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Diffstat (limited to 'tests/networks_new/AlexNetNetwork.h')
-rw-r--r--tests/networks_new/AlexNetNetwork.h242
1 files changed, 156 insertions, 86 deletions
diff --git a/tests/networks_new/AlexNetNetwork.h b/tests/networks_new/AlexNetNetwork.h
index 7e1a855f07..8c801f70d3 100644
--- a/tests/networks_new/AlexNetNetwork.h
+++ b/tests/networks_new/AlexNetNetwork.h
@@ -43,6 +43,7 @@ template <typename ITensorType,
typename Accessor,
typename ActivationLayerFunction,
typename ConvolutionLayerFunction,
+ typename DirectConvolutionLayerFunction,
typename FullyConnectedLayerFunction,
typename NormalizationLayerFunction,
typename PoolingLayerFunction,
@@ -60,11 +61,104 @@ public:
// Initialize weights and biases
if(!_reshaped_weights)
{
- init_weights();
+ w[0].allocator()->init(TensorInfo(TensorShape(11U, 11U, 3U, 96U), 1, _data_type, _fixed_point_position));
+ b[0].allocator()->init(TensorInfo(TensorShape(96U), 1, _data_type, _fixed_point_position));
+ w[1].allocator()->init(TensorInfo(TensorShape(5U, 5U, 48U, 256U), 1, _data_type, _fixed_point_position));
+ b[1].allocator()->init(TensorInfo(TensorShape(256U), 1, _data_type, _fixed_point_position));
+ w[2].allocator()->init(TensorInfo(TensorShape(3U, 3U, 256U, 384U), 1, _data_type, _fixed_point_position));
+ b[2].allocator()->init(TensorInfo(TensorShape(384U), 1, _data_type, _fixed_point_position));
+ w[3].allocator()->init(TensorInfo(TensorShape(3U, 3U, 192U, 384U), 1, _data_type, _fixed_point_position));
+ b[3].allocator()->init(TensorInfo(TensorShape(384U), 1, _data_type, _fixed_point_position));
+ w[4].allocator()->init(TensorInfo(TensorShape(3U, 3U, 192U, 256U), 1, _data_type, _fixed_point_position));
+ b[4].allocator()->init(TensorInfo(TensorShape(256U), 1, _data_type, _fixed_point_position));
+ w[5].allocator()->init(TensorInfo(TensorShape(9216U, 4096U), 1, _data_type, _fixed_point_position));
+ b[5].allocator()->init(TensorInfo(TensorShape(4096U), 1, _data_type, _fixed_point_position));
+ w[6].allocator()->init(TensorInfo(TensorShape(4096U, 4096U), 1, _data_type, _fixed_point_position));
+ b[6].allocator()->init(TensorInfo(TensorShape(4096U), 1, _data_type, _fixed_point_position));
+ w[7].allocator()->init(TensorInfo(TensorShape(4096U, 1000U), 1, _data_type, _fixed_point_position));
+ b[7].allocator()->init(TensorInfo(TensorShape(1000U), 1, _data_type, _fixed_point_position));
+
+ w21 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[1], TensorShape(5U, 5U, 48U, 128U), Coordinates()));
+ w22 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[1], TensorShape(5U, 5U, 48U, 128U), Coordinates(0, 0, 0, 128)));
+ b21 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[1], TensorShape(128U), Coordinates()));
+ b22 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[1], TensorShape(128U), Coordinates(128)));
+
+ w41 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[3], TensorShape(3U, 3U, 192U, 192U), Coordinates()));
+ w42 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[3], TensorShape(3U, 3U, 192U, 192U), Coordinates(0, 0, 0, 192)));
+ b41 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[3], TensorShape(192U), Coordinates()));
+ b42 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[3], TensorShape(192U), Coordinates(192)));
+
+ w51 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[4], TensorShape(3U, 3U, 192U, 128U), Coordinates()));
+ w52 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[4], TensorShape(3U, 3U, 192U, 128U), Coordinates(0, 0, 0, 128)));
+ b51 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[4], TensorShape(128U), Coordinates()));
+ b52 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[4], TensorShape(128U), Coordinates(128)));
}
else
{
- init_reshaped_weights();
+ const unsigned int data_type_size = 16 / arm_compute::data_size_from_type(_data_type);
+
+ // Create tensor for the reshaped weights
+ auto w21_tensor = std::unique_ptr<TensorType>(new TensorType());
+ auto w22_tensor = std::unique_ptr<TensorType>(new TensorType());
+
+ w[0].allocator()->init(TensorInfo(TensorShape(366U * data_type_size, 96U / data_type_size), 1, _data_type, _fixed_point_position));
+ 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));
+ w21 = std::move(w21_tensor);
+ w22 = std::move(w22_tensor);
+
+ // Configure the direct convolution's weights. Direct convolution doesn't need reshape weights
+ if(!_is_direct_conv)
+ {
+ auto w41_tensor = std::unique_ptr<TensorType>(new TensorType());
+ 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());
+ 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));
+ w41 = std::move(w41_tensor);
+ w42 = std::move(w42_tensor);
+ w51 = std::move(w51_tensor);
+ w52 = std::move(w52_tensor);
+ }
+ else
+ {
+ w[2].allocator()->init(TensorInfo(TensorShape(3U, 3U, 256U, 384U), 1, _data_type, _fixed_point_position));
+ b[2].allocator()->init(TensorInfo(TensorShape(384U), 1, _data_type, _fixed_point_position));
+ w[3].allocator()->init(TensorInfo(TensorShape(3U, 3U, 192U, 384U), 1, _data_type, _fixed_point_position));
+ b[3].allocator()->init(TensorInfo(TensorShape(384U), 1, _data_type, _fixed_point_position));
+ w[4].allocator()->init(TensorInfo(TensorShape(3U, 3U, 192U, 256U), 1, _data_type, _fixed_point_position));
+ b[4].allocator()->init(TensorInfo(TensorShape(256U), 1, _data_type, _fixed_point_position));
+ w41 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[3], TensorShape(3U, 3U, 192U, 192U), Coordinates()));
+ w42 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[3], TensorShape(3U, 3U, 192U, 192U), Coordinates(0, 0, 0, 192)));
+ b41 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[3], TensorShape(192U), Coordinates()));
+ b42 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[3], TensorShape(192U), Coordinates(192)));
+
+ w51 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[4], TensorShape(3U, 3U, 192U, 128U), Coordinates()));
+ w52 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[4], TensorShape(3U, 3U, 192U, 128U), Coordinates(0, 0, 0, 128)));
+ b51 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[4], TensorShape(128U), Coordinates()));
+ b52 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[4], TensorShape(128U), Coordinates(128)));
+ }
+
+ b[5].allocator()->init(TensorInfo(TensorShape(4096U), 1, _data_type, _fixed_point_position));
+ b[6].allocator()->init(TensorInfo(TensorShape(4096U), 1, _data_type, _fixed_point_position));
+ b[7].allocator()->init(TensorInfo(TensorShape(1000U), 1, _data_type, _fixed_point_position));
+
+ if(_batches > 1)
+ {
+ 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));
+ }
+ else
+ {
+ w[5].allocator()->init(TensorInfo(TensorShape(4096U, 9216U), 1, _data_type, _fixed_point_position));
+ w[6].allocator()->init(TensorInfo(TensorShape(4096U, 4096U), 1, _data_type, _fixed_point_position));
+ w[7].allocator()->init(TensorInfo(TensorShape(1000U, 4096U), 1, _data_type, _fixed_point_position));
+ }
}
}
@@ -129,7 +223,7 @@ public:
norm2.configure(&act2_out, &norm2_out, NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f));
pool2.configure(&norm2_out, &pool2_out, PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)));
// Layer 3
- TensorType *b2 = _reshaped_weights ? nullptr : &b[2];
+ TensorType *b2 = (_reshaped_weights && !_is_direct_conv) ? nullptr : &b[2];
conv3.configure(&pool2_out, &w[2], b2, &conv3_out, PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U, 3U));
act3.configure(&conv3_out, &act3_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
// Layer 4
@@ -184,10 +278,21 @@ public:
dynamic_cast<TensorType *>(w21.get())->allocator()->allocate();
dynamic_cast<TensorType *>(w22.get())->allocator()->allocate();
- dynamic_cast<TensorType *>(w41.get())->allocator()->allocate();
- dynamic_cast<TensorType *>(w42.get())->allocator()->allocate();
- dynamic_cast<TensorType *>(w51.get())->allocator()->allocate();
- dynamic_cast<TensorType *>(w52.get())->allocator()->allocate();
+ if(!_is_direct_conv)
+ {
+ dynamic_cast<TensorType *>(w41.get())->allocator()->allocate();
+ dynamic_cast<TensorType *>(w42.get())->allocator()->allocate();
+ dynamic_cast<TensorType *>(w51.get())->allocator()->allocate();
+ dynamic_cast<TensorType *>(w52.get())->allocator()->allocate();
+ }
+ else
+ {
+ b[2].allocator()->allocate();
+ b[3].allocator()->allocate();
+ b[4].allocator()->allocate();
+ w[3].allocator()->allocate();
+ w[4].allocator()->allocate();
+ }
}
conv1_out.allocator()->allocate();
@@ -239,10 +344,21 @@ public:
library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w21.get())), 9);
library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w22.get())), 10);
- library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w41.get())), 11);
- library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w42.get())), 12);
- library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w51.get())), 13);
- library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w52.get())), 14);
+
+ if(!_is_direct_conv)
+ {
+ library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w41.get())), 11);
+ library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w42.get())), 12);
+ library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w51.get())), 13);
+ library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w52.get())), 14);
+ }
+ else
+ {
+ library->fill_tensor_uniform(Accessor(w[3]), 11);
+ library->fill_tensor_uniform(Accessor(b[3]), 12);
+ library->fill_tensor_uniform(Accessor(w[4]), 13);
+ library->fill_tensor_uniform(Accessor(b[4]), 14);
+ }
}
}
@@ -340,6 +456,15 @@ public:
b[5].allocator()->free();
b[6].allocator()->free();
b[7].allocator()->free();
+
+ if(_is_direct_conv)
+ {
+ w[3].allocator()->free();
+ w[4].allocator()->free();
+ b[2].allocator()->free();
+ b[3].allocator()->free();
+ b[4].allocator()->free();
+ }
}
w21.reset();
@@ -416,94 +541,39 @@ public:
}
private:
- void init_weights()
+ struct DirectConv
{
- w[0].allocator()->init(TensorInfo(TensorShape(11U, 11U, 3U, 96U), 1, _data_type, _fixed_point_position));
- b[0].allocator()->init(TensorInfo(TensorShape(96U), 1, _data_type, _fixed_point_position));
- w[1].allocator()->init(TensorInfo(TensorShape(5U, 5U, 48U, 256U), 1, _data_type, _fixed_point_position));
- b[1].allocator()->init(TensorInfo(TensorShape(256U), 1, _data_type, _fixed_point_position));
- w[2].allocator()->init(TensorInfo(TensorShape(3U, 3U, 256U, 384U), 1, _data_type, _fixed_point_position));
- b[2].allocator()->init(TensorInfo(TensorShape(384U), 1, _data_type, _fixed_point_position));
- w[3].allocator()->init(TensorInfo(TensorShape(3U, 3U, 192U, 384U), 1, _data_type, _fixed_point_position));
- b[3].allocator()->init(TensorInfo(TensorShape(384U), 1, _data_type, _fixed_point_position));
- w[4].allocator()->init(TensorInfo(TensorShape(3U, 3U, 192U, 256U), 1, _data_type, _fixed_point_position));
- b[4].allocator()->init(TensorInfo(TensorShape(256U), 1, _data_type, _fixed_point_position));
- w[5].allocator()->init(TensorInfo(TensorShape(9216U, 4096U), 1, _data_type, _fixed_point_position));
- b[5].allocator()->init(TensorInfo(TensorShape(4096U), 1, _data_type, _fixed_point_position));
- w[6].allocator()->init(TensorInfo(TensorShape(4096U, 4096U), 1, _data_type, _fixed_point_position));
- b[6].allocator()->init(TensorInfo(TensorShape(4096U), 1, _data_type, _fixed_point_position));
- w[7].allocator()->init(TensorInfo(TensorShape(4096U, 1000U), 1, _data_type, _fixed_point_position));
- b[7].allocator()->init(TensorInfo(TensorShape(1000U), 1, _data_type, _fixed_point_position));
-
- w21 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[1], TensorShape(5U, 5U, 48U, 128U), Coordinates()));
- w22 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[1], TensorShape(5U, 5U, 48U, 128U), Coordinates(0, 0, 0, 128)));
- b21 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[1], TensorShape(128U), Coordinates()));
- b22 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[1], TensorShape(128U), Coordinates(128)));
-
- w41 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[3], TensorShape(3U, 3U, 192U, 192U), Coordinates()));
- w42 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[3], TensorShape(3U, 3U, 192U, 192U), Coordinates(0, 0, 0, 192)));
- b41 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[3], TensorShape(192U), Coordinates()));
- b42 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[3], TensorShape(192U), Coordinates(192)));
-
- w51 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[4], TensorShape(3U, 3U, 192U, 128U), Coordinates()));
- w52 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[4], TensorShape(3U, 3U, 192U, 128U), Coordinates(0, 0, 0, 128)));
- b51 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[4], TensorShape(128U), Coordinates()));
- b52 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[4], TensorShape(128U), Coordinates(128)));
- }
+ template <typename ConvolutionLayerFunction1 = ConvolutionLayerFunction, typename DirectConvolutionLayerFunction1 = DirectConvolutionLayerFunction>
+ typename std::enable_if < !std::is_same<ConvolutionLayerFunction1, DirectConvolutionLayerFunction1>::value, void >::type
+ configure(ITensorType *input, const ITensorType *weights, const ITensorType *biases, ITensorType *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info = WeightsInfo())
+ {
+ _func.configure(input, weights, biases, output, conv_info);
+ }
- void init_reshaped_weights()
- {
- const unsigned int data_type_size = 16 / arm_compute::data_size_from_type(_data_type);
-
- // Create tensor for the reshaped weights
- auto w21_tensor = std::unique_ptr<TensorType>(new TensorType());
- auto w22_tensor = std::unique_ptr<TensorType>(new TensorType());
- auto w41_tensor = std::unique_ptr<TensorType>(new TensorType());
- 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());
-
- w[0].allocator()->init(TensorInfo(TensorShape(366U * data_type_size, 96U / data_type_size), 1, _data_type, _fixed_point_position));
- 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));
- w[2].allocator()->init(TensorInfo(TensorShape(2560U * data_type_size, 384U / 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));
-
- w21 = std::move(w21_tensor);
- w22 = std::move(w22_tensor);
- w41 = std::move(w41_tensor);
- w42 = std::move(w42_tensor);
- w51 = std::move(w51_tensor);
- w52 = std::move(w52_tensor);
-
- b[5].allocator()->init(TensorInfo(TensorShape(4096U), 1, _data_type, _fixed_point_position));
- b[6].allocator()->init(TensorInfo(TensorShape(4096U), 1, _data_type, _fixed_point_position));
- b[7].allocator()->init(TensorInfo(TensorShape(1000U), 1, _data_type, _fixed_point_position));
-
- if(_batches > 1)
+ template <typename ConvolutionLayerFunction1 = ConvolutionLayerFunction, typename DirectConvolutionLayerFunction1 = DirectConvolutionLayerFunction>
+ typename std::enable_if<std::is_same<ConvolutionLayerFunction1, DirectConvolutionLayerFunction1>::value, void>::type
+ configure(ITensorType *input, const ITensorType *weights, const ITensorType *biases, ITensorType *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info = WeightsInfo())
{
- 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));
+ _func.configure(input, weights, biases, output, conv_info, weights_info);
}
- else
+
+ void run()
{
- w[5].allocator()->init(TensorInfo(TensorShape(4096U, 9216U), 1, _data_type, _fixed_point_position));
- w[6].allocator()->init(TensorInfo(TensorShape(4096U, 4096U), 1, _data_type, _fixed_point_position));
- w[7].allocator()->init(TensorInfo(TensorShape(1000U, 4096U), 1, _data_type, _fixed_point_position));
+ _func.run();
}
- }
+
+ DirectConvolutionLayerFunction _func{};
+ };
DataType _data_type{ DataType::UNKNOWN };
int _fixed_point_position{ 0 };
unsigned int _batches{ 0 };
bool _reshaped_weights{ false };
+ bool _is_direct_conv{ !std::is_same<ConvolutionLayerFunction, DirectConvolutionLayerFunction>::value };
ActivationLayerFunction act1{}, act2{}, act3{}, act4{}, act5{}, act6{}, act7{};
- ConvolutionLayerFunction conv1{}, conv21{}, conv22{}, conv3{}, conv41{}, conv42{}, conv51{}, conv52{};
+ ConvolutionLayerFunction conv1{}, conv21{}, conv22{};
+ DirectConv conv3{}, conv41{}, conv42{}, conv51{}, conv52{};
FullyConnectedLayerFunction fc6{}, fc7{}, fc8{};
NormalizationLayerFunction norm1{}, norm2{};
PoolingLayerFunction pool1{}, pool2{}, pool5{};
generated by cgit v1.2.1 (git 2.23.0) at 2024-07-01 21:38:01 +0000