0;
- int pad_x = 0;
- int pad_y = 0;
- std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info().stride();
- std::tie(pad_x, pad_y) = pool_info.pad_stride_info().pad();
-
- const int w_in = static_cast<int>(in.shape()[0]);
- const int h_in = static_cast<int>(in.shape()[1]);
-
- const int w_out = static_cast<int>(out.shape()[0]);
- const int h_out = static_cast<int>(out.shape()[1]);
-
- int upper_dims = in.shape().total_size() / (w_in * h_in);
-
- int pooled_w = 0;
- int pooled_h = 0;
- if(pool_info.pad_stride_info().round() == DimensionRoundingType::CEIL)
- {
- pooled_w = static_cast<int>(ceil(static_cast<float>(w_in + 2 * pad_x - pool_size) / pool_stride_x)) + 1;
- pooled_h = static_cast<int>(ceil(static_cast<float>(h_in + 2 * pad_y - pool_size) / pool_stride_y)) + 1;
- }
- else
- {
- pooled_w = static_cast<int>(floor(static_cast<float>(w_in + 2 * pad_x - pool_size) / pool_stride_x)) + 1;
- pooled_h = static_cast<int>(floor(static_cast<float>(h_in + 2 * pad_y - pool_size) / pool_stride_y)) + 1;
- }
-
- if((pooled_w - 1) * pool_stride_x >= w_in + pad_x)
- {
- --pooled_w;
- }
- if((pooled_h - 1) * pool_stride_y >= h_in + pad_y)
- {
- --pooled_h;
- }
-
- if(type == PoolingType::MAX)
- {
- for(int r = 0; r < upper_dims; ++r)
- {
- for(int h = 0; h < pooled_h; ++h)
- {
- for(int w = 0; w < pooled_w; ++w)
- {
- int wstart = w * pool_stride_x - pad_x;
- int hstart = h * pool_stride_y - pad_y;
- int wend = std::min(wstart + pool_size, w_in);
- int hend = std::min(hstart + pool_size, h_in);
- wstart = std::max(wstart, 0);
- hstart = std::max(hstart, 0);
-
- T max_val = std::numeric_limits<T>::lowest();
- for(int y = hstart; y < hend; ++y)
- {
- for(int x = wstart; x < wend; ++x)
- {
- const T val = in[r * h_in * w_in + y * w_in + x];
- if(val > max_val)
- {
- max_val = val;
- }
- }
- }
-
- out[r * h_out * w_out + h * pooled_w + w] = max_val;
- }
- }
- }
- }
- else // Average pooling
- {
- for(int r = 0; r < upper_dims; ++r)
- {
- for(int h = 0; h < pooled_h; ++h)
- {
- for(int w = 0; w < pooled_w; ++w)
- {
- T avg_val(0);
- int wstart = w * pool_stride_x - pad_x;
- int hstart = h * pool_stride_y - pad_y;
- int wend = std::min(wstart + pool_size, w_in + pad_x);
- int hend = std::min(hstart + pool_size, h_in + pad_y);
- int pool = (hend - hstart) * (wend - wstart);
- wstart = std::max(wstart, 0);
- hstart = std::max(hstart, 0);
- wend = std::min(wend, w_in);
- hend = std::min(hend, h_in);
-
- for(int y = hstart; y < hend; ++y)
- {
- for(int x = wstart; x < wend; ++x)
- {
- avg_val += in[r * h_in * w_in + y * w_in + x];
- }
- }
- out[r * h_out * w_out + h * pooled_w + w] = avg_val / pool;
- }
- }
- }
- }
-}
-
-// Pooling layer
-template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type * = nullptr>
-void pooling_layer(const Tensor<T> &in, Tensor<T> &out, PoolingLayerInfo pool_info)
-{
- const int pool_size = pool_info.pool_size();
- PoolingType type = pool_info.pool_type();
- int pool_stride_x = 0;
- int pool_stride_y = 0;
- int pad_x = 0;
- int pad_y = 0;
- std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info().stride();
- std::tie(pad_x, pad_y) = pool_info.pad_stride_info().pad();
-
- const int w_in = static_cast<int>(in.shape()[0]);
- const int h_in = static_cast<int>(in.shape()[1]);
-
- const int w_out = static_cast<int>(out.shape()[0]);
- const int h_out = static_cast<int>(out.shape()[1]);
-
- int upper_dims = in.shape().total_size() / (w_in * h_in);
-
- int pooled_w = 0;
- int pooled_h = 0;
- if(pool_info.pad_stride_info().round() == DimensionRoundingType::CEIL)
- {
- pooled_w = static_cast<int>(ceil(static_cast<float>(w_in + 2 * pad_x - pool_size) / pool_stride_x)) + 1;
- pooled_h = static_cast<int>(ceil(static_cast<float>(h_in + 2 * pad_y - pool_size) / pool_stride_y)) + 1;
- }
- else
- {
- pooled_w = static_cast<int>(floor(static_cast<float>(w_in + 2 * pad_x - pool_size) / pool_stride_x)) + 1;
- pooled_h = static_cast<int>(floor(static_cast<float>(h_in + 2 * pad_y - pool_size) / pool_stride_y)) + 1;
- }
-
- if((pooled_w - 1) * pool_stride_x >= w_in + pad_x)
- {
- --pooled_w;
- }
- if((pooled_h - 1) * pool_stride_y >= h_in + pad_y)
- {
- --pooled_h;
- }
-
- if(type == PoolingType::MAX)
- {
- for(int r = 0; r < upper_dims; ++r)
- {
- for(int h = 0; h < pooled_h; ++h)
- {
- for(int w = 0; w < pooled_w; ++w)
- {
- int wstart = w * pool_stride_x - pad_x;
- int hstart = h * pool_stride_y - pad_y;
- int wend = std::min(wstart + pool_size, w_in);
- int hend = std::min(hstart + pool_size, h_in);
- wstart = std::max(wstart, 0);
- hstart = std::max(hstart, 0);
-
- T max_val = std::numeric_limits<T>::lowest();
- for(int y = hstart; y < hend; ++y)
- {
- for(int x = wstart; x < wend; ++x)
- {
- T val = in[r * h_in * w_in + y * w_in + x];
- if(val > max_val)
- {
- max_val = val;
- }
- }
- }
-
- out[r * h_out * w_out + h * pooled_w + w] = max_val;
- }
- }
- }
- }
- else // Average pooling
- {
- for(int r = 0; r < upper_dims; ++r)
- {
- for(int h = 0; h < pooled_h; ++h)
- {
- for(int w = 0; w < pooled_w; ++w)
- {
- int wstart = w * pool_stride_x - pad_x;
- int hstart = h * pool_stride_y - pad_y;
- int wend = std::min(wstart + pool_size, w_in + pad_x);
- int hend = std::min(hstart + pool_size, h_in + pad_y);
- int pool = (hend - hstart) * (wend - wstart);
- wstart = std::max(wstart, 0);
- hstart = std::max(hstart, 0);
- wend = std::min(wend, w_in);
- hend = std::min(hend, h_in);
-
- using namespace fixed_point_arithmetic;
-
- const int fixed_point_position = in.fixed_point_position();
- const fixed_point<T> invpool_fp(1.f / static_cast<float>(pool), fixed_point_position);
- fixed_point<T> avg_val(0, fixed_point_position, true);
- for(int y = hstart; y < hend; ++y)
- {
- for(int x = wstart; x < wend; ++x)
- {
- const fixed_point<T> in_fp(in[r * h_in * w_in + y * w_in + x], fixed_point_position, true);
- avg_val = add(avg_val, in_fp);
- }
- }
- out[r * h_out * w_out + h * pooled_w + w] = mul(avg_val, invpool_fp).raw();
- }
- }
- }
- }
-}
-
// ROI Pooling layer
template <typename T>
void roi_pooling_layer(const Tensor<T> &in, Tensor<T> &out, const std::vector<ROI> &rois, const ROIPoolingLayerInfo &pool_info)
generated by cgit v1.2.1 (git 2.23.0) at 2024-06-12 13:47:37 +0000