tests/validation/fixtures/ConvolutionLayerFixture.h

   1 /*
   2  * Copyright (c) 2017 ARM Limited.
   3  *
   4  * SPDX-License-Identifier: MIT
   5  *
   6  * Permission is hereby granted, free of charge, to any person obtaining a copy
   7  * of this software and associated documentation files (the "Software"), to
   8  * deal in the Software without restriction, including without limitation the
   9  * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
  10  * sell copies of the Software, and to permit persons to whom the Software is
  11  * furnished to do so, subject to the following conditions:
  12  *
  13  * The above copyright notice and this permission notice shall be included in all
  14  * copies or substantial portions of the Software.
  15  *
  16  * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  17  * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  18  * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  19  * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  20  * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  21  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  22  * SOFTWARE.
  23  */
  24 #ifndef ARM_COMPUTE_TEST_CONVOLUTION_LAYER_FIXTURE
  25 #define ARM_COMPUTE_TEST_CONVOLUTION_LAYER_FIXTURE
  26
  27 #include "arm_compute/core/TensorShape.h"
  28 #include "arm_compute/core/Types.h"
  29 #include "arm_compute/runtime/NEON/NEScheduler.h"
  30 #include "tests/AssetsLibrary.h"
  31 #include "tests/Globals.h"
  32 #include "tests/IAccessor.h"
  33 #include "tests/framework/Asserts.h"
  34 #include "tests/framework/Fixture.h"
  35 #include "tests/validation/CPP/ConvolutionLayer.h"
  36 #include "tests/validation/CPP/Utils.h"
  37 #include "tests/validation/Helpers.h"
  38
  39 #include <random>
  40
  41 namespace arm_compute
  42 {
  43 class NEConvolutionLayer;
  44
  45 namespace test
  46 {
  47 namespace validation
  48 {
  49 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
  50 class ConvolutionValidationFixedPointFixture : public framework::Fixture
  51 {
  52 public:
  53     template <typename...>
  54     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)
  55     {
  56         _fractional_bits = fractional_bits;
  57         _data_type       = data_type;
  58
  59         _target    = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, reshape_weights, data_type, fractional_bits);
  60         _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type, fractional_bits);
  61     }
  62
  63 protected:
  64     template <typename U>
  65     void fill(U &&tensor, int i)
  66     {
  67         switch(tensor.data_type())
  68         {
  69             case DataType::F16:
  70             case DataType::F32:
  71             {
  72                 std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
  73                 library->fill(tensor, distribution, i);
  74                 break;
  75             }
  76             default:
  77                 library->fill_tensor_uniform(tensor, i);
  78         }
  79     }
  80
  81     TensorType compute_target(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info,
  82                               bool reshape_weights, DataType data_type, int fixed_point_position)
  83     {
  84         WeightsInfo weights_info(!reshape_weights, weights_shape.x(), weights_shape.y(), weights_shape[3]);
  85         TensorShape reshaped_weights_shape(weights_shape);
  86
  87         if(!reshape_weights)
  88         {
  89             // Check if its a "fully connected" convolution
  90             const bool is_fully_connected_convolution = (output_shape.x() == 1 && output_shape.y() == 1);
  91             bool       is_optimised                   = false;
  92 #if defined(__arm__)
  93             is_optimised = std::is_same<FunctionType, NEConvolutionLayer>::value && NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && data_type == DataType::F32;
  94 #elif defined(__aarch64__)
  95             is_optimised = std::is_same<FunctionType, NEConvolutionLayer>::value && NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && data_type == DataType::F32;
  96 #endif /* defined(__arm__) || defined(__aarch64__) */
  97
  98             reshaped_weights_shape.collapse(3);
  99
 100             if(bias_shape.total_size() > 0)
 101             {
 102                 reshaped_weights_shape.set(0, reshaped_weights_shape.x() + 1);
 103             }
 104
 105             if(is_fully_connected_convolution || is_optimised)
 106             {
 107                 const size_t shape_x = reshaped_weights_shape.x();
 108                 reshaped_weights_shape.set(0, reshaped_weights_shape.y());
 109                 reshaped_weights_shape.set(1, shape_x);
 110             }
 111             else
 112             {
 113                 const int interleave_width = 16 / data_size_from_type(data_type);
 114                 reshaped_weights_shape.set(0, reshaped_weights_shape.x() * interleave_width);
 115                 reshaped_weights_shape.set(1, static_cast<unsigned int>(std::ceil(reshaped_weights_shape.y() / static_cast<float>(interleave_width))));
 116             }
 117         }
 118
 119         // Create tensors
 120         TensorType src     = create_tensor<TensorType>(input_shape, data_type, 1, fixed_point_position);
 121         TensorType weights = create_tensor<TensorType>(reshaped_weights_shape, data_type, 1, fixed_point_position);
 122         TensorType bias    = create_tensor<TensorType>(bias_shape, data_type, 1, fixed_point_position);
 123         TensorType dst     = create_tensor<TensorType>(output_shape, data_type, 1, fixed_point_position);
 124
 125         // Create and configure function
 126         FunctionType conv;
 127         conv.configure(&src, &weights, &bias, &dst, info, weights_info);
 128
 129         ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
 130         ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS);
 131         ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
 132         ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
 133
 134         // Allocate tensors
 135         src.allocator()->allocate();
 136         weights.allocator()->allocate();
 137         bias.allocator()->allocate();
 138         dst.allocator()->allocate();
 139
 140         ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
 141         ARM_COMPUTE_EXPECT(!weights.info()->is_resizable(), framework::LogLevel::ERRORS);
 142         ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
 143         ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
 144
 145         // Fill tensors
 146         fill(AccessorType(src), 0);
 147
 148         if(!reshape_weights)
 149         {
 150             const bool is_fully_connected_convolution = (output_shape.x() == 1 && output_shape.y() == 1);
 151             bool       is_optimised                   = false;
 152 #if defined(__arm__)
 153             is_optimised = std::is_same<FunctionType, NEConvolutionLayer>::value && NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && data_type == DataType::F32;
 154 #elif defined(__aarch64__)
 155             is_optimised = std::is_same<FunctionType, NEConvolutionLayer>::value && NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && data_type == DataType::F32;
 156 #endif /* defined(__arm__) || defined(__aarch64__) */
 157
 158             TensorShape     tmp_weights_shape(weights_shape);
 159             SimpleTensor<T> tmp_weights(tmp_weights_shape, data_type, 1, fixed_point_position);
 160             SimpleTensor<T> tmp_bias(bias_shape, data_type, 1, fixed_point_position);
 161
 162             // Fill with original shape
 163             fill(tmp_weights, 1);
 164             fill(tmp_bias, 2);
 165
 166             tmp_weights = linearise_weights(tmp_weights, &tmp_bias);
 167
 168             if(!is_fully_connected_convolution && !is_optimised)
 169             {
 170                 // Transpose with interleave
 171                 const int interleave_size = 16 / tmp_weights.element_size();
 172                 tmp_weights               = transpose(std::move(tmp_weights), interleave_size);
 173             }
 174
 175             AccessorType weights_accessor(weights);
 176
 177             for(int i = 0; i < tmp_weights.num_elements(); ++i)
 178             {
 179                 Coordinates coord = index2coord(tmp_weights.shape(), i);
 180                 std::copy_n(static_cast<const T *>(tmp_weights(coord)), 1, static_cast<T *>(weights_accessor(coord)));
 181             }
 182         }
 183         else
 184         {
 185             fill(AccessorType(weights), 1);
 186             fill(AccessorType(bias), 2);
 187         }
 188
 189         // Compute NEConvolutionLayer function
 190         conv.run();
 191
 192         return dst;
 193     }
 194
 195     SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info,
 196                                       DataType data_type, int fixed_point_position)
 197     {
 198         // Create reference
 199         SimpleTensor<T> src{ input_shape, data_type, 1, fixed_point_position };
 200         SimpleTensor<T> weights{ weights_shape, data_type, 1, fixed_point_position };
 201         SimpleTensor<T> bias{ bias_shape, data_type, 1, fixed_point_position };
 202
 203         // Fill reference
 204         fill(src, 0);
 205         fill(weights, 1);
 206         fill(bias, 2);
 207
 208         return reference::convolution_layer<T>(src, weights, bias, output_shape, info);
 209     }
 210
 211     TensorType      _target{};
 212     SimpleTensor<T> _reference{};
 213     int             _fractional_bits{};
 214     DataType        _data_type{};
 215
 216 private:
 217     template <typename U>
 218     SimpleTensor<U> linearise_weights(const SimpleTensor<U> &weights, const SimpleTensor<U> *biases = nullptr)
 219     {
 220         TensorShape dst_shape(weights.shape());
 221         dst_shape.collapse(3);
 222
 223         if(biases != nullptr)
 224         {
 225             dst_shape.set(0, dst_shape.x() + 1);
 226         }
 227
 228         const size_t shape_x = dst_shape.x();
 229         dst_shape.set(0, dst_shape.y());
 230         dst_shape.set(1, shape_x);
 231
 232         SimpleTensor<U> dst(dst_shape, weights.data_type());
 233
 234         // Don't iterate over biases yet
 235         for(int weights_idx = 0; weights_idx < weights.num_elements(); ++weights_idx)
 236         {
 237             Coordinates weights_coord = index2coord(weights.shape(), weights_idx);
 238             const int   dst_row       = weights_idx % weights.shape().total_size_lower(3);
 239             Coordinates dst_coord{ weights_coord[3], dst_row, weights_coord[4] };
 240             const int   dst_idx = coord2index(dst.shape(), dst_coord);
 241
 242             dst[dst_idx] = weights[weights_idx];
 243         }
 244
 245         if(biases != nullptr)
 246         {
 247             // Fill last row with biases
 248             for(int bias_idx = 0; bias_idx < biases->num_elements(); ++bias_idx)
 249             {
 250                 Coordinates bias_coord = index2coord(biases->shape(), bias_idx);
 251                 Coordinates dst_coord{ bias_coord.x(), static_cast<int>(dst.shape().y()) - 1, bias_coord.y() };
 252                 int         dst_idx = coord2index(dst.shape(), dst_coord);
 253
 254                 dst[dst_idx] = (*biases)[bias_idx];
 255             }
 256         }
 257
 258         return dst;
 259     }
 260 };
 261
 262 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
 263 class ConvolutionValidationFixture : public ConvolutionValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T>
 264 {
 265 public:
 266     template <typename...>
 267     void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, bool reshape_weights, DataType data_type)
 268     {
 269         ConvolutionValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, reshape_weights, data_type, 0);
 270     }
 271 };
 272 } // namespace validation
 273 } // namespace test
 274 } // namespace arm_compute
 275 #endif /* ARM_COMPUTE_TEST_CONVOLUTION_LAYER_FIXTURE */