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-rw-r--r--examples/graph_resnet12.cpp136
1 files changed, 69 insertions, 67 deletions
diff --git a/examples/graph_resnet12.cpp b/examples/graph_resnet12.cpp
index 48708ce29a..80db826be5 100644
--- a/examples/graph_resnet12.cpp
+++ b/examples/graph_resnet12.cpp
@@ -22,6 +22,7 @@
* SOFTWARE.
*/
#include "arm_compute/graph.h"
+
#include "support/ToolchainSupport.h"
#include "utils/CommonGraphOptions.h"
#include "utils/GraphUtils.h"
@@ -36,7 +37,12 @@ class GraphResNet12Example : public Example
{
public:
GraphResNet12Example()
- : cmd_parser(), common_opts(cmd_parser), model_input_width(nullptr), model_input_height(nullptr), common_params(), graph(0, "ResNet12")
+ : cmd_parser(),
+ common_opts(cmd_parser),
+ model_input_width(nullptr),
+ model_input_height(nullptr),
+ common_params(),
+ graph(0, "ResNet12")
{
model_input_width = cmd_parser.add_option<SimpleOption<unsigned int>>("image-width", 192);
model_input_height = cmd_parser.add_option<SimpleOption<unsigned int>>("image-height", 128);
@@ -45,7 +51,7 @@ public:
model_input_width->set_help("Input image width.");
model_input_height->set_help("Input image height.");
}
- GraphResNet12Example(const GraphResNet12Example &) = delete;
+ GraphResNet12Example(const GraphResNet12Example &) = delete;
GraphResNet12Example &operator=(const GraphResNet12Example &) = delete;
~GraphResNet12Example() override = default;
bool do_setup(int argc, char **argv) override
@@ -58,7 +64,7 @@ public:
common_params = consume_common_graph_parameters(common_opts);
// Return when help menu is requested
- if(common_params.help)
+ if (common_params.help)
{
cmd_parser.print_help(argv[0]);
return false;
@@ -69,7 +75,8 @@ public:
const unsigned int image_height = model_input_height->value();
// Checks
- ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "QASYMM8 not supported for this graph");
+ ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type),
+ "QASYMM8 not supported for this graph");
// Print parameter values
std::cout << common_params << std::endl;
@@ -84,50 +91,47 @@ public:
std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<TFPreproccessor>();
// Create input descriptor
- const TensorShape tensor_shape = permute_shape(TensorShape(image_width, image_height, 3U, common_params.batches), DataLayout::NCHW, common_params.data_layout);
- TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
+ const TensorShape tensor_shape =
+ permute_shape(TensorShape(image_width, image_height, 3U, common_params.batches), DataLayout::NCHW,
+ common_params.data_layout);
+ TensorDescriptor input_descriptor =
+ TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
// Set weights trained layout
const DataLayout weights_layout = DataLayout::NCHW;
- graph << common_params.target
- << common_params.fast_math_hint
- << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor), false /* Do not convert to BGR */))
- << ConvolutionLayer(
- 9U, 9U, 64U,
- get_weights_accessor(data_path, "conv1_weights.npy", weights_layout),
- get_weights_accessor(data_path, "conv1_biases.npy", weights_layout),
- PadStrideInfo(1, 1, 4, 4))
- .set_name("conv1/convolution")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv1/Relu");
+ graph << common_params.target << common_params.fast_math_hint
+ << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor),
+ false /* Do not convert to BGR */))
+ << ConvolutionLayer(9U, 9U, 64U, get_weights_accessor(data_path, "conv1_weights.npy", weights_layout),
+ get_weights_accessor(data_path, "conv1_biases.npy", weights_layout),
+ PadStrideInfo(1, 1, 4, 4))
+ .set_name("conv1/convolution")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("conv1/Relu");
add_residual_block(data_path, "block1", weights_layout);
add_residual_block(data_path, "block2", weights_layout);
add_residual_block(data_path, "block3", weights_layout);
add_residual_block(data_path, "block4", weights_layout);
- graph << ConvolutionLayer(
- 3U, 3U, 64U,
- get_weights_accessor(data_path, "conv10_weights.npy", weights_layout),
- get_weights_accessor(data_path, "conv10_biases.npy"),
- PadStrideInfo(1, 1, 1, 1))
- .set_name("conv10/convolution")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv10/Relu")
- << ConvolutionLayer(
- 3U, 3U, 64U,
- get_weights_accessor(data_path, "conv11_weights.npy", weights_layout),
- get_weights_accessor(data_path, "conv11_biases.npy"),
- PadStrideInfo(1, 1, 1, 1))
- .set_name("conv11/convolution")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv11/Relu")
- << ConvolutionLayer(
- 9U, 9U, 3U,
- get_weights_accessor(data_path, "conv12_weights.npy", weights_layout),
- get_weights_accessor(data_path, "conv12_biases.npy"),
- PadStrideInfo(1, 1, 4, 4))
- .set_name("conv12/convolution")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH)).set_name("conv12/Tanh")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 0.58f, 0.5f)).set_name("conv12/Linear")
+ graph << ConvolutionLayer(3U, 3U, 64U, get_weights_accessor(data_path, "conv10_weights.npy", weights_layout),
+ get_weights_accessor(data_path, "conv10_biases.npy"), PadStrideInfo(1, 1, 1, 1))
+ .set_name("conv10/convolution")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("conv10/Relu")
+ << ConvolutionLayer(3U, 3U, 64U, get_weights_accessor(data_path, "conv11_weights.npy", weights_layout),
+ get_weights_accessor(data_path, "conv11_biases.npy"), PadStrideInfo(1, 1, 1, 1))
+ .set_name("conv11/convolution")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("conv11/Relu")
+ << ConvolutionLayer(9U, 9U, 3U, get_weights_accessor(data_path, "conv12_weights.npy", weights_layout),
+ get_weights_accessor(data_path, "conv12_biases.npy"), PadStrideInfo(1, 1, 4, 4))
+ .set_name("conv12/convolution")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH))
+ .set_name("conv12/Tanh")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 0.58f, 0.5f))
+ .set_name("conv12/Linear")
<< OutputLayer(std::make_unique<DummyAccessor>(0));
// Finalize graph
@@ -152,8 +156,8 @@ public:
private:
CommandLineParser cmd_parser;
CommonGraphOptions common_opts;
- SimpleOption<unsigned int> *model_input_width{ nullptr };
- SimpleOption<unsigned int> *model_input_height{ nullptr };
+ SimpleOption<unsigned int> *model_input_width{nullptr};
+ SimpleOption<unsigned int> *model_input_height{nullptr};
CommonGraphParams common_params;
Stream graph;
@@ -170,35 +174,33 @@ private:
SubStream left(graph);
SubStream right(graph);
- right << ConvolutionLayer(
- 3U, 3U, 64U,
- get_weights_accessor(data_path, unit_path + "conv1_weights.npy", weights_layout),
- get_weights_accessor(data_path, unit_path + "conv1_biases.npy", weights_layout),
- PadStrideInfo(1, 1, 1, 1))
- .set_name(unit_name + "conv1/convolution")
+ right << ConvolutionLayer(3U, 3U, 64U,
+ get_weights_accessor(data_path, unit_path + "conv1_weights.npy", weights_layout),
+ get_weights_accessor(data_path, unit_path + "conv1_biases.npy", weights_layout),
+ PadStrideInfo(1, 1, 1, 1))
+ .set_name(unit_name + "conv1/convolution")
<< BatchNormalizationLayer(
- get_weights_accessor(data_path, unit_path + "conv1_BatchNorm_moving_mean.npy"),
- get_weights_accessor(data_path, unit_path + "conv1_BatchNorm_moving_variance.npy"),
- get_weights_accessor(data_path, unit_path + "conv1_BatchNorm_gamma.npy"),
- get_weights_accessor(data_path, unit_path + "conv1_BatchNorm_beta.npy"),
- 0.0000100099996416f)
- .set_name(unit_name + "conv1/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "conv1/Relu")
-
- << ConvolutionLayer(
- 3U, 3U, 64U,
- get_weights_accessor(data_path, unit_path + "conv2_weights.npy", weights_layout),
- get_weights_accessor(data_path, unit_path + "conv2_biases.npy", weights_layout),
- PadStrideInfo(1, 1, 1, 1))
- .set_name(unit_name + "conv2/convolution")
+ get_weights_accessor(data_path, unit_path + "conv1_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, unit_path + "conv1_BatchNorm_moving_variance.npy"),
+ get_weights_accessor(data_path, unit_path + "conv1_BatchNorm_gamma.npy"),
+ get_weights_accessor(data_path, unit_path + "conv1_BatchNorm_beta.npy"), 0.0000100099996416f)
+ .set_name(unit_name + "conv1/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name(unit_name + "conv1/Relu")
+
+ << ConvolutionLayer(3U, 3U, 64U,
+ get_weights_accessor(data_path, unit_path + "conv2_weights.npy", weights_layout),
+ get_weights_accessor(data_path, unit_path + "conv2_biases.npy", weights_layout),
+ PadStrideInfo(1, 1, 1, 1))
+ .set_name(unit_name + "conv2/convolution")
<< BatchNormalizationLayer(
- get_weights_accessor(data_path, unit_path + "conv2_BatchNorm_moving_mean.npy"),
- get_weights_accessor(data_path, unit_path + "conv2_BatchNorm_moving_variance.npy"),
- get_weights_accessor(data_path, unit_path + "conv2_BatchNorm_gamma.npy"),
- get_weights_accessor(data_path, unit_path + "conv2_BatchNorm_beta.npy"),
- 0.0000100099996416f)
- .set_name(unit_name + "conv2/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "conv2/Relu");
+ get_weights_accessor(data_path, unit_path + "conv2_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, unit_path + "conv2_BatchNorm_moving_variance.npy"),
+ get_weights_accessor(data_path, unit_path + "conv2_BatchNorm_gamma.npy"),
+ get_weights_accessor(data_path, unit_path + "conv2_BatchNorm_beta.npy"), 0.0000100099996416f)
+ .set_name(unit_name + "conv2/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name(unit_name + "conv2/Relu");
graph << EltwiseLayer(std::move(left), std::move(right), EltwiseOperation::Add).set_name(unit_name + "add");
}
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-03 16:37:29 +0000