COMPMID-417: Fix benchmark tests
Change-Id: I4c4786c38fd3381015abbb9f1ef5612c712594b6
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/86019
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: SiCong Li <sicong.li@arm.com>
diff --git a/tests/TypePrinter.h b/tests/TypePrinter.h
index 394ee9d..c207c1d 100644
--- a/tests/TypePrinter.h
+++ b/tests/TypePrinter.h
@@ -360,7 +360,7 @@
/** Formatted output of @ref PoolingLayerInfo. */
inline ::std::ostream &operator<<(::std::ostream &os, const PoolingLayerInfo &info)
{
- os << info.pool_type();
+ os << info.pool_type() << ";" << info.pool_size() << ";" << info.pad_stride_info();
return os;
}
diff --git a/tests/datasets_new/system_tests/googlenet/inceptionv4/GoogLeNetInceptionV4PoolingLayerDataset.h b/tests/datasets_new/system_tests/googlenet/inceptionv4/GoogLeNetInceptionV4PoolingLayerDataset.h
index 117d96b..f0eb1ea 100644
--- a/tests/datasets_new/system_tests/googlenet/inceptionv4/GoogLeNetInceptionV4PoolingLayerDataset.h
+++ b/tests/datasets_new/system_tests/googlenet/inceptionv4/GoogLeNetInceptionV4PoolingLayerDataset.h
@@ -48,15 +48,15 @@
// inception_stem3_pool
add_config(TensorShape(71U, 71U, 192U), TensorShape(35U, 35U, 192U), PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)));
// inception_a1_pool_ave, inception_a2_pool_ave, inception_a3_pool_ave, inception_a4_pool_ave
- add_config(TensorShape(35U, 35U, 384U), TensorShape(3U, 3U, 384U), PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL)));
+ add_config(TensorShape(35U, 35U, 384U), TensorShape(35U, 35U, 384U), PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL)));
// reduction_a_pool
add_config(TensorShape(35U, 35U, 384U), TensorShape(17U, 17U, 384U), PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)));
// inception_b1_pool_ave, inception_b2_pool_ave, inception_b3_pool_ave, inception_b4_pool_ave, inception_b5_pool_ave, inception_b6_pool_ave, inception_b7_pool_ave
- add_config(TensorShape(17U, 17U, 1024U), TensorShape(3U, 3U, 1024U), PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL)));
+ add_config(TensorShape(17U, 17U, 1024U), TensorShape(17U, 17U, 1024U), PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL)));
// reduction_b_pool
add_config(TensorShape(17U, 17U, 1024U), TensorShape(8U, 8U, 1024U), PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)));
// inception_c1_pool_ave, inception_c2_pool_ave, inception_c3_pool_ave
- add_config(TensorShape(8U, 8U, 1536U), TensorShape(3U, 3U, 1536U), PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL)));
+ add_config(TensorShape(8U, 8U, 1536U), TensorShape(8U, 8U, 1536U), PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL)));
}
};
} // namespace datasets
diff --git a/tests/networks_new/AlexNetNetwork.h b/tests/networks_new/AlexNetNetwork.h
index b3a7196..11171d6 100644
--- a/tests/networks_new/AlexNetNetwork.h
+++ b/tests/networks_new/AlexNetNetwork.h
@@ -97,10 +97,15 @@
}
else
{
- const unsigned int data_type_size = 16 / arm_compute::data_size_from_type(_data_type);
+ auto reshape = [&](unsigned int width, unsigned int height) -> TensorShape
+ {
+ const int interleave_width = 16 / arm_compute::data_size_from_type(_data_type);
+
+ return TensorShape{ width * interleave_width, static_cast<unsigned int>(std::ceil(static_cast<float>(height) / interleave_width)) };
+ };
// Create tensor for the reshaped weights
- w[0].allocator()->init(TensorInfo(TensorShape(366U * data_type_size, 96U / data_type_size), 1, _data_type, _fixed_point_position));
+ w[0].allocator()->init(TensorInfo(reshape(366U, 96U), 1, _data_type, _fixed_point_position));
// Configure the direct convolution's weights. Direct convolution doesn't need reshape weights
if(!_is_direct_conv)
@@ -111,13 +116,13 @@
auto w42_tensor = std::unique_ptr<TensorType>(new TensorType());
auto w51_tensor = std::unique_ptr<TensorType>(new TensorType());
auto w52_tensor = std::unique_ptr<TensorType>(new TensorType());
- w21_tensor->allocator()->init(TensorInfo(TensorShape(1248U * data_type_size, 128U / data_type_size), 1, _data_type, _fixed_point_position));
- w22_tensor->allocator()->init(TensorInfo(TensorShape(1248U * data_type_size, 128U / data_type_size), 1, _data_type, _fixed_point_position));
- w41_tensor->allocator()->init(TensorInfo(TensorShape(1920U * data_type_size, 192U / data_type_size), 1, _data_type, _fixed_point_position));
- w42_tensor->allocator()->init(TensorInfo(TensorShape(1920U * data_type_size, 192U / data_type_size), 1, _data_type, _fixed_point_position));
- w51_tensor->allocator()->init(TensorInfo(TensorShape(1920U * data_type_size, 128U / data_type_size), 1, _data_type, _fixed_point_position));
- w52_tensor->allocator()->init(TensorInfo(TensorShape(1920U * data_type_size, 128U / data_type_size), 1, _data_type, _fixed_point_position));
- w[2].allocator()->init(TensorInfo(TensorShape(2560U * data_type_size, 384U / data_type_size), 1, _data_type, _fixed_point_position));
+ w21_tensor->allocator()->init(TensorInfo(reshape(1248U, 128U), 1, _data_type, _fixed_point_position));
+ w22_tensor->allocator()->init(TensorInfo(reshape(1248U, 128U), 1, _data_type, _fixed_point_position));
+ w41_tensor->allocator()->init(TensorInfo(reshape(1920U, 192U), 1, _data_type, _fixed_point_position));
+ w42_tensor->allocator()->init(TensorInfo(reshape(1920U, 192U), 1, _data_type, _fixed_point_position));
+ w51_tensor->allocator()->init(TensorInfo(reshape(1920U, 128U), 1, _data_type, _fixed_point_position));
+ w52_tensor->allocator()->init(TensorInfo(reshape(1920U, 128U), 1, _data_type, _fixed_point_position));
+ w[2].allocator()->init(TensorInfo(reshape(2560U, 384U), 1, _data_type, _fixed_point_position));
w21 = std::move(w21_tensor);
w22 = std::move(w22_tensor);
w41 = std::move(w41_tensor);
@@ -157,9 +162,9 @@
if(_batches > 1 && std::is_same<TensorType, Tensor>::value)
{
- w[5].allocator()->init(TensorInfo(TensorShape(9216U * data_type_size, 4096U / data_type_size), 1, _data_type, _fixed_point_position));
- w[6].allocator()->init(TensorInfo(TensorShape(4096U * data_type_size, 4096U / data_type_size), 1, _data_type, _fixed_point_position));
- w[7].allocator()->init(TensorInfo(TensorShape(4096U * data_type_size, 1000U / data_type_size), 1, _data_type, _fixed_point_position));
+ w[5].allocator()->init(TensorInfo(reshape(9216U, 4096U), 1, _data_type, _fixed_point_position));
+ w[6].allocator()->init(TensorInfo(reshape(4096U, 4096U), 1, _data_type, _fixed_point_position));
+ w[7].allocator()->init(TensorInfo(reshape(4096U, 1000U), 1, _data_type, _fixed_point_position));
}
else
{