diff options
Diffstat (limited to 'examples/graph_inception_v4.cpp')
| -rw-r--r-- | examples/graph_inception_v4.cpp | 277 |
1 files changed, 148 insertions, 129 deletions
diff --git a/examples/graph_inception_v4.cpp b/examples/graph_inception_v4.cpp index f004b41fb..ed95baa99 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/graph.h" #include "support/ToolchainSupport.h" #include "utils/GraphUtils.h" #include "utils/Utils.h" @@ -32,19 +30,22 @@ #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 InceptionV4'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 InceptionV4Example final : public Example { public: void do_setup(int argc, char **argv) override { + // Disabled the test for now because the process gets killed on Linux Firefly 32 bit even when using ConvolutionMethodHint::DIRECT. + // Needs to review/rework to run the code below. +#if __aarch64__ std::string data_path; /* Path to the trainable data */ std::string image; /* Image data */ std::string label; /* Label data */ @@ -52,45 +53,56 @@ 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_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)) // Conv2d_1a_3x3 << ConvolutionLayer(3U, 3U, 32U, get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_1a_3x3_weights.npy"), @@ -120,64 +132,71 @@ public: get_random_accessor(1.f, 1.f), get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2b_3x3_BatchNorm_beta.npy"), 0.001f) - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) - - << get_mixed_3a(data_path) - << get_mixed_4a(data_path) - << get_mixed_5a(data_path) - // 4 inception A blocks - << get_inceptionA_block(data_path, "Mixed_5b") - << get_inceptionA_block(data_path, "Mixed_5c") - << get_inceptionA_block(data_path, "Mixed_5d") - << get_inceptionA_block(data_path, "Mixed_5e") - // reduction A block - << get_reductionA_block(data_path) - // 7 inception B blocks - << get_inceptionB_block(data_path, "Mixed_6b") - << get_inceptionB_block(data_path, "Mixed_6c") - << get_inceptionB_block(data_path, "Mixed_6d") - << get_inceptionB_block(data_path, "Mixed_6e") - << get_inceptionB_block(data_path, "Mixed_6f") - << get_inceptionB_block(data_path, "Mixed_6g") - << get_inceptionB_block(data_path, "Mixed_6h") - // reduction B block - << get_reductionB_block(data_path) - // 3 inception C blocks - << get_inceptionC_block(data_path, "Mixed_7b") - << get_inceptionC_block(data_path, "Mixed_7c") - << get_inceptionC_block(data_path, "Mixed_7d") - << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + + graph << get_mixed_3a(data_path); + graph << get_mixed_4a(data_path); + graph << get_mixed_5a(data_path); + // 4 inception A blocks + graph << get_inceptionA_block(data_path, "Mixed_5b"); + graph << get_inceptionA_block(data_path, "Mixed_5c"); + graph << get_inceptionA_block(data_path, "Mixed_5d"); + graph << get_inceptionA_block(data_path, "Mixed_5e"); + // reduction A block + graph << get_reductionA_block(data_path); + // 7 inception B blocks + graph << get_inceptionB_block(data_path, "Mixed_6b"); + graph << get_inceptionB_block(data_path, "Mixed_6c"); + graph << get_inceptionB_block(data_path, "Mixed_6d"); + graph << get_inceptionB_block(data_path, "Mixed_6e"); + graph << get_inceptionB_block(data_path, "Mixed_6f"); + graph << get_inceptionB_block(data_path, "Mixed_6g"); + graph << get_inceptionB_block(data_path, "Mixed_6h"); + // reduction B block + graph << get_reductionB_block(data_path); + // 3 inception C blocks + graph << get_inceptionC_block(data_path, "Mixed_7b"); + graph << get_inceptionC_block(data_path, "Mixed_7c"); + graph << get_inceptionC_block(data_path, "Mixed_7d"); + graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)) << FlattenLayer() << FullyConnectedLayer( 1001U, 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)); - - // 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); + << OutputLayer(get_output_accessor(label, 5)); + + // Finalize graph + GraphConfig config; + config.use_tuner = (target == 2); + graph.finalize(target_hint, config); +#else /* __aarch64__ */ + using namespace arm_compute; + ARM_COMPUTE_UNUSED(argc); + ARM_COMPUTE_UNUSED(argv); +#endif /* __aarch64__ */ } void do_run() override { +#if __aarch64__ graph.run(); +#endif /* __aarch64__ */ } 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 +214,7 @@ 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 +234,7 @@ 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 +279,7 @@ 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 +290,8 @@ 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 +300,7 @@ 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 +311,7 @@ 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 +331,7 @@ 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 +360,7 @@ 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 +379,7 @@ 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 +390,7 @@ 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 +419,9 @@ 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 +429,7 @@ 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 +440,7 @@ 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 +469,7 @@ 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 +516,7 @@ 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 +535,7 @@ 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 +555,7 @@ 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 +593,9 @@ 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 +603,7 @@ 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 +614,7 @@ 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 +626,40 @@ 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 +695,40 @@ 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"), @@ -728,7 +747,7 @@ private: /** Main program for Inception V4 * * @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) { |