1 //
2 // Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
3 // SPDX-License-Identifier: MIT
4 //
5
6 #include "Pooling2DOperator.hpp"
7
ConvertPooling2DToTosaOperator(const Layer * layer,const std::vector<const TensorInfo * > & inputs,const std::vector<const TensorInfo * > & outputs,const Pooling2dDescriptor * poolDescriptor)8 TosaSerializationBasicBlock* ConvertPooling2DToTosaOperator(const Layer* layer,
9 const std::vector<const TensorInfo*>& inputs,
10 const std::vector<const TensorInfo*>& outputs,
11 const Pooling2dDescriptor* poolDescriptor)
12 {
13 std::string poolType = (poolDescriptor->m_PoolType == PoolingAlgorithm::Max) ? "Op_MAX" : "Op_AVG";
14 Op opcode = (poolDescriptor->m_PoolType == PoolingAlgorithm::Max) ? Op_MAX_POOL2D : Op_AVG_POOL2D;
15
16 std::string input0Name = std::string("input0_");
17 std::string outputName = std::string("output0_");
18 std::string blockName = std::string("Op_") + poolType + std::string("_POOL2D_block_") + GetUniqueTosaMappingID();
19
20 // If a layer is present then the block will be used for execution, so input and output names need to be determined
21 // using the previous and following layers so the graph is connected correctly. For validation this doesn't matter.
22 if(layer != nullptr)
23 {
24 // Get the layers connected to the input slots and determine unique tensor names.
25 Layer& connectedInputLayer = layer->GetInputSlot(0).GetConnectedOutputSlot()->GetOwningLayer();
26 input0Name = GenerateUniqueName(connectedInputLayer, 0);
27
28 // Determine unique output tensor name.
29 outputName = GenerateUniqueOutputName(*layer, 0);
30 }
31
32 std::vector<int> pad = {static_cast<int>(poolDescriptor->m_PadTop),
33 static_cast<int>(poolDescriptor->m_PadBottom),
34 static_cast<int>(poolDescriptor->m_PadLeft),
35 static_cast<int>(poolDescriptor->m_PadRight)};
36 std::vector<int> kernel = {static_cast<int>(poolDescriptor->m_PoolHeight),
37 static_cast<int>(poolDescriptor->m_PoolWidth)};
38 std::vector<int> stride = {static_cast<int>(poolDescriptor->m_StrideY),
39 static_cast<int>(poolDescriptor->m_StrideX)};
40 TosaPoolAttribute attribute(pad, kernel, stride, 0, 0, ArmNNToDType(inputs[0]->GetDataType()));
41
42 auto* op = new TosaSerializationOperator(opcode,
43 Attribute_PoolAttribute,
44 &attribute,
45 {input0Name},
46 {outputName});
47
48 std::vector<TosaSerializationTensor*> tensors;
49
50 // Only add input tensors if connected layer is an input layer.
51 // As intermediate or constant tensors will be created separately.
52 // There also can't be duplicate tensor.
53 if(input0Name.find("input0_") != std::string::npos)
54 {
55 std::vector<int32_t> inputShape0 = GetTosaTensorShape(inputs[0]->GetShape());
56 DType inputDType0 = ArmNNToDType(inputs[0]->GetDataType());
57
58 tensors.push_back(new TosaSerializationTensor(input0Name, inputShape0, inputDType0, {}));
59 }
60
61 std::vector<int32_t> outputShape0 = GetTosaTensorShape(outputs[0]->GetShape());
62 DType outputDType0 = ArmNNToDType(outputs[0]->GetDataType());
63
64 tensors.push_back(new TosaSerializationTensor(outputName, outputShape0, outputDType0, {}));
65
66 // operatorInputNames/operatorOutputNames ends up being the same as
67 // blockInputNames/blockOutputNames for one-to-one ArmNN to TOSA mappings
68 return new TosaSerializationBasicBlock(blockName, // name
69 {op}, // operators
70 tensors, // tensors
71 {input0Name}, // inputs
72 {outputName}); // outputs
73 }