/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-#include "arm_compute/graph/Graph.h"
-#include "arm_compute/graph/Nodes.h"
+#include "arm_compute/graph.h"
#include "support/ToolchainSupport.h"
#include "utils/GraphUtils.h"
#include "utils/Utils.h"
#include <cstdlib>
-using namespace arm_compute::graph;
+using namespace arm_compute::utils;
+using namespace arm_compute::graph::frontend;
using namespace arm_compute::graph_utils;
/** Example demonstrating how to implement VGG19's network using the Compute Library's graph API
*
* @param[in] argc Number of arguments
- * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels )
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
*/
-void main_graph_vgg19(int argc, const char **argv)
+class GraphVGG19Example : public Example
{
- std::string data_path; /* Path to the trainable data */
- std::string image; /* Image data */
- std::string label; /* Label data */
+public:
+ void do_setup(int argc, char **argv) override
+ {
+ std::string data_path; /* Path to the trainable data */
+ std::string image; /* Image data */
+ std::string label; /* Label data */
- constexpr float mean_r = 123.68f; /* Mean value to subtract from red channel */
- constexpr float mean_g = 116.779f; /* Mean value to subtract from green channel */
- constexpr float mean_b = 103.939f; /* Mean value to subtract from blue channel */
+ // Create a preprocessor object
+ const std::array<float, 3> mean_rgb{ { 123.68f, 116.779f, 103.939f } };
+ std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
- // Set target. 0 (NEON), 1 (OpenCL). By default it is NEON
- TargetHint target_hint = set_target_hint(argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0);
- ConvolutionMethodHint convolution_hint = ConvolutionMethodHint::DIRECT;
+ // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON
+ const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
+ Target target_hint = set_target_hint(target);
+ FastMathHint fast_math_hint = FastMathHint::DISABLED;
+ const bool is_opencl = target_hint == Target::CL;
- // Parse arguments
- if(argc < 2)
- {
- // Print help
- std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n";
- std::cout << "No data folder provided: using random values\n\n";
- }
- else if(argc == 2)
- {
- std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels]\n\n";
- std::cout << "No data folder provided: using random values\n\n";
- }
- else if(argc == 3)
- {
- data_path = argv[2];
- std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels]\n\n";
- std::cout << "No image provided: using random values\n\n";
- }
- else if(argc == 4)
- {
- data_path = argv[2];
- image = argv[3];
- std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels]\n\n";
- std::cout << "No text file with labels provided: skipping output accessor\n\n";
+ ConvolutionMethod first_convolution3x3_hint = is_opencl ? ConvolutionMethod::DIRECT : ConvolutionMethod::GEMM;
+ ConvolutionMethod convolution3x3_hint = ConvolutionMethod::DEFAULT;
+
+ // Parse arguments
+ if(argc < 2)
+ {
+ // Print help
+ std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\n\n";
+ std::cout << "No data folder provided: using random values\n\n";
+ }
+ else if(argc == 2)
+ {
+ std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels] [fast_math_hint]\n\n";
+ std::cout << "No data folder provided: using random values\n\n";
+ }
+ else if(argc == 3)
+ {
+ data_path = argv[2];
+ std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels] [fast_math_hint]\n\n";
+ std::cout << "No image provided: using random values\n\n";
+ }
+ else if(argc == 4)
+ {
+ data_path = argv[2];
+ image = argv[3];
+ std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n";
+ std::cout << "No text file with labels provided: skipping output accessor\n\n";
+ }
+ else if(argc == 5)
+ {
+ data_path = argv[2];
+ image = argv[3];
+ label = argv[4];
+ std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n";
+ std::cout << "No fast math info provided: disabling fast math\n\n";
+ }
+ else
+ {
+ data_path = argv[2];
+ image = argv[3];
+ label = argv[4];
+ fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;
+ }
+
+ graph << target_hint
+ << first_convolution3x3_hint
+ << fast_math_hint
+ << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::F32),
+ get_input_accessor(image, std::move(preprocessor)))
+ // Layer 1
+ << ConvolutionLayer(
+ 3U, 3U, 64U,
+ get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_1_w.npy"),
+ 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")
+ << convolution3x3_hint
+ << ConvolutionLayer(
+ 3U, 3U, 64U,
+ get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_2_w.npy"),
+ 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, 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"),
+ 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"),
+ 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, 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"),
+ 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"),
+ 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"),
+ 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"),
+ 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, 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"),
+ 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"),
+ 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"),
+ 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"),
+ 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, 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"),
+ 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"),
+ 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"),
+ 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"),
+ 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, PadStrideInfo(2, 2, 0, 0))).set_name("pool5")
+ // Layer 6
+ << FullyConnectedLayer(
+ 4096U,
+ get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc6_w.npy"),
+ 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"),
+ 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"),
+ get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc8_b.npy"))
+ .set_name("fc8")
+ // Softmax
+ << SoftmaxLayer().set_name("prob")
+ << OutputLayer(get_output_accessor(label, 5));
+
+ // Finalize graph
+ GraphConfig config;
+ config.use_tuner = (target == 2);
+ graph.finalize(target_hint, config);
}
- else
+ void do_run() override
{
- data_path = argv[2];
- image = argv[3];
- label = argv[4];
+ // Run graph
+ graph.run();
}
- Graph graph;
-
- graph << target_hint
- << convolution_hint
- << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32),
- get_input_accessor(image, mean_r, mean_g, mean_b))
- // Layer 1
- << ConvolutionLayer(
- 3U, 3U, 64U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_1_w.npy"),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_1_b.npy"),
- PadStrideInfo(1, 1, 1, 1))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
- << ConvolutionLayer(
- 3U, 3U, 64U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_2_w.npy"),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_2_b.npy"),
- PadStrideInfo(1, 1, 1, 1))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
- << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
- // Layer 2
- << ConvolutionLayer(
- 3U, 3U, 128U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_1_w.npy"),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_1_b.npy"),
- PadStrideInfo(1, 1, 1, 1))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
- << ConvolutionLayer(
- 3U, 3U, 128U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_2_w.npy"),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_2_b.npy"),
- PadStrideInfo(1, 1, 1, 1))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
- << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
- // Layer 3
- << ConvolutionLayer(
- 3U, 3U, 256U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_1_w.npy"),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_1_b.npy"),
- PadStrideInfo(1, 1, 1, 1))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
- << ConvolutionLayer(
- 3U, 3U, 256U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_2_w.npy"),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_2_b.npy"),
- PadStrideInfo(1, 1, 1, 1))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
- << ConvolutionLayer(
- 3U, 3U, 256U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_3_w.npy"),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_3_b.npy"),
- PadStrideInfo(1, 1, 1, 1))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
- << ConvolutionLayer(
- 3U, 3U, 256U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_4_w.npy"),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_4_b.npy"),
- PadStrideInfo(1, 1, 1, 1))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
- << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
- // Layer 4
- << ConvolutionLayer(
- 3U, 3U, 512U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_1_w.npy"),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_1_b.npy"),
- PadStrideInfo(1, 1, 1, 1))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
- << ConvolutionLayer(
- 3U, 3U, 512U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_2_w.npy"),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_2_b.npy"),
- PadStrideInfo(1, 1, 1, 1))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
- << ConvolutionLayer(
- 3U, 3U, 512U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_3_w.npy"),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_3_b.npy"),
- PadStrideInfo(1, 1, 1, 1))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
- << ConvolutionLayer(
- 3U, 3U, 512U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_4_w.npy"),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_4_b.npy"),
- PadStrideInfo(1, 1, 1, 1))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
- << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
- // Layer 5
- << ConvolutionLayer(
- 3U, 3U, 512U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_1_w.npy"),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_1_b.npy"),
- PadStrideInfo(1, 1, 1, 1))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
- << ConvolutionLayer(
- 3U, 3U, 512U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_2_w.npy"),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_2_b.npy"),
- PadStrideInfo(1, 1, 1, 1))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
- << ConvolutionLayer(
- 3U, 3U, 512U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_3_w.npy"),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_3_b.npy"),
- PadStrideInfo(1, 1, 1, 1))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
- << ConvolutionLayer(
- 3U, 3U, 512U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_4_w.npy"),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_4_b.npy"),
- PadStrideInfo(1, 1, 1, 1))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
- << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
- // Layer 6
- << FullyConnectedLayer(
- 4096U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc6_w.npy"),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc6_b.npy"))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
- // Layer 7
- << FullyConnectedLayer(
- 4096U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc7_w.npy"),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc7_b.npy"))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
- // Layer 8
- << FullyConnectedLayer(
- 1000U,
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc8_w.npy"),
- get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc8_b.npy"))
- // Softmax
- << SoftmaxLayer()
- << Tensor(get_output_accessor(label, 5));
-
- // Run graph
- graph.run();
-}
+private:
+ Stream graph{ 0, "VGG19" };
+};
/** Main program for VGG19
*
* @param[in] argc Number of arguments
- * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels )
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
*/
-int main(int argc, const char **argv)
+int main(int argc, char **argv)
{
- return arm_compute::utils::run_example(argc, argv, main_graph_vgg19);
+ return arm_compute::utils::run_example<GraphVGG19Example>(argc, argv);
}
projects / platform / upstream / armcl.git / blobdiff