Add unit-test for non-const weights on Fully Connected layers
Resolves: COMPMID-4221
Signed-off-by: Georgios Pinitas <georgios.pinitas@arm.com>
Change-Id: I5c860dc1863535f0558063dfbb6ef703981cce69
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5638
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>
diff --git a/tests/validation/fixtures/FullyConnectedLayerFixture.h b/tests/validation/fixtures/FullyConnectedLayerFixture.h
index 9f6a0a2..e5fea60 100644
--- a/tests/validation/fixtures/FullyConnectedLayerFixture.h
+++ b/tests/validation/fixtures/FullyConnectedLayerFixture.h
@@ -271,6 +271,139 @@
quantization_info, activation_info, mixed_layout);
}
};
+
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class FullyConnectedWithDynamicWeightsFixture : public framework::Fixture
+{
+private:
+ template <typename U>
+ void fill(U &&tensor, int i)
+ {
+ if(_data_type == DataType::F16)
+ {
+ arm_compute::utils::uniform_real_distribution_16bit<half> distribution(-1.0f, 1.0f);
+ library->fill(tensor, distribution, i);
+ }
+ else if(_data_type == DataType::F32)
+ {
+ std::uniform_real_distribution<float> distribution(-1.0f, 1.0f);
+ library->fill(tensor, distribution, i);
+ }
+ else
+ {
+ library->fill_tensor_uniform(tensor, i);
+ }
+ }
+
+ void fill_transposed_weights(TensorType &weights, TensorShape weights_shape, int seed)
+ {
+ RawTensor tmp(weights_shape, _data_type, 1);
+
+ // Fill with original shape
+ fill(tmp, seed);
+
+ // Transpose elementwise
+ tmp = transpose(tmp);
+
+ AccessorType weights_accessor(weights);
+
+ for(int i = 0; i < tmp.num_elements(); ++i)
+ {
+ Coordinates coord = index2coord(tmp.shape(), i);
+ std::copy_n(static_cast<const RawTensor::value_type *>(tmp(coord)),
+ tmp.element_size(),
+ static_cast<RawTensor::value_type *>(weights_accessor(coord)));
+ }
+ }
+
+ void validate_with_tolerance(TensorType &target, SimpleTensor<T> &ref)
+ {
+ if(_data_type == DataType::F32)
+ {
+ constexpr RelativeTolerance<float> rel_tolerance_f32(0.05f);
+ constexpr AbsoluteTolerance<float> abs_tolerance_f32(0.0001f);
+ validate(AccessorType(target), ref, rel_tolerance_f32, 0, abs_tolerance_f32);
+ }
+ else
+ {
+ validate(AccessorType(target), ref);
+ }
+ }
+
+public:
+ template <typename...>
+ void setup(TensorShape src_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape dst_shape,
+ DataType data_type, ActivationLayerInfo activation_info)
+ {
+ _data_type = data_type;
+
+ // Setup tensor meta-data
+ TensorInfo src_info(src_shape, 1, data_type);
+ _src.allocator()->init(src_info);
+
+ TensorShape tr_weights_shape{ weights_shape[1], weights_shape[0] };
+ TensorInfo wei_info(tr_weights_shape, 1, data_type);
+ _weights.allocator()->init(wei_info);
+
+ TensorInfo bias_info(bias_shape, 1, data_type);
+ _bias.allocator()->init(bias_info);
+
+ TensorInfo dst_info(dst_shape, 1, data_type);
+ _dst.allocator()->init(dst_info);
+
+ // Configure FC layer and mark the weights as non constant
+ FullyConnectedLayerInfo fc_info;
+ fc_info.activation_info = activation_info;
+ fc_info.are_weights_reshaped = true;
+ fc_info.constant_weights = false;
+ FunctionType fc;
+ fc.configure(&_src, &_weights, &_bias, &_dst, fc_info);
+
+ // Allocate all the tensors
+ _src.allocator()->allocate();
+ _weights.allocator()->allocate();
+ _bias.allocator()->allocate();
+ _dst.allocator()->allocate();
+
+ // Run multiple iterations with different inputs
+ constexpr int num_iterations = 5;
+ int randomizer_offset = 0;
+ for(int i = 0; i < num_iterations; ++i)
+ {
+ // Run target
+ {
+ fill(AccessorType(_src), randomizer_offset);
+ fill_transposed_weights(_weights, weights_shape, randomizer_offset + 1);
+ fill(AccessorType(_bias), randomizer_offset + 2);
+
+ fc.run();
+ }
+
+ // Run reference and compare
+ {
+ SimpleTensor<T> src{ src_shape, data_type };
+ SimpleTensor<T> weights{ weights_shape, data_type };
+ SimpleTensor<T> bias{ bias_shape, data_type };
+
+ // Fill reference
+ fill(src, randomizer_offset);
+ fill(weights, randomizer_offset + 1);
+ fill(bias, randomizer_offset + 2);
+
+ auto dst = reference::activation_layer(reference::fully_connected_layer<T>(src, weights, bias, dst_shape), activation_info);
+
+ // Validate
+ validate_with_tolerance(_dst, dst);
+ }
+
+ randomizer_offset += 100;
+ }
+ }
+
+private:
+ TensorType _src{}, _weights{}, _bias{}, _dst{};
+ DataType _data_type{ DataType::UNKNOWN };
+};
} // namespace validation
} // namespace test
} // namespace arm_compute