* 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/SubGraph.h"
+#include "arm_compute/graph.h"
#include "support/ToolchainSupport.h"
#include "utils/GraphUtils.h"
#include "utils/Utils.h"
#include <tuple>
using namespace arm_compute::utils;
-using namespace arm_compute::graph;
+using namespace arm_compute::graph::frontend;
using namespace arm_compute::graph_utils;
/** Example demonstrating how to implement InceptionV3'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, 2 = OpenCL with Tuner), [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) )
*/
-class InceptionV3Example final : public Example
+class InceptionV3Example : public Example
{
public:
void do_setup(int argc, char **argv) override
std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>();
// Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON
- const int int_target_hint = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
- TargetHint target_hint = set_target_hint(int_target_hint);
+ 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;
// Parse arguments
if(argc < 2)
{
// Print help
- std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n";
+ 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]\n\n";
+ 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]\n\n";
+ 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]\n\n";
+ 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
+ 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 << Tensor(TensorInfo(TensorShape(299U, 299U, 3U, 1U), 1, DataType::F32),
- get_input_accessor(image, std::move(preprocessor), false))
-
+ graph << target_hint
+ << fast_math_hint
+ << InputLayer(TensorDescriptor(TensorShape(299U, 299U, 3U, 1U), DataType::F32),
+ get_input_accessor(image, std::move(preprocessor), false))
<< ConvolutionLayer(3U, 3U, 32U,
get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 0, 0))
+ .set_name("Conv2d_1a_3x3/convolution")
<< BatchNormalizationLayer(get_weights_accessor(data_path,
"/cnn_data/inceptionv3_model/Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path,
"/cnn_data/inceptionv3_model/Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f), get_weights_accessor(data_path,
"/cnn_data/inceptionv3_model/Conv2d_1a_3x3_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-
+ 0.001f)
+ .set_name("Conv2d_1a_3x3/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_1a_3x3/Relu")
<< ConvolutionLayer(3U, 3U, 32U,
get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_2a_3x3_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name("Conv2d_2a_3x3/convolution")
<< BatchNormalizationLayer(get_weights_accessor(data_path,
"/cnn_data/inceptionv3_model/Conv2d_2a_3x3_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path,
"/cnn_data/inceptionv3_model/Conv2d_2a_3x3_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f), get_weights_accessor(data_path,
"/cnn_data/inceptionv3_model/Conv2d_2a_3x3_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ 0.001f)
+ .set_name("Conv2d_2a_3x3/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2a_3x3/Relu")
<< ConvolutionLayer(3U, 3U, 64U,
get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_2b_3x3_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
+ .set_name("Conv2d_2b_3x3/convolution")
<< BatchNormalizationLayer(get_weights_accessor(data_path,
"/cnn_data/inceptionv3_model/Conv2d_2b_3x3_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path,
"/cnn_data/inceptionv3_model/Conv2d_2b_3x3_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f), get_weights_accessor(data_path,
"/cnn_data/inceptionv3_model/Conv2d_2b_3x3_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ 0.001f)
+ .set_name("Conv2d_2b_3x3/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2b_3x3/Relu")
- << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+ << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("MaxPool_3a_3x3/MaxPool")
<< ConvolutionLayer(1U, 1U, 80U,
get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_3b_1x1_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name("Conv2d_3b_1x1/convolution")
<< BatchNormalizationLayer(get_weights_accessor(data_path,
"/cnn_data/inceptionv3_model/Conv2d_3b_1x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path,
"/cnn_data/inceptionv3_model/Conv2d_3b_1x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f), get_weights_accessor(data_path,
"/cnn_data/inceptionv3_model/Conv2d_3b_1x1_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ 0.001f)
+ .set_name("Conv2d_3b_1x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_3b_1x1/Relu")
<< ConvolutionLayer(3U, 3U, 192U,
get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_4a_3x3_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name("Conv2d_4a_3x3/convolution")
<< BatchNormalizationLayer(get_weights_accessor(data_path,
"/cnn_data/inceptionv3_model/Conv2d_4a_3x3_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path,
"/cnn_data/inceptionv3_model/Conv2d_4a_3x3_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f), get_weights_accessor(data_path,
"/cnn_data/inceptionv3_model/Conv2d_4a_3x3_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ 0.001f)
+ .set_name("Conv2d_4a_3x3/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_4a_3x3/Relu")
- << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+ << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("MaxPool_5a_3x3/MaxPool");
- << get_inception_node_A(data_path, "Mixed_5b", 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U),
+ graph << get_inception_node_A(data_path, "Mixed_5b", 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U),
32U)
- << get_inception_node_A(data_path, "Mixed_5c", 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U),
+ .set_name("Mixed_5b/concat");
+ graph << get_inception_node_A(data_path, "Mixed_5c", 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U),
64U, true)
- << get_inception_node_A(data_path, "Mixed_5d", 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U),
+ .set_name("Mixed_5c/concat");
+ graph << get_inception_node_A(data_path, "Mixed_5d", 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U),
64U)
+ .set_name("Mixed_5d/concat");
- << get_inception_node_B(data_path, "Mixed_6a", 384U, std::make_tuple(64U, 96U, 96U))
+ graph << get_inception_node_B(data_path, "Mixed_6a", 384U, std::make_tuple(64U, 96U, 96U)).set_name("Mixed_6a/concat");
- << get_inception_node_C(data_path, "Mixed_6b", 192U, std::make_tuple(128U, 128U, 192U),
+ graph << get_inception_node_C(data_path, "Mixed_6b", 192U, std::make_tuple(128U, 128U, 192U),
std::make_tuple(128U, 128U, 128U, 128U, 192U), 192U)
- << get_inception_node_C(data_path, "Mixed_6c", 192U, std::make_tuple(160U, 160U, 192U),
+ .set_name("Mixed_6b/concat");
+ graph << get_inception_node_C(data_path, "Mixed_6c", 192U, std::make_tuple(160U, 160U, 192U),
std::make_tuple(160U, 160U, 160U, 160U, 192U), 192U)
- << get_inception_node_C(data_path, "Mixed_6d", 192U, std::make_tuple(160U, 160U, 192U),
+ .set_name("Mixed_6c/concat");
+ graph << get_inception_node_C(data_path, "Mixed_6d", 192U, std::make_tuple(160U, 160U, 192U),
std::make_tuple(160U, 160U, 160U, 160U, 192U), 192U)
- << get_inception_node_C(data_path, "Mixed_6e", 192U, std::make_tuple(192U, 192U, 192U),
+ .set_name("Mixed_6d/concat");
+ graph << get_inception_node_C(data_path, "Mixed_6e", 192U, std::make_tuple(192U, 192U, 192U),
std::make_tuple(192U, 192U, 192U, 192U, 192U), 192U)
+ .set_name("Mixed_6e/concat");
- << get_inception_node_D(data_path, "Mixed_7a", std::make_tuple(192U, 320U),
+ graph << get_inception_node_D(data_path, "Mixed_7a", std::make_tuple(192U, 320U),
std::make_tuple(192U, 192U, 192U, 192U))
+ .set_name("Mixed_7a/concat");
- << get_inception_node_E(data_path, "Mixed_7b", 320U, std::make_tuple(384U, 384U, 384U),
+ graph << get_inception_node_E(data_path, "Mixed_7b", 320U, std::make_tuple(384U, 384U, 384U),
std::make_tuple(448U, 384U, 384U, 384U), 192U)
- << get_inception_node_E(data_path, "Mixed_7c", 320U, std::make_tuple(384U, 384U, 384U),
+ .set_name("Mixed_7b/concat");
+ graph << get_inception_node_E(data_path, "Mixed_7c", 320U, std::make_tuple(384U, 384U, 384U),
std::make_tuple(448U, 384U, 384U, 384U), 192U, true)
+ .set_name("Mixed_7c/concat");
- << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 8, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL)))
+ graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 8, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL))).set_name("Logits/AvgPool_1a_8x8/AvgPool")
<< ConvolutionLayer(1U, 1U, 1001U, get_weights_accessor(data_path,
"/cnn_data/inceptionv3_model/Logits_Conv2d_1c_1x1_weights.npy"),
get_weights_accessor(data_path,
"/cnn_data/inceptionv3_model/Logits_Conv2d_1c_1x1_biases.npy"),
PadStrideInfo(1, 1, 0, 0))
- << ReshapeLayer(TensorShape(1001U)) << SoftmaxLayer()
- << Tensor(get_output_accessor(label, 5));
-
- // In order to enable the OpenCL tuner, graph_init() has to be called only when all nodes have been instantiated
- graph.graph_init(int_target_hint == 2);
+ .set_name("Logits/Conv2d_1c_1x1/convolution")
+ << ReshapeLayer(TensorShape(1001U)).set_name("Predictions/Reshape")
+ << SoftmaxLayer().set_name("Predictions/Softmax")
+ << OutputLayer(get_output_accessor(label, 5));
+
+ // Finalize graph
+ GraphConfig config;
+ config.use_tuner = (target == 2);
+ graph.finalize(target_hint, config);
}
void do_run() override
}
private:
- Graph graph{};
+ Stream graph{ 0, "InceptionV3" };
private:
BranchLayer get_inception_node_A(const std::string &data_path, std::string &¶m_path,
conv_id1 = "_1_0c_";
}
- SubGraph i_a;
+ SubStream i_a(graph);
i_a << ConvolutionLayer(
1U, 1U, a_filt,
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/convolution")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ 0.001f)
+ .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu");
- SubGraph i_b;
+ SubStream i_b(graph);
i_b << ConvolutionLayer(
1U, 1U, std::get<0>(b_filters),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_1/Conv2d" + conv_id0 + "1x1/convolution")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ 0.001f)
+ .set_name(param_path + "/Branch_1/Conv2d" + conv_id0 + "1x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d" + conv_id0 + "1x1/Relu")
<< ConvolutionLayer(
5U, 5U, std::get<1>(b_filters),
get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 2, 2))
+ .set_name(param_path + "/Branch_1/Conv2d" + conv_id1 + "5x5/convolution")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ 0.001f)
+ .set_name(param_path + "/Branch_1/Conv2d" + conv_id1 + "5x5/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d" + conv_id1 + "5x5/Relu");
- SubGraph i_c;
+ SubStream i_c(graph);
i_c << ConvolutionLayer(
1U, 1U, std::get<0>(c_filters),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/convolution")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ 0.001f)
+ .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Relu")
<< ConvolutionLayer(
3U, 3U, std::get<1>(c_filters),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 1, 1))
+ .set_name(param_path + "/Branch_2/Conv2d_0b_3x3/convolution")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ 0.001f)
+ .set_name(param_path + "/Branch_2/Conv2d_0b_3x3/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0b_3x3/Relu")
<< ConvolutionLayer(
3U, 3U, std::get<2>(c_filters),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 1, 1))
+ .set_name(param_path + "/Branch_2/Conv2d_0c_3x3/convolution")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ 0.001f)
+ .set_name(param_path + "/Branch_2/Conv2d_0c_3x3/BatchNorm/batcnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0c_3x3/Relu");
- SubGraph i_d;
- i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true))
+ SubStream i_d(graph);
+ i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)).set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool")
<< ConvolutionLayer(
1U, 1U, d_filt,
get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/convolution")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ 0.001f)
+ .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_3/Conv2d_0b_1x1/Relu");
return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d));
}
std::tuple<unsigned int, unsigned int, unsigned int> b_filters)
{
std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_";
- SubGraph i_a;
+ SubStream i_a(graph);
i_a << ConvolutionLayer(
3U, 3U, a_filt,
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(2, 2, 0, 0))
+ .set_name(param_path + "/Branch_0/Conv2d_1a_1x1/convolution")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ 0.001f)
+ .set_name(param_path + "/Branch_0/Conv2d_1a_1x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_1a_1x1/Relu");
- SubGraph i_b;
+ SubStream i_b(graph);
i_b << ConvolutionLayer(
1U, 1U, std::get<0>(b_filters),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/convolution")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ 0.001f)
+ .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu")
<< ConvolutionLayer(
3U, 3U, std::get<1>(b_filters),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 1, 1))
+ .set_name(param_path + "/Branch_1/Conv2d_0b_3x3/convolution")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ 0.001f)
+ .set_name(param_path + "/Branch_1/Conv2d_0b_3x3/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_3x3/Relu")
<< ConvolutionLayer(
3U, 3U, std::get<2>(b_filters),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(2, 2, 0, 0))
+ .set_name(param_path + "/Branch_1/Conv2d_1a_1x1/convolution")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ 0.001f)
+ .set_name(param_path + "/Branch_1/Conv2d_1a_1x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_1a_1x1/Relu");
- SubGraph i_c;
- i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f, 0.f));
+ SubStream i_c(graph);
+ i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name(param_path + "/Branch_2/MaxPool_1a_3x3/MaxPool");
return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c));
}
unsigned int d_filt)
{
std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_";
- SubGraph i_a;
+ SubStream i_a(graph);
i_a << ConvolutionLayer(
1U, 1U, a_filt,
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/convolution")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ 0.001f)
+ .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu");
- SubGraph i_b;
+ SubStream i_b(graph);
i_b << ConvolutionLayer(
1U, 1U, std::get<0>(b_filters),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/convolution")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ 0.001f)
+ .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu")
<< ConvolutionLayer(
7U, 1U, std::get<1>(b_filters),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 3, 0))
+ .set_name(param_path + "/Branch_1/Conv2d_0b_1x7/convolution")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ 0.001f)
+ .set_name(param_path + "/Branch_1/Conv2d_0b_1x7/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_1x7/Relu")
<< ConvolutionLayer(
1U, 7U, std::get<2>(b_filters),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 0, 3))
+ .set_name(param_path + "/Branch_1/Conv2d_0c_7x1/convolution")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ 0.001f)
+ .set_name(param_path + "/Branch_1/Conv2d_0c_7x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0c_7x1/Relu");
- SubGraph i_c;
+ SubStream i_c(graph);
i_c << ConvolutionLayer(
1U, 1U, std::get<0>(c_filters),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/convolution")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ 0.001f)
+ .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Relu")
<< ConvolutionLayer(
1U, 7U, std::get<1>(c_filters),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 0, 3))
+ .set_name(param_path + "/Branch_2/Conv2d_0b_7x1/convolution")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ 0.001f)
+ .set_name(param_path + "/Branch_2/Conv2d_0b_7x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0b_7x1/Relu")
<< ConvolutionLayer(
7U, 1U, std::get<2>(c_filters),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 3, 0))
+ .set_name(param_path + "/Branch_2/Conv2d_0c_1x7/convolution")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ 0.001f)
+ .set_name(param_path + "/Branch_2/Conv2d_0c_1x7/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0c_1x7/Relu")
<< ConvolutionLayer(
1U, 7U, std::get<3>(c_filters),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 0, 3))
+ .set_name(param_path + "/Branch_2/Conv2d_0d_7x1/convolution")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ 0.001f)
+ .set_name(param_path + "/Branch_2/Conv2d_0d_7x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0d_7x1/Relu")
<< ConvolutionLayer(
7U, 1U, std::get<4>(c_filters),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 3, 0))
+ .set_name(param_path + "/Branch_2/Conv2d_0e_1x7/convolution")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ 0.001f)
+ .set_name(param_path + "/Branch_2/Conv2d_0e_1x7/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0e_1x7/Relu");
- SubGraph i_d;
- i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true))
+ SubStream i_d(graph);
+ i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)).set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool")
<< ConvolutionLayer(
1U, 1U, d_filt,
get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/convolution")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ 0.001f)
+ .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_3/Conv2d_0b_1x1/Relu");
return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d));
}
std::tuple<unsigned int, unsigned int, unsigned int, unsigned int> b_filters)
{
std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_";
- SubGraph i_a;
+ SubStream i_a(graph);
i_a << ConvolutionLayer(
1U, 1U, std::get<0>(a_filters),
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/convolution")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ 0.001f)
+ .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu")
<< ConvolutionLayer(
3U, 3U, std::get<1>(a_filters),
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(2, 2, 0, 0))
+ .set_name(param_path + "/Branch_0/Conv2d_1a_3x3/convolution")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ 0.001f)
+ .set_name(param_path + "/Branch_0/Conv2d_1a_3x3/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_1a_3x3/Relu");
- SubGraph i_b;
+ SubStream i_b(graph);
i_b << ConvolutionLayer(
1U, 1U, std::get<0>(b_filters),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/convolution")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ 0.001f)
+ .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu")
<< ConvolutionLayer(
7U, 1U, std::get<1>(b_filters),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 3, 0))
+ .set_name(param_path + "/Branch_1/Conv2d_0b_1x7/convolution")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ 0.001f)
+ .set_name(param_path + "/Branch_1/Conv2d_0b_1x7/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_1x7/Relu")
<< ConvolutionLayer(
1U, 7U, std::get<2>(b_filters),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 0, 3))
+ .set_name(param_path + "/Branch_1/Conv2d_0c_7x1/convolution")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ 0.001f)
+ .set_name(param_path + "/Branch_1/Conv2d_0c_7x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0c_7x1/Relu")
<< ConvolutionLayer(
3U, 3U, std::get<3>(b_filters),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(2, 2, 0, 0))
+ .set_name(param_path + "/Branch_1/Conv2d_1a_3x3/convolution")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ 0.001f)
+ .set_name(param_path + "/Branch_1/Conv2d_1a_3x3/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_1a_3x3/Relu");
- SubGraph i_c;
- i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f, 0.f));
+ SubStream i_c(graph);
+ i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name(param_path + "/Branch_2/MaxPool_1a_3x3/MaxPool");
return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c));
}
}
std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_";
- SubGraph i_a;
+ SubStream i_a(graph);
i_a << ConvolutionLayer(
1U, 1U, a_filt,
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/convolution")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ 0.001f)
+ .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu");
- SubGraph i_b1;
+ SubStream i_b(graph);
+ i_b << ConvolutionLayer(
+ 1U, 1U, std::get<0>(b_filters),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu");
+
+ SubStream i_b1(static_cast<IStream &>(i_b));
i_b1 << ConvolutionLayer(
3U, 1U, std::get<1>(b_filters),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 1, 0))
+ .set_name(param_path + "/Branch_1/Conv2d_0b_1x3/convolution")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ 0.001f)
+ .set_name(param_path + "/Branch_1/Conv2d_0b_1x3/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_1x3/Relu");
- SubGraph i_b2;
+ SubStream i_b2(static_cast<IStream &>(i_b));
i_b2 << ConvolutionLayer(
1U, 3U, std::get<2>(b_filters),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 0, 1))
+ .set_name(param_path + "/Branch_1/Conv2d" + conv_id + "3x1/convolution")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ 0.001f)
+ .set_name(param_path + "/Branch_1/Conv2d" + conv_id + "3x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d" + conv_id + "3x1/Relu");
- SubGraph i_b;
- i_b << ConvolutionLayer(
- 1U, 1U, std::get<0>(b_filters),
- get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy"),
+ // Merge b1 and b2
+ i_b << BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_b1), std::move(i_b2)).set_name(param_path + "/Branch_1/concat");
+
+ SubStream i_c(graph);
+ i_c << ConvolutionLayer(
+ 1U, 1U, std::get<0>(c_filters),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/convolution")
<< BatchNormalizationLayer(
- get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
- get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
- get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
- << BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_b1), std::move(i_b2));
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Relu")
+ << ConvolutionLayer(
+ 3U, 3U, std::get<1>(c_filters),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 1, 1))
+ .set_name(param_path + "/Branch_2/Conv2d_0b_3x3/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_2/Conv2d_0b_3x3/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0b_3x3/Relu");
- SubGraph i_c1;
+ SubStream i_c1(static_cast<IStream &>(i_c));
i_c1 << ConvolutionLayer(
3U, 1U, std::get<2>(c_filters),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 1, 0))
+ .set_name(param_path + "/Branch_2/Conv2d_0c_1x3/convolution")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ 0.001f)
+ .set_name(param_path + "/Branch_2/Conv2d_0c_1x3/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0c_1x3/Relu");
- SubGraph i_c2;
+ SubStream i_c2(static_cast<IStream &>(i_c));
i_c2 << ConvolutionLayer(
1U, 3U, std::get<3>(c_filters),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 0, 1))
+ .set_name(param_path + "/Branch_2/Conv2d_0d_3x1/convolution")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ 0.001f)
+ .set_name(param_path + "/Branch_2/Conv2d_0d_3x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0d_3x1/Relu");
- SubGraph i_c;
- i_c << ConvolutionLayer(
- 1U, 1U, std::get<0>(c_filters),
- get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy"),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- PadStrideInfo(1, 1, 0, 0))
- << BatchNormalizationLayer(
- get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
- get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
- get_random_accessor(1.f, 1.f),
- get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
- << ConvolutionLayer(
- 3U, 3U, std::get<1>(c_filters),
- get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_weights.npy"),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- PadStrideInfo(1, 1, 1, 1))
- << BatchNormalizationLayer(
- get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"),
- get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"),
- get_random_accessor(1.f, 1.f),
- get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
- << BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_c1), std::move(i_c2));
+ // Merge i_c1 and i_c2
+ i_c << BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_c1), std::move(i_c2)).set_name(param_path + "/Branch_2/concat");
- SubGraph i_d;
- i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true))
+ SubStream i_d(graph);
+ i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)).set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool")
<< ConvolutionLayer(
1U, 1U, d_filt,
get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/convolution")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy"),
- 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ 0.001f)
+ .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_3/Conv2d_0b_1x1/Relu");
return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d));
}
/** Main program for Inception V3
*
* @param[in] argc Number of arguments
- * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [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, char **argv)
{
projects / platform / upstream / armcl.git / blobdiff