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Diffstat (limited to 'tests/networks/AlexNetNetwork.h')
-rw-r--r--tests/networks/AlexNetNetwork.h143
1 files changed, 70 insertions, 73 deletions
diff --git a/tests/networks/AlexNetNetwork.h b/tests/networks/AlexNetNetwork.h
index fa3734d106..e92affe954 100644
--- a/tests/networks/AlexNetNetwork.h
+++ b/tests/networks/AlexNetNetwork.h
@@ -56,37 +56,35 @@ class AlexNetNetwork
public:
/** Initialize the network.
*
- * @param[in] data_type Data type.
- * @param[in] fixed_point_position Fixed point position (for quantized data types).
- * @param[in] batches Number of batches.
- * @param[in] reshaped_weights Whether the weights need reshaping or not. Default: false.
+ * @param[in] data_type Data type.
+ * @param[in] batches Number of batches.
+ * @param[in] reshaped_weights Whether the weights need reshaping or not. Default: false.
*/
- void init(DataType data_type, int fixed_point_position, int batches, bool reshaped_weights = false)
+ void init(DataType data_type, int batches, bool reshaped_weights = false)
{
- _data_type = data_type;
- _fixed_point_position = fixed_point_position;
- _batches = batches;
- _reshaped_weights = reshaped_weights;
+ _data_type = data_type;
+ _batches = batches;
+ _reshaped_weights = reshaped_weights;
// Initialize weights and biases
if(!_reshaped_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));
+ w[0].allocator()->init(TensorInfo(TensorShape(11U, 11U, 3U, 96U), 1, _data_type));
+ b[0].allocator()->init(TensorInfo(TensorShape(96U), 1, _data_type));
+ w[1].allocator()->init(TensorInfo(TensorShape(5U, 5U, 48U, 256U), 1, _data_type));
+ b[1].allocator()->init(TensorInfo(TensorShape(256U), 1, _data_type));
+ w[2].allocator()->init(TensorInfo(TensorShape(3U, 3U, 256U, 384U), 1, _data_type));
+ b[2].allocator()->init(TensorInfo(TensorShape(384U), 1, _data_type));
+ w[3].allocator()->init(TensorInfo(TensorShape(3U, 3U, 192U, 384U), 1, _data_type));
+ b[3].allocator()->init(TensorInfo(TensorShape(384U), 1, _data_type));
+ w[4].allocator()->init(TensorInfo(TensorShape(3U, 3U, 192U, 256U), 1, _data_type));
+ b[4].allocator()->init(TensorInfo(TensorShape(256U), 1, _data_type));
+ w[5].allocator()->init(TensorInfo(TensorShape(9216U, 4096U), 1, _data_type));
+ b[5].allocator()->init(TensorInfo(TensorShape(4096U), 1, _data_type));
+ w[6].allocator()->init(TensorInfo(TensorShape(4096U, 4096U), 1, _data_type));
+ b[6].allocator()->init(TensorInfo(TensorShape(4096U), 1, _data_type));
+ w[7].allocator()->init(TensorInfo(TensorShape(4096U, 1000U), 1, _data_type));
+ b[7].allocator()->init(TensorInfo(TensorShape(1000U), 1, _data_type));
w11 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[1], TensorShape(5U, 5U, 48U, 128U), Coordinates()));
w12 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[1], TensorShape(5U, 5U, 48U, 128U), Coordinates(0, 0, 0, 128)));
@@ -122,7 +120,7 @@ public:
};
// Create tensor for the reshaped weights
- w[0].allocator()->init(TensorInfo(reshape(366U, 96U, true), 1, _data_type, _fixed_point_position));
+ w[0].allocator()->init(TensorInfo(reshape(366U, 96U, true), 1, _data_type));
// Configure the direct convolution's weights. Direct convolution doesn't need reshape weights
if(!_is_direct_conv)
@@ -133,13 +131,13 @@ public:
auto w32_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());
- w11_tensor->allocator()->init(TensorInfo(reshape(1248U, 128U, true), 1, _data_type, _fixed_point_position));
- w12_tensor->allocator()->init(TensorInfo(reshape(1248U, 128U, true), 1, _data_type, _fixed_point_position));
- w31_tensor->allocator()->init(TensorInfo(reshape(1920U, 192U, true), 1, _data_type, _fixed_point_position));
- w32_tensor->allocator()->init(TensorInfo(reshape(1920U, 192U, true), 1, _data_type, _fixed_point_position));
- w41_tensor->allocator()->init(TensorInfo(reshape(1920U, 128U, true), 1, _data_type, _fixed_point_position));
- w42_tensor->allocator()->init(TensorInfo(reshape(1920U, 128U, true), 1, _data_type, _fixed_point_position));
- w[2].allocator()->init(TensorInfo(reshape(2560U, 384U, true), 1, _data_type, _fixed_point_position));
+ w11_tensor->allocator()->init(TensorInfo(reshape(1248U, 128U, true), 1, _data_type));
+ w12_tensor->allocator()->init(TensorInfo(reshape(1248U, 128U, true), 1, _data_type));
+ w31_tensor->allocator()->init(TensorInfo(reshape(1920U, 192U, true), 1, _data_type));
+ w32_tensor->allocator()->init(TensorInfo(reshape(1920U, 192U, true), 1, _data_type));
+ w41_tensor->allocator()->init(TensorInfo(reshape(1920U, 128U, true), 1, _data_type));
+ w42_tensor->allocator()->init(TensorInfo(reshape(1920U, 128U, true), 1, _data_type));
+ w[2].allocator()->init(TensorInfo(reshape(2560U, 384U, true), 1, _data_type));
w11 = std::move(w11_tensor);
w12 = std::move(w12_tensor);
w31 = std::move(w31_tensor);
@@ -149,14 +147,14 @@ public:
}
else
{
- 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[1].allocator()->init(TensorInfo(TensorShape(5U, 5U, 48U, 256U), 1, _data_type));
+ b[1].allocator()->init(TensorInfo(TensorShape(256U), 1, _data_type));
+ w[2].allocator()->init(TensorInfo(TensorShape(3U, 3U, 256U, 384U), 1, _data_type));
+ b[2].allocator()->init(TensorInfo(TensorShape(384U), 1, _data_type));
+ w[3].allocator()->init(TensorInfo(TensorShape(3U, 3U, 192U, 384U), 1, _data_type));
+ b[3].allocator()->init(TensorInfo(TensorShape(384U), 1, _data_type));
+ w[4].allocator()->init(TensorInfo(TensorShape(3U, 3U, 192U, 256U), 1, _data_type));
+ b[4].allocator()->init(TensorInfo(TensorShape(256U), 1, _data_type));
w11 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[1], TensorShape(5U, 5U, 48U, 128U), Coordinates()));
w12 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[1], TensorShape(5U, 5U, 48U, 128U), Coordinates(0, 0, 0, 128)));
b11 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[1], TensorShape(128U), Coordinates()));
@@ -173,21 +171,21 @@ public:
b42 = 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));
+ b[5].allocator()->init(TensorInfo(TensorShape(4096U), 1, _data_type));
+ b[6].allocator()->init(TensorInfo(TensorShape(4096U), 1, _data_type));
+ b[7].allocator()->init(TensorInfo(TensorShape(1000U), 1, _data_type));
if(_batches > 1 && std::is_same<TensorType, Tensor>::value)
{
- w[5].allocator()->init(TensorInfo(reshape(9216U, 4096U, false), 1, _data_type, _fixed_point_position));
- w[6].allocator()->init(TensorInfo(reshape(4096U, 4096U, false), 1, _data_type, _fixed_point_position));
- w[7].allocator()->init(TensorInfo(reshape(4096U, 1000U, false), 1, _data_type, _fixed_point_position));
+ w[5].allocator()->init(TensorInfo(reshape(9216U, 4096U, false), 1, _data_type));
+ w[6].allocator()->init(TensorInfo(reshape(4096U, 4096U, false), 1, _data_type));
+ w[7].allocator()->init(TensorInfo(reshape(4096U, 1000U, false), 1, _data_type));
}
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));
+ w[5].allocator()->init(TensorInfo(TensorShape(4096U, 9216U), 1, _data_type));
+ w[6].allocator()->init(TensorInfo(TensorShape(4096U, 4096U), 1, _data_type));
+ w[7].allocator()->init(TensorInfo(TensorShape(1000U, 4096U), 1, _data_type));
}
}
}
@@ -195,50 +193,50 @@ public:
/** Build the network */
void build()
{
- input.allocator()->init(TensorInfo(TensorShape(227U, 227U, 3U, _batches), 1, _data_type, _fixed_point_position));
- output.allocator()->init(TensorInfo(TensorShape(1000U, _batches), 1, _data_type, _fixed_point_position));
+ input.allocator()->init(TensorInfo(TensorShape(227U, 227U, 3U, _batches), 1, _data_type));
+ output.allocator()->init(TensorInfo(TensorShape(1000U, _batches), 1, _data_type));
// Initialize intermediate tensors
// Layer 1
- conv1_out.allocator()->init(TensorInfo(TensorShape(55U, 55U, 96U, _batches), 1, _data_type, _fixed_point_position));
- act1_out.allocator()->init(TensorInfo(TensorShape(55U, 55U, 96U, _batches), 1, _data_type, _fixed_point_position));
- norm1_out.allocator()->init(TensorInfo(TensorShape(55U, 55U, 96U, _batches), 1, _data_type, _fixed_point_position));
- pool1_out.allocator()->init(TensorInfo(TensorShape(27U, 27U, 96U, _batches), 1, _data_type, _fixed_point_position));
+ conv1_out.allocator()->init(TensorInfo(TensorShape(55U, 55U, 96U, _batches), 1, _data_type));
+ act1_out.allocator()->init(TensorInfo(TensorShape(55U, 55U, 96U, _batches), 1, _data_type));
+ norm1_out.allocator()->init(TensorInfo(TensorShape(55U, 55U, 96U, _batches), 1, _data_type));
+ pool1_out.allocator()->init(TensorInfo(TensorShape(27U, 27U, 96U, _batches), 1, _data_type));
pool11_out = std::unique_ptr<SubTensorType>(new SubTensorType(&pool1_out, TensorShape(27U, 27U, 48U, _batches), Coordinates()));
pool12_out = std::unique_ptr<SubTensorType>(new SubTensorType(&pool1_out, TensorShape(27U, 27U, 48U, _batches), Coordinates(0, 0, 48)));
// Layer 2
- conv2_out.allocator()->init(TensorInfo(TensorShape(27U, 27U, 256U, _batches), 1, _data_type, _fixed_point_position));
+ conv2_out.allocator()->init(TensorInfo(TensorShape(27U, 27U, 256U, _batches), 1, _data_type));
conv21_out = std::unique_ptr<SubTensorType>(new SubTensorType(&conv2_out, TensorShape(27U, 27U, 128U, _batches), Coordinates()));
conv22_out = std::unique_ptr<SubTensorType>(new SubTensorType(&conv2_out, TensorShape(27U, 27U, 128U, _batches), Coordinates(0, 0, 128)));
- act2_out.allocator()->init(TensorInfo(TensorShape(27U, 27U, 256U, _batches), 1, _data_type, _fixed_point_position));
- norm2_out.allocator()->init(TensorInfo(TensorShape(27U, 27U, 256U, _batches), 1, _data_type, _fixed_point_position));
- pool2_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 256U, _batches), 1, _data_type, _fixed_point_position));
+ act2_out.allocator()->init(TensorInfo(TensorShape(27U, 27U, 256U, _batches), 1, _data_type));
+ norm2_out.allocator()->init(TensorInfo(TensorShape(27U, 27U, 256U, _batches), 1, _data_type));
+ pool2_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 256U, _batches), 1, _data_type));
// Layer 3
- conv3_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 384U, _batches), 1, _data_type, _fixed_point_position));
- act3_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 384U, _batches), 1, _data_type, _fixed_point_position));
+ conv3_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 384U, _batches), 1, _data_type));
+ act3_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 384U, _batches), 1, _data_type));
act31_out = std::unique_ptr<SubTensorType>(new SubTensorType(&act3_out, TensorShape(13U, 13U, 192U, _batches), Coordinates()));
act32_out = std::unique_ptr<SubTensorType>(new SubTensorType(&act3_out, TensorShape(13U, 13U, 192U, _batches), Coordinates(0, 0, 192)));
// Layer 4
- conv4_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 384U, _batches), 1, _data_type, _fixed_point_position));
+ conv4_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 384U, _batches), 1, _data_type));
conv41_out = std::unique_ptr<SubTensorType>(new SubTensorType(&conv4_out, TensorShape(13U, 13U, 192U, _batches), Coordinates()));
conv42_out = std::unique_ptr<SubTensorType>(new SubTensorType(&conv4_out, TensorShape(13U, 13U, 192U, _batches), Coordinates(0, 0, 192)));
- act4_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 384U, _batches), 1, _data_type, _fixed_point_position));
+ act4_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 384U, _batches), 1, _data_type));
act41_out = std::unique_ptr<SubTensorType>(new SubTensorType(&act4_out, TensorShape(13U, 13U, 192U, _batches), Coordinates()));
act42_out = std::unique_ptr<SubTensorType>(new SubTensorType(&act4_out, TensorShape(13U, 13U, 192U, _batches), Coordinates(0, 0, 192)));
// Layer 5
- conv5_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 256U, _batches), 1, _data_type, _fixed_point_position));
+ conv5_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 256U, _batches), 1, _data_type));
conv51_out = std::unique_ptr<SubTensorType>(new SubTensorType(&conv5_out, TensorShape(13U, 13U, 128U, _batches), Coordinates()));
conv52_out = std::unique_ptr<SubTensorType>(new SubTensorType(&conv5_out, TensorShape(13U, 13U, 128U, _batches), Coordinates(0, 0, 128)));
- act5_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 256U, _batches), 1, _data_type, _fixed_point_position));
- pool5_out.allocator()->init(TensorInfo(TensorShape(6U, 6U, 256U, _batches), 1, _data_type, _fixed_point_position));
+ act5_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 256U, _batches), 1, _data_type));
+ pool5_out.allocator()->init(TensorInfo(TensorShape(6U, 6U, 256U, _batches), 1, _data_type));
// Layer 6
- fc6_out.allocator()->init(TensorInfo(TensorShape(4096U, _batches), 1, _data_type, _fixed_point_position));
- act6_out.allocator()->init(TensorInfo(TensorShape(4096U, _batches), 1, _data_type, _fixed_point_position));
+ fc6_out.allocator()->init(TensorInfo(TensorShape(4096U, _batches), 1, _data_type));
+ act6_out.allocator()->init(TensorInfo(TensorShape(4096U, _batches), 1, _data_type));
// Layer 7
- fc7_out.allocator()->init(TensorInfo(TensorShape(4096U, _batches), 1, _data_type, _fixed_point_position));
- act7_out.allocator()->init(TensorInfo(TensorShape(4096U, _batches), 1, _data_type, _fixed_point_position));
+ fc7_out.allocator()->init(TensorInfo(TensorShape(4096U, _batches), 1, _data_type));
+ act7_out.allocator()->init(TensorInfo(TensorShape(4096U, _batches), 1, _data_type));
// Layer 8
- fc8_out.allocator()->init(TensorInfo(TensorShape(1000U, _batches), 1, _data_type, _fixed_point_position));
+ fc8_out.allocator()->init(TensorInfo(TensorShape(1000U, _batches), 1, _data_type));
// Configure Layers
// Layer 1
@@ -606,7 +604,6 @@ private:
};
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 };
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-23 04:03:53 +0000