diff options
author | Moritz Pflanzer <moritz.pflanzer@arm.com> | 2017-09-08 09:53:14 +0100 |
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committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:35:24 +0000 |
commit | cde1e8adeacea5c33a1682ef7b05a0ef643463b8 (patch) | |
tree | 47e58abdf5bb6ef39db362a2ac777c93b3f76666 /tests/validation/fixtures | |
parent | 86b53339679e12c952a24a8845a5409ac3d52de6 (diff) | |
download | ComputeLibrary-cde1e8adeacea5c33a1682ef7b05a0ef643463b8.tar.gz |
COMPMID-415: Add tests for ConvolutionLayer reshaped weights
Change-Id: I6c1209a2afafccba2cbdbcda16aceb3ae0cc7b4b
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/87000
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Diffstat (limited to 'tests/validation/fixtures')
3 files changed, 202 insertions, 43 deletions
diff --git a/tests/validation/fixtures/ConvolutionLayerFixture.h b/tests/validation/fixtures/ConvolutionLayerFixture.h index 87b11ac130..dd2df727e9 100644 --- a/tests/validation/fixtures/ConvolutionLayerFixture.h +++ b/tests/validation/fixtures/ConvolutionLayerFixture.h @@ -32,6 +32,7 @@ #include "tests/framework/Asserts.h" #include "tests/framework/Fixture.h" #include "tests/validation/CPP/ConvolutionLayer.h" +#include "tests/validation/CPP/Utils.h" #include "tests/validation/Helpers.h" #include <random> @@ -47,12 +48,12 @@ class ConvolutionValidationFixedPointFixture : public framework::Fixture { public: template <typename...> - void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, DataType data_type, int fractional_bits) + void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, bool reshape_weights, DataType data_type, int fractional_bits) { _fractional_bits = fractional_bits; _data_type = data_type; - _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, data_type, fractional_bits); + _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, reshape_weights, data_type, fractional_bits); _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type, fractional_bits); } @@ -75,17 +76,45 @@ protected: } TensorType compute_target(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info, - DataType data_type, int fixed_point_position) + bool reshape_weights, DataType data_type, int fixed_point_position) { + WeightsInfo weights_info(!reshape_weights, weights_shape.x(), weights_shape.y(), weights_shape[3]); + TensorShape reshaped_weights_shape(weights_shape); + + if(!reshape_weights) + { + // Check if its a "fully connected" convolution + const bool is_fully_connected_convolution = (output_shape.x() == 1 && output_shape.y() == 1); + reshaped_weights_shape.collapse(3); + + if(bias_shape.total_size() > 0) + { + reshaped_weights_shape.set(0, reshaped_weights_shape.x() + 1); + } + + if(is_fully_connected_convolution) + { + const size_t shape_x = reshaped_weights_shape.x(); + reshaped_weights_shape.set(0, reshaped_weights_shape.y()); + reshaped_weights_shape.set(1, shape_x); + } + else + { + const int interleave_width = 16 / data_size_from_type(data_type); + reshaped_weights_shape.set(0, reshaped_weights_shape.x() * interleave_width); + reshaped_weights_shape.set(1, static_cast<unsigned int>(std::ceil(reshaped_weights_shape.y() / static_cast<float>(interleave_width)))); + } + } + // Create tensors TensorType src = create_tensor<TensorType>(input_shape, data_type, 1, fixed_point_position); - TensorType weights = create_tensor<TensorType>(weights_shape, data_type, 1, fixed_point_position); + TensorType weights = create_tensor<TensorType>(reshaped_weights_shape, data_type, 1, fixed_point_position); TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1, fixed_point_position); TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1, fixed_point_position); // Create and configure function FunctionType conv; - conv.configure(&src, &weights, &bias, &dst, info); + conv.configure(&src, &weights, &bias, &dst, info, weights_info); ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS); @@ -105,8 +134,41 @@ protected: // Fill tensors fill(AccessorType(src), 0); - fill(AccessorType(weights), 1); - fill(AccessorType(bias), 2); + + if(!reshape_weights) + { + const bool is_fully_connected_convolution = (output_shape.x() == 1 && output_shape.y() == 1); + + TensorShape tmp_weights_shape(weights_shape); + SimpleTensor<T> tmp_weights(tmp_weights_shape, data_type, 1, fixed_point_position); + SimpleTensor<T> tmp_bias(bias_shape, data_type, 1, fixed_point_position); + + // Fill with original shape + fill(tmp_weights, 1); + fill(tmp_bias, 2); + + tmp_weights = linearise_weights(tmp_weights, &tmp_bias); + + if(!is_fully_connected_convolution) + { + // Transpose with interleave + const int interleave_size = 16 / tmp_weights.element_size(); + tmp_weights = transpose(std::move(tmp_weights), interleave_size); + } + + AccessorType weights_accessor(weights); + + for(int i = 0; i < tmp_weights.num_elements(); ++i) + { + Coordinates coord = index2coord(tmp_weights.shape(), i); + std::copy_n(static_cast<const T *>(tmp_weights(coord)), 1, static_cast<T *>(weights_accessor(coord))); + } + } + else + { + fill(AccessorType(weights), 1); + fill(AccessorType(bias), 2); + } // Compute NEConvolutionLayer function conv.run(); @@ -134,6 +196,51 @@ protected: SimpleTensor<T> _reference{}; int _fractional_bits{}; DataType _data_type{}; + +private: + template <typename U> + SimpleTensor<U> linearise_weights(const SimpleTensor<U> &weights, const SimpleTensor<U> *biases = nullptr) + { + TensorShape dst_shape(weights.shape()); + dst_shape.collapse(3); + + if(biases != nullptr) + { + dst_shape.set(0, dst_shape.x() + 1); + } + + const size_t shape_x = dst_shape.x(); + dst_shape.set(0, dst_shape.y()); + dst_shape.set(1, shape_x); + + SimpleTensor<U> dst(dst_shape, weights.data_type()); + + // Don't iterate over biases yet + for(int weights_idx = 0; weights_idx < weights.num_elements(); ++weights_idx) + { + Coordinates weights_coord = index2coord(weights.shape(), weights_idx); + const int dst_row = weights_idx % weights.shape().total_size_lower(3); + Coordinates dst_coord{ weights_coord[3], dst_row, weights_coord[4] }; + const int dst_idx = coord2index(dst.shape(), dst_coord); + + dst[dst_idx] = weights[weights_idx]; + } + + if(biases != nullptr) + { + // Fill last row with biases + for(int bias_idx = 0; bias_idx < biases->num_elements(); ++bias_idx) + { + Coordinates bias_coord = index2coord(biases->shape(), bias_idx); + Coordinates dst_coord{ bias_coord.x(), static_cast<int>(dst.shape().y()) - 1, bias_coord.y() }; + int dst_idx = coord2index(dst.shape(), dst_coord); + + dst[dst_idx] = (*biases)[bias_idx]; + } + } + + return dst; + } }; template <typename TensorType, typename AccessorType, typename FunctionType, typename T> @@ -141,9 +248,9 @@ class ConvolutionValidationFixture : public ConvolutionValidationFixedPointFixtu { public: template <typename...> - void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, DataType data_type) + void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, bool reshape_weights, DataType data_type) { - ConvolutionValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, data_type, 0); + ConvolutionValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, reshape_weights, data_type, 0); } }; } // namespace validation diff --git a/tests/validation/fixtures/DirectConvolutionLayerFixture.h b/tests/validation/fixtures/DirectConvolutionLayerFixture.h index 6ffebce108..a709157c7b 100644 --- a/tests/validation/fixtures/DirectConvolutionLayerFixture.h +++ b/tests/validation/fixtures/DirectConvolutionLayerFixture.h @@ -41,20 +41,103 @@ namespace test namespace validation { template <typename TensorType, typename AccessorType, typename FunctionType, typename T> -class DirectConvolutionValidationFixedPointFixture : public ConvolutionValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T> +class DirectConvolutionValidationFixedPointFixture : public framework::Fixture { public: template <typename...> void setup(TensorShape input_shape, int stride_x, int stride_y, int pad_x, int pad_y, unsigned int kernel_size, unsigned int num_kernels, DataType data_type, int fractional_bits) { + _fractional_bits = fractional_bits; + _data_type = data_type; + const TensorShape weights_shape(kernel_size, kernel_size, input_shape.z(), num_kernels); const TensorShape bias_shape(num_kernels); const PadStrideInfo info(stride_x, stride_y, pad_x, pad_y, DimensionRoundingType::FLOOR); const TensorShape output_shape = get_output_shape(input_shape, weights_shape, info); - ConvolutionValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, data_type, fractional_bits); + _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, data_type, fractional_bits); + _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type, fractional_bits); + } + +protected: + template <typename U> + void fill(U &&tensor, int i) + { + switch(tensor.data_type()) + { + case DataType::F16: + case DataType::F32: + { + std::uniform_real_distribution<> distribution(-1.0f, 1.0f); + library->fill(tensor, distribution, i); + break; + } + default: + library->fill_tensor_uniform(tensor, i); + } + } + + TensorType compute_target(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info, + DataType data_type, int fixed_point_position) + { + // Create tensors + TensorType src = create_tensor<TensorType>(input_shape, data_type, 1, fixed_point_position); + TensorType weights = create_tensor<TensorType>(weights_shape, data_type, 1, fixed_point_position); + TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1, fixed_point_position); + TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1, fixed_point_position); + + // Create and configure function + FunctionType conv; + conv.configure(&src, &weights, &bias, &dst, info); + + ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Allocate tensors + src.allocator()->allocate(); + weights.allocator()->allocate(); + bias.allocator()->allocate(); + dst.allocator()->allocate(); + + ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!weights.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Fill tensors + fill(AccessorType(src), 0); + fill(AccessorType(weights), 1); + fill(AccessorType(bias), 2); + + // Compute NEConvolutionLayer function + conv.run(); + + return dst; + } + + SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info, + DataType data_type, int fixed_point_position) + { + // Create reference + SimpleTensor<T> src{ input_shape, data_type, 1, fixed_point_position }; + SimpleTensor<T> weights{ weights_shape, data_type, 1, fixed_point_position }; + SimpleTensor<T> bias{ bias_shape, data_type, 1, fixed_point_position }; + + // Fill reference + fill(src, 0); + fill(weights, 1); + fill(bias, 2); + + return reference::convolution_layer<T>(src, weights, bias, output_shape, info); } + TensorType _target{}; + SimpleTensor<T> _reference{}; + int _fractional_bits{}; + DataType _data_type{}; + private: TensorShape get_output_shape(TensorShape in_shape, TensorShape kernel_shape, const PadStrideInfo &info) { diff --git a/tests/validation/fixtures/FullyConnectedLayerFixture.h b/tests/validation/fixtures/FullyConnectedLayerFixture.h index d4d68f1af8..b19c40d5ea 100644 --- a/tests/validation/fixtures/FullyConnectedLayerFixture.h +++ b/tests/validation/fixtures/FullyConnectedLayerFixture.h @@ -34,6 +34,7 @@ #include "tests/framework/Asserts.h" #include "tests/framework/Fixture.h" #include "tests/validation/CPP/FullyConnectedLayer.h" +#include "tests/validation/CPP/Utils.h" #include "tests/validation/Helpers.h" #include <random> @@ -44,38 +45,6 @@ namespace test { namespace validation { -namespace -{ -RawTensor transpose(const RawTensor &src, int interleave = 1) -{ - // Create reference - TensorShape dst_shape(src.shape()); - dst_shape.set(0, src.shape().y() * interleave); - dst_shape.set(1, std::ceil(src.shape().x() / static_cast<float>(interleave))); - - RawTensor dst{ dst_shape, src.data_type() }; - - // Compute reference - uint8_t *out_ptr = dst.data(); - - for(int i = 0; i < dst.num_elements(); i += interleave) - { - Coordinates coord = index2coord(dst.shape(), i); - size_t coord_x = coord.x(); - coord.set(0, coord.y() * interleave); - coord.set(1, coord_x / interleave); - - const int num_elements = std::min<int>(interleave, src.shape().x() - coord.x()); - - std::copy_n(static_cast<const uint8_t *>(src(coord)), num_elements * src.element_size(), out_ptr); - - out_ptr += interleave * dst.element_size(); - } - - return dst; -} -} // namespace - template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool run_interleave> class FullyConnectedLayerValidationFixedPointFixture : public framework::Fixture { |