arm_compute v19.02
Change-Id: I853a3ecf38f206da13c1b03640c8adf73c20477c
diff --git a/examples/graph_yolov3.cpp b/examples/graph_yolov3.cpp
index 11d564c..6d0f67e 100644
--- a/examples/graph_yolov3.cpp
+++ b/examples/graph_yolov3.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2018 ARM Limited.
+ * Copyright (c) 2018-2019 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -170,7 +170,7 @@
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), 80)
+ << YOLOLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC, 0.1f), 80).set_name("Yolo1")
<< OutputLayer(get_output_accessor(common_params, 5));
route_1 << ConvolutionLayer(
1U, 1U, 256U,
@@ -188,7 +188,7 @@
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_60/LeakyRelu")
<< UpsampleLayer(Size2D(2, 2), InterpolationPolicy::NEAREST_NEIGHBOR).set_name("Upsample_60");
SubStream concat_1(route_1);
- concat_1 << ConcatLayer(std::move(route_1), std::move(intermediate_layers.second))
+ 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),
@@ -281,7 +281,7 @@
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), 80)
+ << YOLOLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC, 0.1f), 80).set_name("Yolo2")
<< OutputLayer(get_output_accessor(common_params, 5));
route_2 << ConvolutionLayer(
1U, 1U, 128U,
@@ -299,7 +299,7 @@
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_68/LeakyRelu")
<< UpsampleLayer(Size2D(2, 2), InterpolationPolicy::NEAREST_NEIGHBOR).set_name("Upsample_68");
SubStream concat_2(route_2);
- concat_2 << ConcatLayer(std::move(route_2), std::move(intermediate_layers.first))
+ 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),
@@ -391,7 +391,7 @@
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), 80)
+ << YOLOLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC, 0.1f), 80).set_name("Yolo3")
<< OutputLayer(get_output_accessor(common_params, 5));
// Finalize graph
@@ -423,7 +423,7 @@
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")
+ .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"),
@@ -437,7 +437,7 @@
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")
+ .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"),
@@ -452,7 +452,7 @@
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")
+ .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"),
@@ -468,7 +468,7 @@
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")
+ .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"),
@@ -491,7 +491,7 @@
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")
+ .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"),
@@ -514,7 +514,7 @@
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")
+ .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"),
@@ -543,6 +543,7 @@
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"),
@@ -550,12 +551,13 @@
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")
+ << 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")
<< 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"),
@@ -565,7 +567,7 @@
.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);
+ graph << EltwiseLayer(std::move(i_a), std::move(i_b), EltwiseOperation::Add).set_name("").set_name("add_" + param_path + "_" + param_path2);
}
};