arm_compute v18.02
Change-Id: I7207aa488e5470f235f39b6c188b4678dc38d1a6
diff --git a/src/graph/operations/NESimpleOperations.cpp b/src/graph/operations/NESimpleOperations.cpp
index 49adbe9..4154b9a 100644
--- a/src/graph/operations/NESimpleOperations.cpp
+++ b/src/graph/operations/NESimpleOperations.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -66,6 +66,34 @@
return std::move(activation);
}
+/* Arithmetic addition */
+REGISTER_SIMPLE_OPERATION(NEArithmeticAdditionOperation, NEON, OperationType::ArithmeticAddition)
+{
+ ARM_COMPUTE_ERROR_ON(ctx.num_inputs() != 2);
+ ARM_COMPUTE_ERROR_ON(ctx.num_outputs() != 1);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(0)) == nullptr);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(1)) == nullptr);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.output(0)) == nullptr);
+
+ // Extract IO and info
+ auto *in1 = dynamic_cast<arm_compute::ITensor *>(ctx.input(0));
+ auto *in2 = dynamic_cast<arm_compute::ITensor *>(ctx.input(1));
+ auto *out = dynamic_cast<arm_compute::ITensor *>(ctx.output(0));
+
+ auto addition = arm_compute::support::cpp14::make_unique<arm_compute::NEArithmeticAddition>();
+ addition->configure(in1, in2, out, ConvertPolicy::SATURATE);
+
+ // Log info
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEArithmeticAddition"
+ << " Data Type: " << in1->info()->data_type()
+ << " Input 1 shape: " << in1->info()->tensor_shape()
+ << " Input 2 shape: " << in2->info()->tensor_shape()
+ << " Output shape: " << out->info()->tensor_shape()
+ << std::endl);
+
+ return std::move(addition);
+}
+
/* Batch Normalization Layer */
REGISTER_SIMPLE_OPERATION(NEBatchNormalizationLayerOperation, NEON, OperationType::BatchNormalizationLayer)
{
@@ -79,17 +107,18 @@
ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.output(0)) == nullptr);
// Extract IO and info
- auto *in = dynamic_cast<arm_compute::ITensor *>(ctx.input(0));
- auto *mean = dynamic_cast<arm_compute::ITensor *>(ctx.input(1));
- auto *var = dynamic_cast<arm_compute::ITensor *>(ctx.input(2));
- auto *beta = dynamic_cast<arm_compute::ITensor *>(ctx.input(3));
- auto *gamma = dynamic_cast<arm_compute::ITensor *>(ctx.input(4));
- auto *out = dynamic_cast<arm_compute::ITensor *>(ctx.output(0));
- const auto epsilon = ctx.parameter<float>("epsilon");
+ auto *in = dynamic_cast<arm_compute::ITensor *>(ctx.input(0));
+ auto *mean = dynamic_cast<arm_compute::ITensor *>(ctx.input(1));
+ auto *var = dynamic_cast<arm_compute::ITensor *>(ctx.input(2));
+ auto *beta = dynamic_cast<arm_compute::ITensor *>(ctx.input(3));
+ auto *gamma = dynamic_cast<arm_compute::ITensor *>(ctx.input(4));
+ auto *out = dynamic_cast<arm_compute::ITensor *>(ctx.output(0));
+ const auto epsilon = ctx.parameter<float>("epsilon");
+ const auto act_info = ctx.parameter<ActivationLayerInfo>("act_info");
// Create and configure function
auto batch_norm = arm_compute::support::cpp14::make_unique<arm_compute::NEBatchNormalizationLayer>();
- batch_norm->configure(in, out, mean, var, beta, gamma, epsilon);
+ batch_norm->configure(in, out, mean, var, beta, gamma, epsilon, act_info);
// Log info
ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEBatchNormalizationLayer"
@@ -101,6 +130,9 @@
<< " Beta shape: " << beta->info()->tensor_shape()
<< " Gamma shape: " << gamma->info()->tensor_shape()
<< " Epsilon: " << epsilon
+ << " Activation function: " << act_info.activation()
+ << " a: " << act_info.a()
+ << " b: " << act_info.b()
<< std::endl);
return std::move(batch_norm);
@@ -149,12 +181,23 @@
auto *biases = ctx.num_inputs() == 3 ? dynamic_cast<arm_compute::ITensor *>(ctx.input(2)) : nullptr;
auto *out = dynamic_cast<arm_compute::ITensor *>(ctx.output(0));
const auto conv_info = ctx.parameter<PadStrideInfo>("ConvolutionInfo");
+ const auto opt3x3 = ctx.parameter<bool>("Optimized3x3");
// Create and configure function
std::unique_ptr<arm_compute::IFunction> func;
- auto depwthwise_conv = arm_compute::support::cpp14::make_unique<arm_compute::NEDepthwiseConvolutionLayer>();
- depwthwise_conv->configure(in, weights, biases, out, conv_info);
- func = std::move(depwthwise_conv);
+ bool run_3x3_opt = opt3x3 && weights->info()->dimension(0) == 3;
+ if(run_3x3_opt)
+ {
+ auto depwthwise_conv = arm_compute::support::cpp14::make_unique<arm_compute::NEDepthwiseConvolutionLayer3x3>();
+ depwthwise_conv->configure(in, weights, biases, out, conv_info);
+ func = std::move(depwthwise_conv);
+ }
+ else
+ {
+ auto depwthwise_conv = arm_compute::support::cpp14::make_unique<arm_compute::NEDepthwiseConvolutionLayer>();
+ depwthwise_conv->configure(in, weights, biases, out, conv_info);
+ func = std::move(depwthwise_conv);
+ }
// Log info
ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEDepthwiseConvolutionLayer"