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-rw-r--r--examples/graph_vgg19.cpp295
1 files changed, 141 insertions, 154 deletions
diff --git a/examples/graph_vgg19.cpp b/examples/graph_vgg19.cpp
index efc0bcce19..9293544655 100644
--- a/examples/graph_vgg19.cpp
+++ b/examples/graph_vgg19.cpp
@@ -22,6 +22,7 @@
* SOFTWARE.
*/
#include "arm_compute/graph.h"
+
#include "support/ToolchainSupport.h"
#include "utils/CommonGraphOptions.h"
#include "utils/GraphUtils.h"
@@ -34,8 +35,7 @@ using namespace arm_compute::graph_utils;
class GraphVGG19Example : public Example
{
public:
- GraphVGG19Example()
- : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "VGG19")
+ GraphVGG19Example() : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "VGG19")
{
}
bool do_setup(int argc, char **argv) override
@@ -48,7 +48,7 @@ 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;
@@ -61,165 +61,152 @@ public:
std::string data_path = common_params.data_path;
// Create a preprocessor object
- const std::array<float, 3> mean_rgb{ { 123.68f, 116.779f, 103.939f } };
+ const std::array<float, 3> mean_rgb{{123.68f, 116.779f, 103.939f}};
std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<CaffePreproccessor>(mean_rgb);
// Create input descriptor
const auto operation_layout = common_params.data_layout;
- const TensorShape tensor_shape = permute_shape(TensorShape(224U, 224U, 3U, common_params.batches), DataLayout::NCHW, operation_layout);
- TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout);
+ const TensorShape tensor_shape =
+ permute_shape(TensorShape(224U, 224U, 3U, common_params.batches), DataLayout::NCHW, operation_layout);
+ TensorDescriptor input_descriptor =
+ TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_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)))
- // Layer 1
- << ConvolutionLayer(
- 3U, 3U, 64U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_1_w.npy", weights_layout),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_1_b.npy"),
- PadStrideInfo(1, 1, 1, 1))
- .set_name("conv1_1")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv1_1/Relu")
- << ConvolutionLayer(
- 3U, 3U, 64U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_2_w.npy", weights_layout),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_2_b.npy"),
- PadStrideInfo(1, 1, 1, 1))
- .set_name("conv1_2")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv1_2/Relu")
- << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool1")
- // Layer 2
- << ConvolutionLayer(
- 3U, 3U, 128U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_1_w.npy", weights_layout),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_1_b.npy"),
- PadStrideInfo(1, 1, 1, 1))
- .set_name("conv2_1")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv2_1/Relu")
- << ConvolutionLayer(
- 3U, 3U, 128U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_2_w.npy", weights_layout),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_2_b.npy"),
- PadStrideInfo(1, 1, 1, 1))
- .set_name("conv2_2")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv2_2/Relu")
- << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool2")
- // Layer 3
- << ConvolutionLayer(
- 3U, 3U, 256U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_1_w.npy", weights_layout),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_1_b.npy"),
- PadStrideInfo(1, 1, 1, 1))
- .set_name("conv3_1")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv3_1/Relu")
- << ConvolutionLayer(
- 3U, 3U, 256U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_2_w.npy", weights_layout),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_2_b.npy"),
- PadStrideInfo(1, 1, 1, 1))
- .set_name("conv3_2")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv3_2/Relu")
- << ConvolutionLayer(
- 3U, 3U, 256U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_3_w.npy", weights_layout),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_3_b.npy"),
- PadStrideInfo(1, 1, 1, 1))
- .set_name("conv3_3")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv3_3/Relu")
- << ConvolutionLayer(
- 3U, 3U, 256U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_4_w.npy", weights_layout),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_4_b.npy"),
- PadStrideInfo(1, 1, 1, 1))
- .set_name("conv3_4")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv3_4/Relu")
- << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool3")
- // Layer 4
- << ConvolutionLayer(
- 3U, 3U, 512U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_1_w.npy", weights_layout),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_1_b.npy"),
- PadStrideInfo(1, 1, 1, 1))
- .set_name("conv4_1")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv4_1/Relu")
- << ConvolutionLayer(
- 3U, 3U, 512U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_2_w.npy", weights_layout),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_2_b.npy"),
- PadStrideInfo(1, 1, 1, 1))
- .set_name("conv4_2")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv4_2/Relu")
- << ConvolutionLayer(
- 3U, 3U, 512U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_3_w.npy", weights_layout),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_3_b.npy"),
- PadStrideInfo(1, 1, 1, 1))
- .set_name("conv4_3")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv4_3/Relu")
- << ConvolutionLayer(
- 3U, 3U, 512U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_4_w.npy", weights_layout),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_4_b.npy"),
- PadStrideInfo(1, 1, 1, 1))
- .set_name("conv4_4")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv4_4/Relu")
- << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool4")
- // Layer 5
- << ConvolutionLayer(
- 3U, 3U, 512U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_1_w.npy", weights_layout),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_1_b.npy"),
- PadStrideInfo(1, 1, 1, 1))
- .set_name("conv5_1")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv5_1/Relu")
- << ConvolutionLayer(
- 3U, 3U, 512U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_2_w.npy", weights_layout),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_2_b.npy"),
- PadStrideInfo(1, 1, 1, 1))
- .set_name("conv5_2")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv5_2/Relu")
- << ConvolutionLayer(
- 3U, 3U, 512U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_3_w.npy", weights_layout),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_3_b.npy"),
- PadStrideInfo(1, 1, 1, 1))
- .set_name("conv5_3")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv5_3/Relu")
- << ConvolutionLayer(
- 3U, 3U, 512U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_4_w.npy", weights_layout),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_4_b.npy"),
- PadStrideInfo(1, 1, 1, 1))
- .set_name("conv5_4")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv5_4/Relu")
- << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool5")
- // Layer 6
- << FullyConnectedLayer(
- 4096U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc6_w.npy", weights_layout),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc6_b.npy"))
- .set_name("fc6")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Relu")
- // Layer 7
- << FullyConnectedLayer(
- 4096U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc7_w.npy", weights_layout),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc7_b.npy"))
- .set_name("fc7")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Relu_1")
- // Layer 8
- << FullyConnectedLayer(
- 1000U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc8_w.npy", weights_layout),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc8_b.npy"))
- .set_name("fc8")
- // Softmax
- << SoftmaxLayer().set_name("prob")
- << OutputLayer(get_output_accessor(common_params, 5));
+ graph
+ << common_params.target << common_params.fast_math_hint
+ << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor)))
+ // Layer 1
+ << ConvolutionLayer(
+ 3U, 3U, 64U, get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_1_w.npy", weights_layout),
+ get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_1_b.npy"), PadStrideInfo(1, 1, 1, 1))
+ .set_name("conv1_1")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("conv1_1/Relu")
+ << ConvolutionLayer(
+ 3U, 3U, 64U, get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_2_w.npy", weights_layout),
+ get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_2_b.npy"), PadStrideInfo(1, 1, 1, 1))
+ .set_name("conv1_2")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("conv1_2/Relu")
+ << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0)))
+ .set_name("pool1")
+ // Layer 2
+ << ConvolutionLayer(
+ 3U, 3U, 128U, get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_1_w.npy", weights_layout),
+ get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_1_b.npy"), PadStrideInfo(1, 1, 1, 1))
+ .set_name("conv2_1")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("conv2_1/Relu")
+ << ConvolutionLayer(
+ 3U, 3U, 128U, get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_2_w.npy", weights_layout),
+ get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_2_b.npy"), PadStrideInfo(1, 1, 1, 1))
+ .set_name("conv2_2")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("conv2_2/Relu")
+ << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0)))
+ .set_name("pool2")
+ // Layer 3
+ << ConvolutionLayer(
+ 3U, 3U, 256U, get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_1_w.npy", weights_layout),
+ get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_1_b.npy"), PadStrideInfo(1, 1, 1, 1))
+ .set_name("conv3_1")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("conv3_1/Relu")
+ << ConvolutionLayer(
+ 3U, 3U, 256U, get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_2_w.npy", weights_layout),
+ get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_2_b.npy"), PadStrideInfo(1, 1, 1, 1))
+ .set_name("conv3_2")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("conv3_2/Relu")
+ << ConvolutionLayer(
+ 3U, 3U, 256U, get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_3_w.npy", weights_layout),
+ get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_3_b.npy"), PadStrideInfo(1, 1, 1, 1))
+ .set_name("conv3_3")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("conv3_3/Relu")
+ << ConvolutionLayer(
+ 3U, 3U, 256U, get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_4_w.npy", weights_layout),
+ get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_4_b.npy"), PadStrideInfo(1, 1, 1, 1))
+ .set_name("conv3_4")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("conv3_4/Relu")
+ << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0)))
+ .set_name("pool3")
+ // Layer 4
+ << ConvolutionLayer(
+ 3U, 3U, 512U, get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_1_w.npy", weights_layout),
+ get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_1_b.npy"), PadStrideInfo(1, 1, 1, 1))
+ .set_name("conv4_1")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("conv4_1/Relu")
+ << ConvolutionLayer(
+ 3U, 3U, 512U, get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_2_w.npy", weights_layout),
+ get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_2_b.npy"), PadStrideInfo(1, 1, 1, 1))
+ .set_name("conv4_2")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("conv4_2/Relu")
+ << ConvolutionLayer(
+ 3U, 3U, 512U, get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_3_w.npy", weights_layout),
+ get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_3_b.npy"), PadStrideInfo(1, 1, 1, 1))
+ .set_name("conv4_3")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("conv4_3/Relu")
+ << ConvolutionLayer(
+ 3U, 3U, 512U, get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_4_w.npy", weights_layout),
+ get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_4_b.npy"), PadStrideInfo(1, 1, 1, 1))
+ .set_name("conv4_4")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("conv4_4/Relu")
+ << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0)))
+ .set_name("pool4")
+ // Layer 5
+ << ConvolutionLayer(
+ 3U, 3U, 512U, get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_1_w.npy", weights_layout),
+ get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_1_b.npy"), PadStrideInfo(1, 1, 1, 1))
+ .set_name("conv5_1")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("conv5_1/Relu")
+ << ConvolutionLayer(
+ 3U, 3U, 512U, get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_2_w.npy", weights_layout),
+ get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_2_b.npy"), PadStrideInfo(1, 1, 1, 1))
+ .set_name("conv5_2")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("conv5_2/Relu")
+ << ConvolutionLayer(
+ 3U, 3U, 512U, get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_3_w.npy", weights_layout),
+ get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_3_b.npy"), PadStrideInfo(1, 1, 1, 1))
+ .set_name("conv5_3")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("conv5_3/Relu")
+ << ConvolutionLayer(
+ 3U, 3U, 512U, get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_4_w.npy", weights_layout),
+ get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_4_b.npy"), PadStrideInfo(1, 1, 1, 1))
+ .set_name("conv5_4")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("conv5_4/Relu")
+ << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0)))
+ .set_name("pool5")
+ // Layer 6
+ << FullyConnectedLayer(4096U,
+ get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc6_w.npy", weights_layout),
+ get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc6_b.npy"))
+ .set_name("fc6")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Relu")
+ // Layer 7
+ << FullyConnectedLayer(4096U,
+ get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc7_w.npy", weights_layout),
+ get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc7_b.npy"))
+ .set_name("fc7")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Relu_1")
+ // Layer 8
+ << FullyConnectedLayer(1000U,
+ get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc8_w.npy", weights_layout),
+ get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc8_b.npy"))
+ .set_name("fc8")
+ // Softmax
+ << SoftmaxLayer().set_name("prob") << OutputLayer(get_output_accessor(common_params, 5));
// Finalize graph
GraphConfig config;
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-03 17:03:54 +0000