diff options
Diffstat (limited to 'tests/validation/TensorOperations.h')
| -rw-r--r-- | tests/validation/TensorOperations.h | 84 |
1 files changed, 46 insertions, 38 deletions
diff --git a/tests/validation/TensorOperations.h b/tests/validation/TensorOperations.h index 119be02423..7337924b47 100644 --- a/tests/validation/TensorOperations.h +++ b/tests/validation/TensorOperations.h @@ -1154,47 +1154,57 @@ void pooling_layer(const Tensor<T> &in, Tensor<T> &out, PoolingLayerInfo pool_in 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 cols_in = static_cast<int>(in.shape()[0]); - const int rows_in = static_cast<int>(in.shape()[1]); + const int w_in = static_cast<int>(in.shape()[0]); + const int h_in = static_cast<int>(in.shape()[1]); - const int cols_out = static_cast<int>(out.shape()[0]); - const int rows_out = static_cast<int>(out.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() / (cols_in * rows_in); + int upper_dims = in.shape().total_size() / (w_in * h_in); - int pooled_height = static_cast<int>(ceil(static_cast<float>(rows_in + 2 * pad_x - pool_size) / pool_stride_x)) + 1; - int pooled_width = static_cast<int>(ceil(static_cast<float>(cols_in + 2 * pad_y - pool_size) / pool_stride_y)) + 1; + 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_height - 1) * pool_stride_x >= rows_in + pad_x) + if((pooled_w - 1) * pool_stride_x >= w_in + pad_x) { - --pooled_height; + --pooled_w; } - if((pooled_width - 1) * pool_stride_y >= cols_in + pad_y) + if((pooled_h - 1) * pool_stride_y >= h_in + pad_y) { - --pooled_width; + --pooled_h; } if(type == PoolingType::MAX) { for(int r = 0; r < upper_dims; ++r) { - for(int i = 0; i < pooled_height; ++i) + for(int h = 0; h < pooled_h; ++h) { - for(int k = 0; k < pooled_width; ++k) + for(int w = 0; w < pooled_w; ++w) { - int hstart = i * pool_stride_x - pad_x; - int wstart = k * pool_stride_y - pad_y; - int hend = std::min(hstart + pool_size, rows_in); - int wend = std::min(wstart + pool_size, cols_in); - hstart = std::max(hstart, 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); + 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 * cols_in * rows_in + y * cols_in + x]; + T val = in[r * h_in * w_in + y * w_in + x]; if(val > max_val) { max_val = val; @@ -1202,7 +1212,7 @@ void pooling_layer(const Tensor<T> &in, Tensor<T> &out, PoolingLayerInfo pool_in } } - out[r * rows_out * cols_out + i * pooled_width + k] = max_val; + out[r * h_out * w_out + h * pooled_w + w] = max_val; } } } @@ -1211,32 +1221,30 @@ void pooling_layer(const Tensor<T> &in, Tensor<T> &out, PoolingLayerInfo pool_in { for(int r = 0; r < upper_dims; ++r) { - for(int i = 0; i < pooled_height; ++i) + for(int h = 0; h < pooled_h; ++h) { - for(int k = 0; k < pooled_width; ++k) + for(int w = 0; w < pooled_w; ++w) { - T avg_val = 0; - - int hstart = i * pool_stride_x - pad_x; - int wstart = k * pool_stride_y - pad_y; - int hend = std::min(hstart + pool_size, cols_in + pad_x); - int wend = std::min(wstart + pool_size, rows_in + pad_y); - int pool = (hend - hstart) * (wend - wstart); - hstart = std::max(hstart, 0); - wstart = std::max(wstart, 0); - hend = std::min(hend, rows_in); - wend = std::min(wend, cols_in); - + 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); if(std::is_floating_point<T>::value) { for(int y = hstart; y < hend; ++y) { for(int x = wstart; x < wend; ++x) { - avg_val += in[r * cols_in * rows_in + y * cols_in + x]; + avg_val += in[r * h_in * w_in + y * w_in + x]; } } - out[r * rows_out * cols_out + i * pooled_width + k] = avg_val / pool; + out[r * h_out * w_out + h * pooled_w + w] = avg_val / pool; } else { @@ -1247,10 +1255,10 @@ void pooling_layer(const Tensor<T> &in, Tensor<T> &out, PoolingLayerInfo pool_in { for(int x = wstart; x < wend; ++x) { - avg_val = sqadd_qs8(avg_val, in[r * cols_in * rows_in + y * cols_in + x]); + avg_val = sqadd_qs8(avg_val, in[r * h_in * w_in + y * w_in + x]); } } - out[r * rows_out * cols_out + i * pooled_width + k] = sqmul_qs8(avg_val, (scale_values_q8[pool] >> (7 - fixed_point_position)), fixed_point_position); + out[r * h_out * w_out + h * pooled_w + w] = sqmul_qs8(avg_val, (scale_values_q8[pool] >> (7 - fixed_point_position)), fixed_point_position); } } } |