diff options
author | Georgios Pinitas <georgios.pinitas@arm.com> | 2017-12-22 15:27:52 +0000 |
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committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:49:16 +0000 |
commit | d8734b55d89f05901ba9a75349761a9c955d9243 (patch) | |
tree | e23d53a0fb73251f7416993e4d3a7241e533e79e /examples/graph_inception_v4.cpp | |
parent | 7390e05561a5c49306ebbf2eb2dcb1848546f201 (diff) | |
download | ComputeLibrary-d8734b55d89f05901ba9a75349761a9c955d9243.tar.gz |
COMPMID-793 : Add graph intermediate representation
Change-Id: Ic1685de4e19e0ac79669ef2da64e1dc96c7ea0bf
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/115248
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Diffstat (limited to 'examples/graph_inception_v4.cpp')
-rw-r--r-- | examples/graph_inception_v4.cpp | 184 |
1 files changed, 91 insertions, 93 deletions
diff --git a/examples/graph_inception_v4.cpp b/examples/graph_inception_v4.cpp index f004b41fb0..d9f6156fb2 100644 --- a/examples/graph_inception_v4.cpp +++ b/examples/graph_inception_v4.cpp @@ -21,9 +21,7 @@ * 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/graph2.h" #include "support/ToolchainSupport.h" #include "utils/GraphUtils.h" #include "utils/Utils.h" @@ -32,7 +30,7 @@ #include <tuple> using namespace arm_compute::utils; -using namespace arm_compute::graph; +using namespace arm_compute::graph2::frontend; using namespace arm_compute::graph_utils; /** Example demonstrating how to implement InceptionV4's network using the Compute Library's graph API @@ -52,9 +50,11 @@ public: // Create a preprocessor object 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); + // Set target. 0 (NEON), 1 (OpenCL). By default it is NEON + const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0; + Target target_hint = set_target_hint2(target); + bool enable_tuning = (target == 2); + bool enable_memory_management = true; // Parse arguments if(argc < 2) @@ -88,8 +88,8 @@ public: label = argv[4]; } - graph << target_hint << Tensor(TensorInfo(TensorShape(299U, 299U, 3U, 1U), 1, DataType::F32), - get_input_accessor(image, std::move(preprocessor), false)) + graph << target_hint << InputLayer(TensorDescriptor(TensorShape(299U, 299U, 3U, 1U), DataType::F32), + get_input_accessor(image, std::move(preprocessor), false)) // Conv2d_1a_3x3 << ConvolutionLayer(3U, 3U, 32U, @@ -153,10 +153,10 @@ public: get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Logits_Logits_weights.npy"), get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Logits_Logits_biases.npy")) << SoftmaxLayer() - << Tensor(get_output_accessor(label, 5)); + << OutputLayer(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); + // Finalize graph + graph.finalize(target_hint, enable_tuning, enable_memory_management); } void do_run() override @@ -165,19 +165,17 @@ public: } private: - Graph graph{}; + Stream graph{ 0, "InceptionV4" }; private: BranchLayer get_mixed_3a(const std::string &data_path) { std::string total_path = "/cnn_data/inceptionv4_model/Mixed_3a_"; - SubGraph i_a; - i_a << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true)) - // TODO (geopin01) : Remove once we understand why a single node graph does not run in CL - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f, 0.f)); + SubStream i_a(graph); + i_a << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true)); - SubGraph i_b; + SubStream i_b(graph); i_b << ConvolutionLayer(3U, 3U, 96U, get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_3x3_weights.npy"), std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 0, 0)) @@ -195,7 +193,7 @@ private: { std::string total_path = "/cnn_data/inceptionv4_model/Mixed_4a_"; - SubGraph i_a; + SubStream i_a(graph); i_a << ConvolutionLayer(1U, 1U, 64U, 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)) @@ -215,7 +213,7 @@ private: 0.001f) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - SubGraph i_b; + SubStream i_b(graph); i_b << ConvolutionLayer(1U, 1U, 64U, 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)) @@ -260,7 +258,7 @@ private: { std::string total_path = "/cnn_data/inceptionv4_model/Mixed_5a_"; - SubGraph i_a; + SubStream i_a(graph); i_a << ConvolutionLayer(3U, 3U, 192U, 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)) @@ -271,10 +269,8 @@ private: 0.001f) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - SubGraph i_b; - i_b << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true)) - // TODO (geopin01) : Remove once we understand why a single node graph does not run in CL - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f, 0.f)); + SubStream i_b(graph); + i_b << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true)); return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b)); } @@ -283,7 +279,7 @@ private: { std::string total_path = "/cnn_data/inceptionv4_model/" + param_path + "_"; - SubGraph i_a; + SubStream i_a(graph); i_a << ConvolutionLayer(1U, 1U, 96U, 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)) @@ -294,7 +290,7 @@ private: 0.001f) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - SubGraph i_b; + SubStream i_b(graph); i_b << ConvolutionLayer(1U, 1U, 64U, 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)) @@ -314,7 +310,7 @@ private: 0.001f) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - SubGraph i_c; + SubStream i_c(graph); i_c << ConvolutionLayer(1U, 1U, 64U, 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)) @@ -343,7 +339,7 @@ private: 0.001f) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - SubGraph i_d; + SubStream i_d(graph); i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)) << ConvolutionLayer(1U, 1U, 96U, get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy"), @@ -362,7 +358,7 @@ private: { std::string total_path = "/cnn_data/inceptionv4_model/Mixed_6a_"; - SubGraph i_a; + SubStream i_a(graph); i_a << ConvolutionLayer(3U, 3U, 384U, 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)) @@ -373,7 +369,7 @@ private: 0.001f) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - SubGraph i_b; + SubStream i_b(graph); i_b << ConvolutionLayer(1U, 1U, 192U, 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)) @@ -402,10 +398,9 @@ private: 0.001f) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - SubGraph i_c; - i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true)) - // TODO (geopin01) : Remove once we understand why a single node graph does not run in CL - << 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), true)); + return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c)); } @@ -413,7 +408,7 @@ private: { std::string total_path = "/cnn_data/inceptionv4_model/" + param_path + "_"; - SubGraph i_a; + SubStream i_a(graph); i_a << ConvolutionLayer(1U, 1U, 384U, 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)) @@ -424,7 +419,7 @@ private: 0.001f) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - SubGraph i_b; + SubStream i_b(graph); i_b << ConvolutionLayer(1U, 1U, 192U, 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)) @@ -453,7 +448,7 @@ private: 0.001f) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - SubGraph i_c; + SubStream i_c(graph); i_c << ConvolutionLayer(1U, 1U, 192U, 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)) @@ -500,7 +495,7 @@ private: 0.001f) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - SubGraph i_d; + SubStream i_d(graph); i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)) << ConvolutionLayer(1U, 1U, 128U, get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy"), @@ -519,7 +514,7 @@ private: { std::string total_path = "/cnn_data/inceptionv4_model/Mixed_7a_"; - SubGraph i_a; + SubStream i_a(graph); i_a << ConvolutionLayer(1U, 1U, 192U, 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)) @@ -539,7 +534,7 @@ private: 0.001f) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - SubGraph i_b; + SubStream i_b(graph); i_b << ConvolutionLayer(1U, 1U, 256U, 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)) @@ -577,10 +572,9 @@ private: 0.001f) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - SubGraph i_c; - i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true)) - // TODO (geopin01) : Remove once we understand why a single node graph does not run in CL - << 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), true)); + return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c)); } @@ -588,7 +582,7 @@ private: { std::string total_path = "/cnn_data/inceptionv4_model/" + param_path + "_"; - SubGraph i_a; + SubStream i_a(graph); i_a << ConvolutionLayer(1U, 1U, 256U, 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)) @@ -599,35 +593,7 @@ private: 0.001f) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - SubGraph i_b1; - i_b1 << ConvolutionLayer( - 3U, 1U, 256U, - 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)) - << 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) - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - - SubGraph i_b2; - i_b2 << ConvolutionLayer( - 1U, 3U, 256U, - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_3x1_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 1)) - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_3x1_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_3x1_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_3x1_BatchNorm_beta.npy"), - 0.001f) - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - - SubGraph i_b; + SubStream i_b(graph); i_b << ConvolutionLayer( 1U, 1U, 384U, get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy"), @@ -639,38 +605,40 @@ private: get_random_accessor(1.f, 1.f), get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"), 0.001f) - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) - << BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_b1), std::move(i_b2)); + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - SubGraph i_c1; - i_c1 << ConvolutionLayer( + SubStream i_b1(static_cast<IStream &>(i_b)); + i_b1 << ConvolutionLayer( 3U, 1U, 256U, - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_1x3_weights.npy"), + 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)) << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_1x3_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_1x3_BatchNorm_moving_variance.npy"), + 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_2_Conv2d_0d_1x3_BatchNorm_beta.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_beta.npy"), 0.001f) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - SubGraph i_c2; - i_c2 << ConvolutionLayer( + SubStream i_b2(static_cast<IStream &>(i_b)); + i_b2 << ConvolutionLayer( 1U, 3U, 256U, - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_3x1_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_3x1_weights.npy"), std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 1)) << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_3x1_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_3x1_BatchNorm_moving_variance.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_3x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_3x1_BatchNorm_moving_variance.npy"), get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_3x1_BatchNorm_beta.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_3x1_BatchNorm_beta.npy"), 0.001f) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - SubGraph i_c; + // Merge b1 and b2 + i_b << BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_b1), std::move(i_b2)); + + SubStream i_c(graph); i_c << ConvolutionLayer( 1U, 1U, 384U, get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy"), @@ -706,10 +674,40 @@ private: get_random_accessor(1.f, 1.f), get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_BatchNorm_beta.npy"), 0.001f) - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) - << BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_c1), std::move(i_c2)); + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + + SubStream i_c1(static_cast<IStream &>(i_c)); + i_c1 << ConvolutionLayer( + 3U, 1U, 256U, + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_1x3_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 1, 0)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_1x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_1x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_1x3_BatchNorm_beta.npy"), + 0.001f) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + + SubStream i_c2(static_cast<IStream &>(i_c)); + i_c2 << ConvolutionLayer( + 1U, 3U, 256U, + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_3x1_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 0, 1)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_3x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_3x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_3x1_BatchNorm_beta.npy"), + 0.001f) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + + // Merge i_c1 and i_c2 + i_c << BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_c1), std::move(i_c2)); - SubGraph i_d; + SubStream i_d(graph); i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)) << ConvolutionLayer(1U, 1U, 256U, get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy"), |