diff options
Diffstat (limited to 'examples/graph_yolov3.cpp')
| -rw-r--r-- | examples/graph_yolov3.cpp | 829 |
1 files changed, 413 insertions, 416 deletions
diff --git a/examples/graph_yolov3.cpp b/examples/graph_yolov3.cpp index 3c8ddbffd8..5c8d3426ec 100644 --- a/examples/graph_yolov3.cpp +++ b/examples/graph_yolov3.cpp @@ -22,6 +22,7 @@ * SOFTWARE. */ #include "arm_compute/graph.h" + #include "support/ToolchainSupport.h" #include "utils/CommonGraphOptions.h" #include "utils/GraphUtils.h" @@ -35,8 +36,7 @@ using namespace arm_compute::graph_utils; class GraphYOLOv3Example : public Example { public: - GraphYOLOv3Example() - : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "YOLOv3") + GraphYOLOv3Example() : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "YOLOv3") { } @@ -50,14 +50,15 @@ public: common_params = consume_common_graph_parameters(common_opts); // Return when help menu is requested - if(common_params.help) + if (common_params.help) { cmd_parser.print_help(argv[0]); return false; } // Checks - ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "QASYMM8 not supported for this graph"); + ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), + "QASYMM8 not supported for this graph"); // Print parameter values std::cout << common_params << std::endl; @@ -69,331 +70,322 @@ public: std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<TFPreproccessor>(0.f); // Create input descriptor - const TensorShape tensor_shape = permute_shape(TensorShape(608U, 608U, 3U, 1U), DataLayout::NCHW, common_params.data_layout); - TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout); + const TensorShape tensor_shape = + permute_shape(TensorShape(608U, 608U, 3U, 1U), DataLayout::NCHW, common_params.data_layout); + TensorDescriptor input_descriptor = + TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout); // Set weights trained layout const DataLayout weights_layout = DataLayout::NCHW; - graph << common_params.target - << common_params.fast_math_hint + graph << common_params.target << common_params.fast_math_hint << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor), false)); std::pair<SubStream, SubStream> intermediate_layers = darknet53(data_path, weights_layout); - graph << ConvolutionLayer( - 1U, 1U, 512U, - get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_53_w.npy", weights_layout), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name("conv2d_53") - << BatchNormalizationLayer( - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_53_mean.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_53_var.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_53_gamma.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_53_beta.npy"), - 0.000001f) - .set_name("conv2d_53/BatchNorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_53/LeakyRelu") - << ConvolutionLayer( - 3U, 3U, 1024U, - get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_54_w.npy", weights_layout), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 1, 1)) - .set_name("conv2d_54") - << BatchNormalizationLayer( - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_54_mean.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_54_var.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_54_gamma.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_54_beta.npy"), - 0.000001f) - .set_name("conv2d_54/BatchNorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_54/LeakyRelu") - << ConvolutionLayer( - 1U, 1U, 512U, - get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_55_w.npy", weights_layout), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name("conv2d_55") - << BatchNormalizationLayer( - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_55_mean.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_55_var.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_55_gamma.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_55_beta.npy"), - 0.000001f) - .set_name("conv2d_55/BatchNorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_55/LeakyRelu") - << ConvolutionLayer( - 3U, 3U, 1024U, - get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_56_w.npy", weights_layout), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 1, 1)) - .set_name("conv2d_56") - << BatchNormalizationLayer( - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_56_mean.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_56_var.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_56_gamma.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_56_beta.npy"), - 0.000001f) - .set_name("conv2d_56/BatchNorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_56/LeakyRelu") - << ConvolutionLayer( - 1U, 1U, 512U, - get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_57_w.npy", weights_layout), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name("conv2d_57") - << BatchNormalizationLayer( - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_57_mean.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_57_var.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_57_gamma.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_57_beta.npy"), - 0.000001f) - .set_name("conv2d_57/BatchNorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_57/LeakyRelu"); + graph + << ConvolutionLayer( + 1U, 1U, 512U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_53_w.npy", weights_layout), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0)) + .set_name("conv2d_53") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_53_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_53_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_53_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_53_beta.npy"), 0.000001f) + .set_name("conv2d_53/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)) + .set_name("conv2d_53/LeakyRelu") + << ConvolutionLayer( + 3U, 3U, 1024U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_54_w.npy", weights_layout), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1)) + .set_name("conv2d_54") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_54_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_54_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_54_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_54_beta.npy"), 0.000001f) + .set_name("conv2d_54/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)) + .set_name("conv2d_54/LeakyRelu") + << ConvolutionLayer( + 1U, 1U, 512U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_55_w.npy", weights_layout), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0)) + .set_name("conv2d_55") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_55_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_55_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_55_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_55_beta.npy"), 0.000001f) + .set_name("conv2d_55/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)) + .set_name("conv2d_55/LeakyRelu") + << ConvolutionLayer( + 3U, 3U, 1024U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_56_w.npy", weights_layout), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1)) + .set_name("conv2d_56") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_56_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_56_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_56_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_56_beta.npy"), 0.000001f) + .set_name("conv2d_56/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)) + .set_name("conv2d_56/LeakyRelu") + << ConvolutionLayer( + 1U, 1U, 512U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_57_w.npy", weights_layout), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0)) + .set_name("conv2d_57") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_57_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_57_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_57_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_57_beta.npy"), 0.000001f) + .set_name("conv2d_57/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)) + .set_name("conv2d_57/LeakyRelu"); SubStream route_1(graph); - graph << ConvolutionLayer( - 3U, 3U, 1024U, - get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_58_w.npy", weights_layout), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 1, 1)) - .set_name("conv2d_58") - << BatchNormalizationLayer( - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_58_mean.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_58_var.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_58_gamma.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_58_beta.npy"), - 0.000001f) - .set_name("conv2d_58/BatchNorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_58/LeakyRelu") - << ConvolutionLayer( - 1U, 1U, 255U, - get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_59_w.npy", weights_layout), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_59_b.npy", weights_layout), - PadStrideInfo(1, 1, 0, 0)) - .set_name("conv2d_59") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f)).set_name("conv2d_59/Linear") - << YOLOLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC, 0.1f)).set_name("Yolo1") - << OutputLayer(get_output_accessor(common_params, 5)); + graph + << ConvolutionLayer( + 3U, 3U, 1024U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_58_w.npy", weights_layout), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1)) + .set_name("conv2d_58") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_58_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_58_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_58_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_58_beta.npy"), 0.000001f) + .set_name("conv2d_58/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)) + .set_name("conv2d_58/LeakyRelu") + << ConvolutionLayer( + 1U, 1U, 255U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_59_w.npy", weights_layout), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_59_b.npy", weights_layout), + PadStrideInfo(1, 1, 0, 0)) + .set_name("conv2d_59") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f)) + .set_name("conv2d_59/Linear") + << YOLOLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC, 0.1f)).set_name("Yolo1") + << OutputLayer(get_output_accessor(common_params, 5)); route_1 << ConvolutionLayer( - 1U, 1U, 256U, - get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_60_w.npy", weights_layout), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name("conv2d_60") + 1U, 1U, 256U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_60_w.npy", weights_layout), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0)) + .set_name("conv2d_60") << BatchNormalizationLayer( - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_59_mean.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_59_var.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_59_gamma.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_59_beta.npy"), - 0.000001f) - .set_name("conv2d_59/BatchNorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_60/LeakyRelu") + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_59_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_59_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_59_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_59_beta.npy"), + 0.000001f) + .set_name("conv2d_59/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)) + .set_name("conv2d_60/LeakyRelu") << ResizeLayer(InterpolationPolicy::NEAREST_NEIGHBOR, 2, 2).set_name("Upsample_60"); SubStream concat_1(route_1); - concat_1 << ConcatLayer(std::move(route_1), std::move(intermediate_layers.second)).set_name("Route1") - << ConvolutionLayer( - 1U, 1U, 256U, - get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_61_w.npy", weights_layout), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name("conv2d_61") - << BatchNormalizationLayer( - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_60_mean.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_60_var.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_60_gamma.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_60_beta.npy"), - 0.000001f) - .set_name("conv2d_60/BatchNorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_61/LeakyRelu") - << ConvolutionLayer( - 3U, 3U, 512U, - get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_62_w.npy", weights_layout), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 1, 1)) - .set_name("conv2d_62") - << BatchNormalizationLayer( - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_61_mean.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_61_var.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_61_gamma.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_61_beta.npy"), - 0.000001f) - .set_name("conv2d_61/BatchNorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_62/LeakyRelu") - << ConvolutionLayer( - 1U, 1U, 256U, - get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_63_w.npy", weights_layout), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name("conv2d_63") - << BatchNormalizationLayer( - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_62_mean.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_62_var.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_62_gamma.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_62_beta.npy"), - 0.000001f) - .set_name("conv2d_62/BatchNorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_63/LeakyRelu") - << ConvolutionLayer( - 3U, 3U, 512U, - get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_64_w.npy", weights_layout), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 1, 1)) - .set_name("conv2d_64") - << BatchNormalizationLayer( - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_63_mean.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_63_var.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_63_gamma.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_63_beta.npy"), - 0.000001f) - .set_name("conv2d_63/BatchNorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_64/LeakyRelu") - << ConvolutionLayer( - 1U, 1U, 256U, - get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_65_w.npy", weights_layout), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name("conv2d_65") - << BatchNormalizationLayer( - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_64_mean.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_64_var.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_64_gamma.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_64_beta.npy"), - 0.000001f) - .set_name("conv2d_65/BatchNorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_65/LeakyRelu"); + concat_1 + << ConcatLayer(std::move(route_1), std::move(intermediate_layers.second)).set_name("Route1") + << ConvolutionLayer( + 1U, 1U, 256U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_61_w.npy", weights_layout), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0)) + .set_name("conv2d_61") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_60_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_60_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_60_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_60_beta.npy"), 0.000001f) + .set_name("conv2d_60/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)) + .set_name("conv2d_61/LeakyRelu") + << ConvolutionLayer( + 3U, 3U, 512U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_62_w.npy", weights_layout), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1)) + .set_name("conv2d_62") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_61_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_61_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_61_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_61_beta.npy"), 0.000001f) + .set_name("conv2d_61/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)) + .set_name("conv2d_62/LeakyRelu") + << ConvolutionLayer( + 1U, 1U, 256U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_63_w.npy", weights_layout), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0)) + .set_name("conv2d_63") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_62_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_62_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_62_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_62_beta.npy"), 0.000001f) + .set_name("conv2d_62/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)) + .set_name("conv2d_63/LeakyRelu") + << ConvolutionLayer( + 3U, 3U, 512U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_64_w.npy", weights_layout), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1)) + .set_name("conv2d_64") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_63_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_63_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_63_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_63_beta.npy"), 0.000001f) + .set_name("conv2d_63/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)) + .set_name("conv2d_64/LeakyRelu") + << ConvolutionLayer( + 1U, 1U, 256U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_65_w.npy", weights_layout), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0)) + .set_name("conv2d_65") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_64_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_64_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_64_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_64_beta.npy"), 0.000001f) + .set_name("conv2d_65/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)) + .set_name("conv2d_65/LeakyRelu"); SubStream route_2(concat_1); - concat_1 << ConvolutionLayer( - 3U, 3U, 512U, - get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_66_w.npy", weights_layout), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 1, 1)) - .set_name("conv2d_66") - << BatchNormalizationLayer( - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_65_mean.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_65_var.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_65_gamma.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_65_beta.npy"), - 0.000001f) - .set_name("conv2d_65/BatchNorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_66/LeakyRelu") - << ConvolutionLayer( - 1U, 1U, 255U, - get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_67_w.npy", weights_layout), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_67_b.npy", weights_layout), - PadStrideInfo(1, 1, 0, 0)) - .set_name("conv2d_67") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f)).set_name("conv2d_67/Linear") - << YOLOLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC, 0.1f)).set_name("Yolo2") - << OutputLayer(get_output_accessor(common_params, 5)); + concat_1 + << ConvolutionLayer( + 3U, 3U, 512U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_66_w.npy", weights_layout), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1)) + .set_name("conv2d_66") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_65_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_65_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_65_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_65_beta.npy"), 0.000001f) + .set_name("conv2d_65/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)) + .set_name("conv2d_66/LeakyRelu") + << ConvolutionLayer( + 1U, 1U, 255U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_67_w.npy", weights_layout), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_67_b.npy", weights_layout), + PadStrideInfo(1, 1, 0, 0)) + .set_name("conv2d_67") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f)) + .set_name("conv2d_67/Linear") + << YOLOLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC, 0.1f)).set_name("Yolo2") + << OutputLayer(get_output_accessor(common_params, 5)); route_2 << ConvolutionLayer( - 1U, 1U, 128U, - get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_68_w.npy", weights_layout), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name("conv2d_68") + 1U, 1U, 128U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_68_w.npy", weights_layout), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0)) + .set_name("conv2d_68") << BatchNormalizationLayer( - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_66_mean.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_66_var.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_66_gamma.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_66_beta.npy"), - 0.000001f) - .set_name("conv2d_66/BatchNorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_68/LeakyRelu") + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_66_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_66_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_66_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_66_beta.npy"), + 0.000001f) + .set_name("conv2d_66/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)) + .set_name("conv2d_68/LeakyRelu") << ResizeLayer(InterpolationPolicy::NEAREST_NEIGHBOR, 2, 2).set_name("Upsample_68"); SubStream concat_2(route_2); - concat_2 << ConcatLayer(std::move(route_2), std::move(intermediate_layers.first)).set_name("Route2") - << ConvolutionLayer( - 1U, 1U, 128U, - get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_69_w.npy", weights_layout), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name("conv2d_69") - << BatchNormalizationLayer( - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_67_mean.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_67_var.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_67_gamma.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_67_beta.npy"), - 0.000001f) - .set_name("conv2d_67/BatchNorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_69/LeakyRelu") - << ConvolutionLayer( - 3U, 3U, 256U, - get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_70_w.npy", weights_layout), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 1, 1)) - .set_name("conv2d_70") - << BatchNormalizationLayer( - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_68_mean.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_68_var.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_68_gamma.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_68_beta.npy"), - 0.000001f) - .set_name("conv2d_68/BatchNorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_70/LeakyRelu") - << ConvolutionLayer( - 1U, 1U, 128U, - get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_71_w.npy", weights_layout), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name("conv2d_71") - << BatchNormalizationLayer( - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_69_mean.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_69_var.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_69_gamma.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_69_beta.npy"), - 0.000001f) - .set_name("conv2d_69/BatchNorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_71/LeakyRelu") - << ConvolutionLayer( - 3U, 3U, 256U, - get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_72_w.npy", weights_layout), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 1, 1)) - .set_name("conv2d_72") - << BatchNormalizationLayer( - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_70_mean.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_70_var.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_70_gamma.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_70_beta.npy"), - 0.000001f) - .set_name("conv2d_70/BatchNorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_72/LeakyRelu") - << ConvolutionLayer( - 1U, 1U, 128U, - get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_73_w.npy", weights_layout), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name("conv2d_73") - << BatchNormalizationLayer( - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_71_mean.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_71_var.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_71_gamma.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_71_beta.npy"), - 0.000001f) - .set_name("conv2d_71/BatchNorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_73/LeakyRelu") - << ConvolutionLayer( - 3U, 3U, 256U, - get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_74_w.npy", weights_layout), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 1, 1)) - .set_name("conv2d_74") - << BatchNormalizationLayer( - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_72_mean.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_72_var.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_72_gamma.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_72_beta.npy"), - 0.000001f) - .set_name("conv2d_72/BatchNorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_74/LeakyRelu") - << ConvolutionLayer( - 1U, 1U, 255U, - get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_75_w.npy", weights_layout), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_75_b.npy", weights_layout), - PadStrideInfo(1, 1, 0, 0)) - .set_name("conv2d_75") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f)).set_name("conv2d_75/Linear") - << YOLOLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC, 0.1f)).set_name("Yolo3") - << OutputLayer(get_output_accessor(common_params, 5)); + concat_2 + << ConcatLayer(std::move(route_2), std::move(intermediate_layers.first)).set_name("Route2") + << ConvolutionLayer( + 1U, 1U, 128U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_69_w.npy", weights_layout), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0)) + .set_name("conv2d_69") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_67_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_67_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_67_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_67_beta.npy"), 0.000001f) + .set_name("conv2d_67/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)) + .set_name("conv2d_69/LeakyRelu") + << ConvolutionLayer( + 3U, 3U, 256U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_70_w.npy", weights_layout), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1)) + .set_name("conv2d_70") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_68_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_68_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_68_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_68_beta.npy"), 0.000001f) + .set_name("conv2d_68/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)) + .set_name("conv2d_70/LeakyRelu") + << ConvolutionLayer( + 1U, 1U, 128U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_71_w.npy", weights_layout), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0)) + .set_name("conv2d_71") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_69_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_69_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_69_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_69_beta.npy"), 0.000001f) + .set_name("conv2d_69/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)) + .set_name("conv2d_71/LeakyRelu") + << ConvolutionLayer( + 3U, 3U, 256U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_72_w.npy", weights_layout), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1)) + .set_name("conv2d_72") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_70_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_70_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_70_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_70_beta.npy"), 0.000001f) + .set_name("conv2d_70/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)) + .set_name("conv2d_72/LeakyRelu") + << ConvolutionLayer( + 1U, 1U, 128U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_73_w.npy", weights_layout), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0)) + .set_name("conv2d_73") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_71_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_71_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_71_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_71_beta.npy"), 0.000001f) + .set_name("conv2d_71/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)) + .set_name("conv2d_73/LeakyRelu") + << ConvolutionLayer( + 3U, 3U, 256U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_74_w.npy", weights_layout), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1)) + .set_name("conv2d_74") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_72_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_72_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_72_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_72_beta.npy"), 0.000001f) + .set_name("conv2d_72/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)) + .set_name("conv2d_74/LeakyRelu") + << ConvolutionLayer( + 1U, 1U, 255U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_75_w.npy", weights_layout), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_75_b.npy", weights_layout), + PadStrideInfo(1, 1, 0, 0)) + .set_name("conv2d_75") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f)) + .set_name("conv2d_75/Linear") + << YOLOLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC, 0.1f)).set_name("Yolo3") + << OutputLayer(get_output_accessor(common_params, 5)); // Finalize graph GraphConfig config; @@ -422,64 +414,64 @@ private: std::pair<SubStream, SubStream> darknet53(const std::string &data_path, DataLayout weights_layout) { graph << ConvolutionLayer( - 3U, 3U, 32U, - get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_1_w.npy", weights_layout), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 1, 1)) - .set_name("conv2d_1/Conv2D") + 3U, 3U, 32U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_1_w.npy", weights_layout), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1)) + .set_name("conv2d_1/Conv2D") << BatchNormalizationLayer( - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_1_mean.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_1_var.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_1_gamma.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_1_beta.npy"), - 0.000001f) - .set_name("conv2d_1/BatchNorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_1/LeakyRelu") + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_1_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_1_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_1_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_1_beta.npy"), + 0.000001f) + .set_name("conv2d_1/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)) + .set_name("conv2d_1/LeakyRelu") << ConvolutionLayer( - 3U, 3U, 64U, - get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_2_w.npy", weights_layout), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(2, 2, 1, 1)) - .set_name("conv2d_2/Conv2D") + 3U, 3U, 64U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_2_w.npy", weights_layout), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 1, 1)) + .set_name("conv2d_2/Conv2D") << BatchNormalizationLayer( - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_2_mean.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_2_var.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_2_gamma.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_2_beta.npy"), - 0.000001f) - .set_name("conv2d_2/BatchNorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_2/LeakyRelu"); + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_2_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_2_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_2_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_2_beta.npy"), + 0.000001f) + .set_name("conv2d_2/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)) + .set_name("conv2d_2/LeakyRelu"); darknet53_block(data_path, "3", weights_layout, 32U); graph << ConvolutionLayer( - 3U, 3U, 128U, - get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_5_w.npy", weights_layout), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(2, 2, 1, 1)) - .set_name("conv2d_5/Conv2D") + 3U, 3U, 128U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_5_w.npy", weights_layout), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 1, 1)) + .set_name("conv2d_5/Conv2D") << BatchNormalizationLayer( - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_5_mean.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_5_var.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_5_gamma.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_5_beta.npy"), - 0.000001f) - .set_name("conv2d_5/BatchNorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_5/LeakyRelu"); + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_5_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_5_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_5_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_5_beta.npy"), + 0.000001f) + .set_name("conv2d_5/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)) + .set_name("conv2d_5/LeakyRelu"); darknet53_block(data_path, "6", weights_layout, 64U); darknet53_block(data_path, "8", weights_layout, 64U); graph << ConvolutionLayer( - 3U, 3U, 256U, - get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_10_w.npy", weights_layout), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(2, 2, 1, 1)) - .set_name("conv2d_10/Conv2D") + 3U, 3U, 256U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_10_w.npy", weights_layout), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 1, 1)) + .set_name("conv2d_10/Conv2D") << BatchNormalizationLayer( - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_10_mean.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_10_var.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_10_gamma.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_10_beta.npy"), - 0.000001f) - .set_name("conv2d_10/BatchNorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_10/LeakyRelu"); + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_10_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_10_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_10_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_10_beta.npy"), + 0.000001f) + .set_name("conv2d_10/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)) + .set_name("conv2d_10/LeakyRelu"); darknet53_block(data_path, "11", weights_layout, 128U); darknet53_block(data_path, "13", weights_layout, 128U); darknet53_block(data_path, "15", weights_layout, 128U); @@ -490,19 +482,19 @@ private: darknet53_block(data_path, "25", weights_layout, 128U); SubStream layer_36(graph); graph << ConvolutionLayer( - 3U, 3U, 512U, - get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_27_w.npy", weights_layout), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(2, 2, 1, 1)) - .set_name("conv2d_27/Conv2D") + 3U, 3U, 512U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_27_w.npy", weights_layout), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 1, 1)) + .set_name("conv2d_27/Conv2D") << BatchNormalizationLayer( - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_27_mean.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_27_var.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_27_gamma.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_27_beta.npy"), - 0.000001f) - .set_name("conv2d_27/BatchNorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_27/LeakyRelu"); + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_27_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_27_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_27_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_27_beta.npy"), + 0.000001f) + .set_name("conv2d_27/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)) + .set_name("conv2d_27/LeakyRelu"); darknet53_block(data_path, "28", weights_layout, 256U); darknet53_block(data_path, "30", weights_layout, 256U); darknet53_block(data_path, "32", weights_layout, 256U); @@ -513,19 +505,19 @@ private: darknet53_block(data_path, "42", weights_layout, 256U); SubStream layer_61(graph); graph << ConvolutionLayer( - 3U, 3U, 1024U, - get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_44_w.npy", weights_layout), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(2, 2, 1, 1)) - .set_name("conv2d_44/Conv2D") + 3U, 3U, 1024U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_44_w.npy", weights_layout), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 1, 1)) + .set_name("conv2d_44/Conv2D") << BatchNormalizationLayer( - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_44_mean.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_44_var.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_44_gamma.npy"), - get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_44_beta.npy"), - 0.000001f) - .set_name("conv2d_44/BatchNorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_44/LeakyRelu"); + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_44_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_44_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_44_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_44_beta.npy"), + 0.000001f) + .set_name("conv2d_44/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)) + .set_name("conv2d_44/LeakyRelu"); darknet53_block(data_path, "45", weights_layout, 512U); darknet53_block(data_path, "47", weights_layout, 512U); darknet53_block(data_path, "49", weights_layout, 512U); @@ -534,43 +526,48 @@ private: return std::pair<SubStream, SubStream>(layer_36, layer_61); } - void darknet53_block(const std::string &data_path, std::string &¶m_path, DataLayout weights_layout, - unsigned int filter_size) + void darknet53_block(const std::string &data_path, + std::string &¶m_path, + DataLayout weights_layout, + unsigned int filter_size) { - std::string total_path = "/cnn_data/yolov3_model/"; - std::string param_path2 = arm_compute::support::cpp11::to_string(arm_compute::support::cpp11::stoi(param_path) + 1); - SubStream i_a(graph); - SubStream i_b(graph); + std::string total_path = "/cnn_data/yolov3_model/"; + std::string param_path2 = + arm_compute::support::cpp11::to_string(arm_compute::support::cpp11::stoi(param_path) + 1); + SubStream i_a(graph); + SubStream i_b(graph); i_a << ConvolutionLayer( - 1U, 1U, filter_size, - get_weights_accessor(data_path, total_path + "conv2d_" + param_path + "_w.npy", weights_layout), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name("conv2d_" + param_path + "/Conv2D") + 1U, 1U, filter_size, + get_weights_accessor(data_path, total_path + "conv2d_" + param_path + "_w.npy", weights_layout), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0)) + .set_name("conv2d_" + param_path + "/Conv2D") << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_mean.npy"), - get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_var.npy"), - get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_gamma.npy"), - get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_beta.npy"), - 0.000001f) - .set_name("conv2d_" + param_path + "/BatchNorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_" + param_path + "/LeakyRelu") + get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_mean.npy"), + get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_var.npy"), + get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_gamma.npy"), + get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_beta.npy"), + 0.000001f) + .set_name("conv2d_" + param_path + "/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)) + .set_name("conv2d_" + param_path + "/LeakyRelu") << ConvolutionLayer( - 3U, 3U, filter_size * 2, - get_weights_accessor(data_path, total_path + "conv2d_" + param_path2 + "_w.npy", weights_layout), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 1, 1)) - .set_name("conv2d_" + param_path2 + "/Conv2D") + 3U, 3U, filter_size * 2, + get_weights_accessor(data_path, total_path + "conv2d_" + param_path2 + "_w.npy", weights_layout), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1)) + .set_name("conv2d_" + param_path2 + "/Conv2D") << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_mean.npy"), - get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_var.npy"), - get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_gamma.npy"), - get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_beta.npy"), - 0.000001f) - .set_name("conv2d_" + param_path2 + "/BatchNorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_" + param_path2 + "/LeakyRelu"); + get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_mean.npy"), + get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_var.npy"), + get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_gamma.npy"), + get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_beta.npy"), + 0.000001f) + .set_name("conv2d_" + param_path2 + "/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)) + .set_name("conv2d_" + param_path2 + "/LeakyRelu"); - graph << EltwiseLayer(std::move(i_a), std::move(i_b), EltwiseOperation::Add).set_name("").set_name("add_" + param_path + "_" + param_path2); + graph << EltwiseLayer(std::move(i_a), std::move(i_b), EltwiseOperation::Add) + .set_name("") + .set_name("add_" + param_path + "_" + param_path2); } }; |