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>
diff --git a/tests/validation/fixtures/DirectConvolutionLayerFixture.h b/tests/validation/fixtures/DirectConvolutionLayerFixture.h
index 6ffebce..a709157 100644
--- a/tests/validation/fixtures/DirectConvolutionLayerFixture.h
+++ b/tests/validation/fixtures/DirectConvolutionLayerFixture.h
@@ -41,20 +41,103 @@
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)
{