diff options
Diffstat (limited to 'tests/validation')
-rw-r--r-- | tests/validation/CL/Convolution3D.cpp | 122 | ||||
-rw-r--r-- | tests/validation/fixtures/DirectConvolution3DFixture.h | 51 | ||||
-rw-r--r-- | tests/validation/reference/Conv3D.cpp | 110 | ||||
-rw-r--r-- | tests/validation/reference/Conv3D.h | 4 |
4 files changed, 253 insertions, 34 deletions
diff --git a/tests/validation/CL/Convolution3D.cpp b/tests/validation/CL/Convolution3D.cpp index 75e2e99b03..381aacc465 100644 --- a/tests/validation/CL/Convolution3D.cpp +++ b/tests/validation/CL/Convolution3D.cpp @@ -38,10 +38,11 @@ namespace validation { namespace { -RelativeTolerance<half> tolerance_fp16(half(0.2)); /**< Tolerance for floating point tests */ -RelativeTolerance<float> tolerance_fp32(0.05f); /**< Tolerance for floating point tests */ -constexpr float abs_tolerance_f32(0.0001f); /**< Absolute tolerance for FP32 tests*/ -constexpr float tolerance_num = 0.07f; /**< Tolerance number */ +RelativeTolerance<half> tolerance_fp16(half(0.2)); /**< Tolerance for floating point tests */ +RelativeTolerance<float> tolerance_fp32(0.05f); /**< Tolerance for floating point tests */ +constexpr AbsoluteTolerance<uint8_t> tolerance_qasymm8(1); /**< Tolerance for quantized tests */ +constexpr float abs_tolerance_f32(0.0001f); /**< Absolute tolerance for FP32 tests*/ +constexpr float tolerance_num = 0.07f; /**< Tolerance number */ } // namespace TEST_SUITE(CL) @@ -165,6 +166,8 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zi template <typename T> using CLDirectConvolution3DFixture = DirectConvolution3DValidationFixture<CLTensor, CLAccessor, CLConv3D, T>; +template <typename T> +using CLDirectConvolution3DQuantizedFixture = DirectConvolution3DValidationQuantizedFixture<CLTensor, CLAccessor, CLConv3D, T>; TEST_SUITE(NDHWC) TEST_SUITE(FP16) @@ -266,6 +269,117 @@ FIXTURE_DATA_TEST_CASE(RunLarge, CLDirectConvolution3DFixture<float>, framework: // clang-format on // *INDENT-ON* TEST_SUITE_END() // FP32 + +TEST_SUITE(QASYMM8) +FIXTURE_DATA_TEST_CASE(RunSmall, CLDirectConvolution3DQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, + combine(combine(combine(combine(combine(combine(zip(zip(zip(zip(zip(zip(zip(zip(zip(zip(zip( + framework::dataset::make("InputShape", { TensorShape(7U, 5U, 3U, 13U, 3U), + TensorShape(15U, 7U, 11U, 7U), + TensorShape(19U, 5U, 16U, 4U), + TensorShape(13U, 5U, 17U, 2U) + }), + framework::dataset::make("StrideX", { 1, 3, 2, 1 })), + framework::dataset::make("StrideY", { 2, 1, 3, 1 })), + framework::dataset::make("StrideZ", { 3, 2, 1, 1 })), + framework::dataset::make("PadX", { 0, 2, 1, 0 })), + framework::dataset::make("PadY", { 1, 0, 2, 0 })), + framework::dataset::make("PadZ", { 2, 1, 0, 0 })), + framework::dataset::make("KernelWidth", { 3, 7, 5, 1 })), + framework::dataset::make("KernelHeight", { 5, 3, 7, 1 })), + framework::dataset::make("KernelDepth", { 7, 5, 3, 1 })), + framework::dataset::make("NumKernels", { 5, 3, 1, 11 })), + framework::dataset::make("HasBias", { true, true, true, false })), + framework::dataset::make("Activation", ActivationLayerInfo())), + framework::dataset::make("DataType", DataType::QASYMM8)), + framework::dataset::make("DataLayout", DataLayout::NDHWC)), + framework::dataset::make("SrcQuantizationInfo", QuantizationInfo(0.1f, 10))), + framework::dataset::make("WeightsQuantizationInfo", QuantizationInfo(0.3f, 20))), + framework::dataset::make("DstQuantizationInfo", QuantizationInfo(0.2f, 5)))) +{ + validate(CLAccessor(_target), _reference, tolerance_qasymm8); +} + +FIXTURE_DATA_TEST_CASE(RunLarge, CLDirectConvolution3DQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, + combine(combine(combine(combine(combine(combine(zip(zip(zip(zip(zip(zip(zip(zip(zip(zip(zip( + framework::dataset::make("InputShape", { TensorShape(400U, 400U, 200U, 11U) }), + framework::dataset::make("StrideX", { 1 })), + framework::dataset::make("StrideY", { 1 })), + framework::dataset::make("StrideZ", { 1 })), + framework::dataset::make("PadX", { 1 })), + framework::dataset::make("PadY", { 1 })), + framework::dataset::make("PadZ", { 1 })), + framework::dataset::make("KernelWidth", { 9 })), + framework::dataset::make("KernelHeight", { 9 })), + framework::dataset::make("KernelDepth", { 9 })), + framework::dataset::make("NumKernels", { 300 })), + framework::dataset::make("HasBias", { true })), + framework::dataset::make("Activation", ActivationLayerInfo())), + framework::dataset::make("DataType", DataType::QASYMM8)), + framework::dataset::make("DataLayout", DataLayout::NDHWC)), + framework::dataset::make("SrcQuantizationInfo", QuantizationInfo(0.1f, 10))), + framework::dataset::make("WeightsQuantizationInfo", QuantizationInfo(0.3f, 20))), + framework::dataset::make("DstQuantizationInfo", QuantizationInfo(0.2f, 5)))) +{ + validate(CLAccessor(_target), _reference, tolerance_qasymm8); +} + +TEST_SUITE_END() // QASYMM8 + +TEST_SUITE(QASYMM8_SIGNED) +FIXTURE_DATA_TEST_CASE(RunSmall, CLDirectConvolution3DQuantizedFixture<int8_t>, framework::DatasetMode::PRECOMMIT, + combine(combine(combine(combine(combine(combine(zip(zip(zip(zip(zip(zip(zip(zip(zip(zip(zip( + framework::dataset::make("InputShape", { TensorShape(7U, 5U, 3U, 13U, 3U), + TensorShape(15U, 7U, 11U, 7U), + TensorShape(19U, 5U, 16U, 4U), + TensorShape(13U, 5U, 17U, 2U) + }), + framework::dataset::make("StrideX", { 1, 3, 2, 1 })), + framework::dataset::make("StrideY", { 2, 1, 3, 1 })), + framework::dataset::make("StrideZ", { 3, 2, 1, 1 })), + framework::dataset::make("PadX", { 0, 2, 1, 0 })), + framework::dataset::make("PadY", { 1, 0, 2, 0 })), + framework::dataset::make("PadZ", { 2, 1, 0, 0 })), + framework::dataset::make("KernelWidth", { 3, 7, 5, 1 })), + framework::dataset::make("KernelHeight", { 5, 3, 7, 1 })), + framework::dataset::make("KernelDepth", { 7, 5, 3, 1 })), + framework::dataset::make("NumKernels", { 5, 3, 1, 11 })), + framework::dataset::make("HasBias", { true, true, true, false })), + framework::dataset::make("Activation", ActivationLayerInfo())), + framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)), + framework::dataset::make("DataLayout", DataLayout::NDHWC)), + framework::dataset::make("SrcQuantizationInfo", QuantizationInfo(0.1f, 10))), + framework::dataset::make("WeightsQuantizationInfo", QuantizationInfo(0.3f, 20))), + framework::dataset::make("DstQuantizationInfo", QuantizationInfo(0.2f, 5)))) +{ + validate(CLAccessor(_target), _reference, tolerance_qasymm8); +} + +FIXTURE_DATA_TEST_CASE(RunLarge, CLDirectConvolution3DQuantizedFixture<int8_t>, framework::DatasetMode::NIGHTLY, + combine(combine(combine(combine(combine(combine(zip(zip(zip(zip(zip(zip(zip(zip(zip(zip(zip( + framework::dataset::make("InputShape", { TensorShape(400U, 400U, 200U, 11U) }), + framework::dataset::make("StrideX", { 1 })), + framework::dataset::make("StrideY", { 1 })), + framework::dataset::make("StrideZ", { 1 })), + framework::dataset::make("PadX", { 1 })), + framework::dataset::make("PadY", { 1 })), + framework::dataset::make("PadZ", { 1 })), + framework::dataset::make("KernelWidth", { 9 })), + framework::dataset::make("KernelHeight", { 9 })), + framework::dataset::make("KernelDepth", { 9 })), + framework::dataset::make("NumKernels", { 300 })), + framework::dataset::make("HasBias", { true })), + framework::dataset::make("Activation", ActivationLayerInfo())), + framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)), + framework::dataset::make("DataLayout", DataLayout::NDHWC)), + framework::dataset::make("SrcQuantizationInfo", QuantizationInfo(0.1f, 10))), + framework::dataset::make("WeightsQuantizationInfo", QuantizationInfo(0.3f, 20))), + framework::dataset::make("DstQuantizationInfo", QuantizationInfo(0.2f, 5)))) +{ + validate(CLAccessor(_target), _reference, tolerance_qasymm8); +} + +TEST_SUITE_END() // QASYMM8_SIGNED + TEST_SUITE_END() // NDHWC TEST_SUITE_END() // DirectConvolution3D TEST_SUITE_END() // CL diff --git a/tests/validation/fixtures/DirectConvolution3DFixture.h b/tests/validation/fixtures/DirectConvolution3DFixture.h index 3a675ac6d3..2250dcaeb0 100644 --- a/tests/validation/fixtures/DirectConvolution3DFixture.h +++ b/tests/validation/fixtures/DirectConvolution3DFixture.h @@ -40,19 +40,23 @@ template <typename TensorType, typename AccessorType, typename FunctionType, typ class DirectConvolution3DValidationGenericFixture : public framework::Fixture { public: + using TBias = typename std::conditional < std::is_same<T, uint8_t>::value || std::is_same<T, int8_t>::value, int32_t, T >::type; + template <typename...> void setup(const TensorShape &input_shape, int stride_x, int stride_y, int stride_z, int pad_x, int pad_y, int pad_z, unsigned int kernel_width, int kernel_height, int kernel_depth, - unsigned int num_kernels, bool has_bias, const ActivationLayerInfo &act_info, const DataType &data_type, const DataLayout &data_layout) + unsigned int num_kernels, bool has_bias, const ActivationLayerInfo &act_info, const DataType &data_type, const DataLayout &data_layout, + const QuantizationInfo &src_qinfo = QuantizationInfo(), const QuantizationInfo &weights_qinfo = QuantizationInfo(), const QuantizationInfo &dst_qinfo = QuantizationInfo()) { ARM_COMPUTE_ERROR_ON(data_layout != DataLayout::NDHWC); const TensorShape weights_shape(num_kernels, input_shape[0], kernel_width, kernel_height, kernel_depth); const TensorShape bias_shape(num_kernels); + const DataType bias_data_type = is_data_type_quantized(data_type) ? DataType::S32 : data_type; const Conv3dInfo conv3d_info(Size3D(stride_x, stride_y, stride_z), Padding3D(pad_x, pad_y, pad_z), act_info, Size3D(1U, 1U, 1U), DimensionRoundingType::FLOOR, false); const TensorShape output_shape = compute_conv3d_shape(input_shape, weights_shape, conv3d_info); - _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, conv3d_info, has_bias, data_type, data_layout); - _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, conv3d_info, has_bias, data_type); + _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, conv3d_info, has_bias, data_type, bias_data_type, data_layout, src_qinfo, weights_qinfo, dst_qinfo); + _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, conv3d_info, has_bias, data_type, bias_data_type, src_qinfo, weights_qinfo, dst_qinfo); } protected: @@ -79,13 +83,14 @@ protected: } TensorType compute_target(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const Conv3dInfo &conv3d_info, - bool has_bias, const DataType &data_type, const DataLayout &data_layout) + bool has_bias, const DataType &data_type, const DataType &bias_data_type, const DataLayout &data_layout, const QuantizationInfo &src_qinfo, + const QuantizationInfo &weights_qinfo, const QuantizationInfo &dst_qinfo) { // Create tensors - TensorType src = create_tensor<TensorType>(input_shape, data_type, 1, QuantizationInfo(), data_layout); - TensorType weights = create_tensor<TensorType>(weights_shape, data_type, 1, QuantizationInfo(), data_layout); - TensorType bias = has_bias ? create_tensor<TensorType>(bias_shape, data_type, 1, QuantizationInfo()) : TensorType(); - TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1, QuantizationInfo(), data_layout); + TensorType src = create_tensor<TensorType>(input_shape, data_type, 1, src_qinfo, data_layout); + TensorType weights = create_tensor<TensorType>(weights_shape, data_type, 1, weights_qinfo, data_layout); + TensorType bias = has_bias ? create_tensor<TensorType>(bias_shape, bias_data_type, 1, QuantizationInfo()) : TensorType(); + TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1, dst_qinfo, data_layout); // Create and configure function FunctionType conv{}; @@ -122,14 +127,15 @@ protected: return dst; } - SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const Conv3dInfo &conv3d_info, - bool has_bias, const DataType &data_type) + SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, + const Conv3dInfo &conv3d_info, bool has_bias, const DataType &data_type, const DataType &bias_data_type, const QuantizationInfo &src_qinfo, + const QuantizationInfo &weights_qinfo, const QuantizationInfo &dst_qinfo) { // Create reference - SimpleTensor<T> src{ input_shape, data_type }; - SimpleTensor<T> weights{ weights_shape, data_type }; - SimpleTensor<T> bias{ bias_shape, data_type }; - SimpleTensor<T> dst{ output_shape, data_type }; + SimpleTensor<T> src{ input_shape, data_type, 1, src_qinfo }; + SimpleTensor<T> weights{ weights_shape, data_type, 1, weights_qinfo }; + SimpleTensor<TBias> bias{ bias_shape, bias_data_type }; + SimpleTensor<T> dst{ output_shape, data_type, 1, dst_qinfo }; // Fill reference fill(src, 0); @@ -140,7 +146,7 @@ protected: fill(bias, 2); } - return reference::activation_layer(reference::conv3d<T>(src, weights, bias, dst, conv3d_info), conv3d_info.act_info); + return reference::activation_layer(reference::conv3d<T, TBias>(src, weights, bias, dst, conv3d_info), conv3d_info.act_info); } TensorType _target{}; @@ -159,6 +165,21 @@ public: kernel_depth, num_kernels, has_bias, act_info, data_type, data_layout); } }; + +template <typename TensorType, typename AccessorType, typename FunctionType, typename T> +class DirectConvolution3DValidationQuantizedFixture : public DirectConvolution3DValidationGenericFixture<TensorType, AccessorType, FunctionType, T> +{ +public: + template <typename...> + void setup(TensorShape input_shape, int stride_x, int stride_y, int stride_z, int pad_x, int pad_y, int pad_z, unsigned int kernel_width, int kernel_height, int kernel_depth, + unsigned int num_kernels, bool has_bias, ActivationLayerInfo act_info, DataType data_type, DataLayout data_layout, QuantizationInfo src_qinfo, QuantizationInfo weights_qinfo, + QuantizationInfo dst_qinfo) + { + DirectConvolution3DValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, stride_x, stride_y, stride_z, pad_x, pad_y, pad_z, kernel_width, kernel_height, + kernel_depth, num_kernels, has_bias, act_info, data_type, data_layout, src_qinfo, + weights_qinfo, dst_qinfo); + } +}; } // namespace validation } // namespace test } // namespace arm_compute diff --git a/tests/validation/reference/Conv3D.cpp b/tests/validation/reference/Conv3D.cpp index ad61105b36..706059d1cb 100644 --- a/tests/validation/reference/Conv3D.cpp +++ b/tests/validation/reference/Conv3D.cpp @@ -22,7 +22,11 @@ * SOFTWARE. */ #include "Conv3D.h" + #include "arm_compute/core/utils/misc/ShapeCalculator.h" +#include "arm_compute/core/utils/quantization/AsymmHelpers.h" +#include "support/Requires.h" +#include "tests/validation/reference/UtilsQuantizedAsymm.h" // Source/Destination Tensor shape indices (N D H W C) constexpr unsigned int batch_dim = 4u; @@ -52,11 +56,14 @@ inline bool is_valid_pixel(int i, int min, int max) { return (i >= min && i < max); } + // Evaluate the weights against an element in a given tensor. -template <typename T> -T calculate_conv3d(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const Size3D &dilation, int batch, - int z_start, int y_start, int x_start, int ch_out) +template < typename T, typename TB, typename std::enable_if < validation::is_floating_point<T>::value &&validation::is_floating_point<TB>::value, int >::type = 0 > +T calculate_conv3d(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, const Size3D &dilation, int batch, + int z_start, int y_start, int x_start, int ch_out, UniformQuantizationInfo oq_info) { + ARM_COMPUTE_UNUSED(oq_info); + const unsigned int weights_width = weights.shape()[weights_width_dim]; const unsigned int weights_height = weights.shape()[weights_height_dim]; const unsigned int weights_depth = weights.shape()[weights_depth_dim]; @@ -101,12 +108,89 @@ T calculate_conv3d(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, c } } } - return total; + + const TB *b_ptr = bias.data(); + TB bias_value = b_ptr[ch_out]; + + return total + bias_value; } + +template < typename T, typename TB, ARM_COMPUTE_REQUIRES_TA(std::is_same<T, uint8_t>::value || std::is_same<T, int8_t>::value) > +T calculate_conv3d(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, const Size3D &dilation, int batch, + int z_start, int y_start, int x_start, int ch_out, UniformQuantizationInfo oq_info) +{ + const unsigned int weights_width = weights.shape()[weights_width_dim]; + const unsigned int weights_height = weights.shape()[weights_height_dim]; + const unsigned int weights_depth = weights.shape()[weights_depth_dim]; + + const unsigned int src_channels = src.shape()[channel_dim]; + const unsigned int src_width = src.shape()[width_dim]; + const unsigned int src_height = src.shape()[height_dim]; + const unsigned int src_depth = src.shape()[depth_dim]; + + const UniformQuantizationInfo iq_info = src.quantization_info().uniform(); + const UniformQuantizationInfo wq_info = weights.quantization_info().uniform(); + + const int input_offset = -iq_info.offset; + const float input_scale = iq_info.scale; + int weights_offset = -wq_info.offset; + float weights_scale = wq_info.scale; + const int output_offset = oq_info.offset; + const float output_scale = oq_info.scale; + + int output_multiplier = 0; + int output_shift = 0; + const float multiplier = input_scale * weights_scale / output_scale; + arm_compute::quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift); + + int32_t total(0); + for(unsigned int weight_d = 0; weight_d < weights_depth; ++weight_d) + { + const int idx_z = z_start + dilation.depth * weight_d; + for(unsigned int weight_y = 0; weight_y < weights_height; ++weight_y) + { + const int idx_y = y_start + dilation.height * weight_y; + for(unsigned int weight_x = 0; weight_x < weights_width; ++weight_x) + { + const int idx_x = x_start + dilation.width * weight_x; + + //Check if the point is within padding + const bool is_x_valid = is_valid_pixel(idx_x, 0, src_width); + const bool is_y_valid = is_valid_pixel(idx_y, 0, src_height); + const bool is_z_valid = is_valid_pixel(idx_z, 0, src_depth); + const bool is_invalid_pixel = !(is_x_valid && is_y_valid && is_z_valid); + if(is_invalid_pixel) + { + continue; + } + + for(unsigned int ch_in = 0; ch_in < src_channels; ++ch_in) + { + const T *in_ptr = src.data(); + const T *w_ptr = weights.data(); + + const int in_offset = coord2index(src.shape(), Coordinates{ ch_in, idx_x, idx_y, idx_z, batch }); + const int weight_offset = coord2index(weights.shape(), Coordinates{ ch_out, ch_in, weight_x, weight_y, weight_d }); + T input_value = in_ptr[in_offset]; + T weight_value = w_ptr[weight_offset]; + total += ((input_value + input_offset) * (weight_value + weights_offset)); + } + } + } + } + + const TB *b_ptr = bias.data(); + TB bias_value = b_ptr[ch_out]; + + total += bias_value; + + return validation::quantize_down_scale_by_fixedpoint(total, output_multiplier, output_shift, output_offset, + std::numeric_limits<T>::lowest(), std::numeric_limits<T>::max()); } +} // namespace -template <typename T> -SimpleTensor<T> conv3d(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<T> &bias, SimpleTensor<T> &dst, const Conv3dInfo &conv3d_info) +template <typename T, typename TB> +SimpleTensor<T> conv3d(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, SimpleTensor<T> &dst, const Conv3dInfo &conv3d_info) { // Compute reference const unsigned int batch_size = src.shape()[batch_dim]; @@ -150,14 +234,10 @@ SimpleTensor<T> conv3d(const SimpleTensor<T> &src, const SimpleTensor<T> &weight const int x_start = (x_out * stride_x) - pad_left; for(unsigned int ch_out = 0; ch_out < dst_channels; ++ch_out) { - T weighted_value = calculate_conv3d<T>(src, weights, conv3d_info.dilation, batch, z_start, - y_start, x_start, ch_out); - T *out_ptr = dst.data(); - const T *b_ptr = bias.data(); - T bias_value(0); + T *out_ptr = dst.data(); + const int out_offset = coord2index(dst.shape(), Coordinates{ ch_out, x_out, y_out, z_out, batch }); - bias_value = b_ptr[ch_out]; - out_ptr[out_offset] = weighted_value + bias_value; + out_ptr[out_offset] = calculate_conv3d<T, TB>(src, weights, bias, conv3d_info.dilation, batch, z_start, y_start, x_start, ch_out, dst.quantization_info().uniform()); } } } @@ -170,6 +250,10 @@ template SimpleTensor<float> conv3d(const SimpleTensor<float> &src, const Simple const Conv3dInfo &conv3d_info); template SimpleTensor<half> conv3d(const SimpleTensor<half> &src, const SimpleTensor<half> &weights, const SimpleTensor<half> &bias, SimpleTensor<half> &dst, const Conv3dInfo &conv3d_info); +template SimpleTensor<uint8_t> conv3d(const SimpleTensor<uint8_t> &src, const SimpleTensor<uint8_t> &weights, const SimpleTensor<int32_t> &bias, SimpleTensor<uint8_t> &dst, + const Conv3dInfo &conv3d_info); +template SimpleTensor<int8_t> conv3d(const SimpleTensor<int8_t> &src, const SimpleTensor<int8_t> &weights, const SimpleTensor<int32_t> &bias, SimpleTensor<int8_t> &dst, + const Conv3dInfo &conv3d_info); } // namespace reference } // namespace validation } // namespace test diff --git a/tests/validation/reference/Conv3D.h b/tests/validation/reference/Conv3D.h index ade8a2c242..e3674f4bfb 100644 --- a/tests/validation/reference/Conv3D.h +++ b/tests/validation/reference/Conv3D.h @@ -37,8 +37,8 @@ namespace validation { namespace reference { -template <typename T> -SimpleTensor<T> conv3d(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<T> &bias, SimpleTensor<T> &dst, +template <typename T, typename TB> +SimpleTensor<T> conv3d(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, SimpleTensor<T> &dst, const Conv3dInfo &conv3d_info); } // namespace reference } // namespace validation |