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yolov4ResourceBuilder.cpp
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yolov4ResourceBuilder.cpp
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#include "pch.h"
#include "yolov4.h"
#include "ATGColors.h"
#include "ControllerFont.h"
#include "FindMedia.h"
#include "ReadData.h"
#include "WeightLoader.h"
using Microsoft::WRL::ComPtr;
using namespace DirectX;
class YoloV4
{
public:
struct ModelOutputs
{
dml::Expression convSBBox;
dml::Expression convMBBox;
dml::Expression convLBBox;
};
explicit YoloV4(dml::Graph* graph, dml::Expression input, uint32_t numClasses)
: m_graph(graph)
, m_weightLoader(graph, 1)
{
m_modelOutputs = BuildModel(input, numClasses);
}
WeightData LoadWeightDataFromFile(const wchar_t* path, DX::DeviceResources* deviceResources)
{
return m_weightLoader.LoadWeightDataFromFile(path, deviceResources);
}
ModelOutputs GetModelOutputs() const
{
return m_modelOutputs;
}
private:
dml::Graph* m_graph;
ModelOutputs m_modelOutputs;
WeightLoader m_weightLoader;
private:
struct Backbone
{
dml::Expression route1;
dml::Expression route2;
dml::Expression conv;
};
enum class Activation
{
None,
LeakyRelu,
Mish,
};
static dml::Expression Mish(dml::Expression x)
{
return x * dml::ActivationTanh(dml::ActivationSoftplus(x));
}
dml::Expression Convolutional(
dml::Expression input,
dml::TensorDesc::Dimensions filterShape,
bool downsample = false,
bool hasBatchNorm = true,
Activation activation = Activation::LeakyRelu)
{
auto weights = m_weightLoader.RegisterConvWeights(filterShape, hasBatchNorm);
uint32_t filterHeight = weights.filter.GetOutputDesc().sizes[2];
uint32_t filterWidth = weights.filter.GetOutputDesc().sizes[3];
std::array<uint32_t, 2> padding = { filterHeight / 2, filterWidth / 2 };
std::array<uint32_t, 2> strides = {};
if (downsample)
{
strides = { 2, 2 };
}
else
{
strides = { 1, 1 };
}
dml::FusedActivation fusedActivation = dml::FusedActivation::None();
if (activation == Activation::LeakyRelu)
{
// LeakyRelu gets fused into the conv
fusedActivation = dml::FusedActivation::LeakyRelu(0.1f);
}
auto conv = dml::ConvolutionBuilder(input, weights.filter, weights.bias)
.StartPadding(padding)
.EndPadding(padding)
.Strides(strides)
.FusedActivation(fusedActivation)
.Build();
if (activation == Activation::Mish)
{
conv = Mish(conv);
}
return conv;
}
dml::Expression ResidualBlock(
dml::Expression input,
uint32_t inputChannel,
uint32_t filterCount1,
uint32_t filterCount2,
Activation activation)
{
auto shortcut = input;
auto conv = input;
conv = Convolutional(conv, { filterCount1, inputChannel, 1, 1 }, false, true, activation);
conv = Convolutional(conv, { filterCount2, filterCount1, 3, 3 }, false, true, activation);
return (shortcut + conv);
}
dml::Expression MaxPool(dml::Expression input, uint32_t windowHeight, uint32_t windowWidth)
{
uint32_t paddingH = windowHeight / 2;
uint32_t paddingW = windowWidth / 2;
auto [output, _] = dml::MaxPoolingBuilder(input, { windowHeight, windowWidth })
.Strides({ 1, 1 })
.StartPadding({ paddingH, paddingW })
.EndPadding({ paddingH, paddingW })
.Build();
return output;
}
dml::Expression Upsample(dml::Expression input)
{
return dml::Upsample2D(input, { 2, 2 }, DML_INTERPOLATION_MODE_NEAREST_NEIGHBOR);
}
Backbone CspDarknet53(dml::Expression input)
{
const uint32_t joinAxis = 1; // Concatenate along channels
dml::Expression route;
input = Convolutional(input, { 32, 3, 3, 3 }, false, true, Activation::Mish);
input = Convolutional(input, { 64, 32, 3, 3 }, true, true, Activation::Mish);
route = Convolutional(input, { 64, 64, 1, 1 }, false, true, Activation::Mish);
input = Convolutional(input, { 64, 64, 1, 1 }, false, true, Activation::Mish);
for (uint32_t i = 0; i < 1; ++i)
input = ResidualBlock(input, 64, 32, 64, Activation::Mish);
input = Convolutional(input, { 64, 64, 1, 1 }, false, true, Activation::Mish);
input = dml::Join({ input, route }, joinAxis);
input = Convolutional(input, { 64, 128, 1, 1 }, false, true, Activation::Mish);
input = Convolutional(input, { 128, 64, 3, 3 }, true, true, Activation::Mish);
route = Convolutional(input, { 64, 128, 1, 1 }, false, true, Activation::Mish);
input = Convolutional(input, { 64, 128, 1, 1 }, false, true, Activation::Mish);
for (uint32_t i = 0; i < 2; ++i)
input = ResidualBlock(input, 64, 64, 64, Activation::Mish);
input = Convolutional(input, { 64, 64, 1, 1 }, false, true, Activation::Mish);
input = dml::Join({ input, route }, joinAxis);
input = Convolutional(input, { 128, 128, 1, 1 }, false, true, Activation::Mish);
input = Convolutional(input, { 256, 128, 3, 3 }, true, true, Activation::Mish);
route = Convolutional(input, { 128, 256, 1, 1 }, false, true, Activation::Mish);
input = Convolutional(input, { 128, 256, 1, 1 }, false, true, Activation::Mish);
for (uint32_t i = 0; i < 8; ++i)
input = ResidualBlock(input, 128, 128, 128, Activation::Mish);
input = Convolutional(input, { 128, 128, 1, 1 }, false, true, Activation::Mish);
input = dml::Join({ input, route }, joinAxis);
input = Convolutional(input, { 256, 256, 1, 1 }, false, true, Activation::Mish);
auto route1 = input;
input = Convolutional(input, { 512, 256, 3, 3 }, true, true, Activation::Mish);
route = Convolutional(input, { 256, 512, 1, 1 }, false, true, Activation::Mish);
input = Convolutional(input, { 256, 512, 1, 1 }, false, true, Activation::Mish);
for (uint32_t i = 0; i < 8; ++i)
input = ResidualBlock(input, 256, 256, 256, Activation::Mish);
input = Convolutional(input, { 256, 256, 1, 1 }, false, true, Activation::Mish);
input = dml::Join({ input, route }, joinAxis);
input = Convolutional(input, { 512, 512, 1, 1 }, false, true, Activation::Mish);
auto route2 = input;
input = Convolutional(input, { 1024, 512, 3, 3 }, true, true, Activation::Mish);
route = Convolutional(input, { 512, 1024, 1, 1 }, false, true, Activation::Mish);
input = Convolutional(input, { 512, 1024, 1, 1 }, false, true, Activation::Mish);
for (uint32_t i = 0; i < 4; ++i)
input = ResidualBlock(input, 512, 512, 512, Activation::Mish);
input = Convolutional(input, { 512, 512, 1, 1 }, false, true, Activation::Mish);
input = dml::Join({ input, route }, joinAxis);
input = Convolutional(input, { 1024, 1024, 1, 1 }, false, true, Activation::Mish);
input = Convolutional(input, { 512, 1024, 1, 1 }, false, true, Activation::LeakyRelu);
input = Convolutional(input, { 1024, 512, 3, 3}, false, true, Activation::LeakyRelu);
input = Convolutional(input, { 512, 1024, 1, 1}, false, true, Activation::LeakyRelu);
auto pool1 = MaxPool(input, 13, 13);
auto pool2 = MaxPool(input, 9, 9);
auto pool3 = MaxPool(input, 5, 5);
input = dml::Join({ pool1, pool2, pool3, input }, joinAxis);
input = Convolutional(input, { 512, 2048, 1, 1});
input = Convolutional(input, { 1024, 512, 3, 3});
input = Convolutional(input, { 512, 1024, 1, 1});
return Backbone{ route1, route2, input };
}
ModelOutputs BuildModel(dml::Expression input, uint32_t numClasses)
{
auto [route1, route2, conv] = CspDarknet53(input);
auto route = conv;
const uint32_t joinAxis = 1; // Concatenate along channels
conv = Convolutional(conv, { 256, 512, 1, 1 });
conv = Upsample(conv);
route2 = Convolutional(route2, { 256, 512, 1, 1 });
conv = dml::Join({ route2, conv }, joinAxis);
conv = Convolutional(conv, { 256, 512, 1, 1 });
conv = Convolutional(conv, { 512, 256, 3, 3 });
conv = Convolutional(conv, { 256, 512, 1, 1 });
conv = Convolutional(conv, { 512, 256, 3, 3 });
conv = Convolutional(conv, { 256, 512, 1, 1 });
route2 = conv;
conv = Convolutional(conv, { 128, 256, 1, 1 });
conv = Upsample(conv);
route1 = Convolutional(route1, { 128, 256, 1, 1 });
conv = dml::Join({ route1, conv }, joinAxis);
conv = Convolutional(conv, { 128, 256, 1, 1 });
conv = Convolutional(conv, { 256, 128, 3, 3 });
conv = Convolutional(conv, { 128, 256, 1, 1 });
conv = Convolutional(conv, { 256, 128, 3, 3 });
conv = Convolutional(conv, { 128, 256, 1, 1 });
route1 = conv;
conv = Convolutional(conv, { 256, 128, 3, 3 });
auto convSBBox = Convolutional(conv, { 3 * (numClasses + 5), 256, 1, 1 }, false, false, Activation::None);
conv = Convolutional(route1, { 256, 128, 3, 3 }, true);
conv = dml::Join({ conv, route2 }, joinAxis);
conv = Convolutional(conv, { 256, 512, 1, 1 });
conv = Convolutional(conv, { 512, 256, 3, 3 });
conv = Convolutional(conv, { 256, 512, 1, 1 });
conv = Convolutional(conv, { 512, 256, 3, 3 });
conv = Convolutional(conv, { 256, 512, 1, 1 });
route2 = conv;
conv = Convolutional(conv, { 512, 256, 3, 3 });
auto convMBBox = Convolutional(conv, { 3 * (numClasses + 5), 512, 1, 1 }, false, false, Activation::None);
conv = Convolutional(route2, { 512, 256, 3, 3 }, true);
conv = dml::Join({ conv, route }, joinAxis);
conv = Convolutional(conv, { 512, 1024, 1, 1 });
conv = Convolutional(conv, { 1024, 512, 3, 3 });
conv = Convolutional(conv, { 512, 1024, 1, 1 });
conv = Convolutional(conv, { 1024, 512, 3, 3 });
conv = Convolutional(conv, { 512, 1024, 1, 1 });
conv = Convolutional(conv, { 1024, 512, 3, 3 });
auto convLBBox = Convolutional(conv, { 3 * (numClasses + 5), 1024, 1, 1 }, false, false, Activation::None);
return ModelOutputs{ convSBBox, convMBBox, convLBBox };
}
};
// Takes a tensor of size [1, 3 * (5 + numClasses), H, W] and returns a tensor of size [3, H, W, 7].
// Sigmoid activation is applied to all channels that represent probabilities (which are not all of them).
dml::Expression DecodeModelOutput(dml::Expression output, uint32_t numClasses)
{
const auto& outputSizes = output.GetOutputDesc().sizes;
assert(outputSizes.size() == 4); // Expect 4 dimensions
assert(outputSizes[0] == 1); // Expect batch of 1
assert(outputSizes[1] == 3 * (numClasses + 5)); // Expect # of channels to equal 3 * (numClasses+5)
assert(outputSizes[2] == outputSizes[3]); // Expect width == height
// Expand the channel into the batch, so that instead of:
// [1, 3 * (5 + numClasses), H, W]
// The shape is now:
// [3, 5 + numClasses, H, W]
// Since this doesn't transform the data any, this can be accomplished with a simple reinterpret.
output = dml::Reinterpret(output, { 3, numClasses + 5, outputSizes[2], outputSizes[3] }, dml::NullOpt);
// Split the new channel (of size 5+numClasses) into 4 different tensors with channels of 2, 2, 1, numClasses.
// These represent the box xy, box wh, confidence, and probabilities for each class.
const uint32_t channelDim = 1;
std::vector<dml::Expression> split = dml::Split(output, channelDim, { 2, 2, 1, numClasses });
assert(split.size() == 4);
// Convenience
auto convXy = split[0];
auto convWh = split[1];
auto convConf = split[2];
auto convProb = split[3];
// Apply final activations
convXy = dml::ActivationSigmoid(convXy);
convWh = dml::Exp(convWh);
convConf = dml::ActivationSigmoid(convConf);
convProb = dml::ActivationSigmoid(convProb);
// Compute the max and argmax of the probabilities. The argmax outputs UINT32 indices which
// are reinterpreted as float so they can be joined into the same output tensor.
auto convProbMax = dml::Reduce(convProb, DML_REDUCE_FUNCTION_MAX, { channelDim });
auto convProbArgMax = dml::Reduce(convProb, DML_REDUCE_FUNCTION_ARGMAX, { channelDim });
convProbArgMax = dml::Reinterpret(convProbArgMax, DML_TENSOR_DATA_TYPE_FLOAT32);
// Join the tensors along channel dimension.
auto joined = dml::Join({ convXy, convWh, convConf, convProbMax, convProbArgMax }, channelDim);
// Transpose from NCHW to NHWC for faster reading on the CPU (converts output from SoA to AoS).
dml::TensorDimensions sizesNchw = joined.GetOutputDesc().sizes;
dml::TensorDimensions sizesNhwc = { sizesNchw[0], sizesNchw[3], sizesNchw[2], sizesNchw[1] };
dml::TensorStrides stridesNhwc = { sizesNchw[1] * sizesNchw[2] * sizesNchw[3], sizesNchw[3], 1, sizesNchw[2] * sizesNchw[3] };
return dml::Identity(dml::Reinterpret(joined, sizesNhwc, stridesNhwc));
}
void Sample::CreateDirectMLResources()
{
auto device = m_deviceResources->GetD3DDevice();
// Shader for converting texture to tensor
{
auto computeShaderBlob = DX::ReadData(L"ImageToTensor.cso");
// Define root table layout
CD3DX12_DESCRIPTOR_RANGE descRange[2];
descRange[0].Init(D3D12_DESCRIPTOR_RANGE_TYPE_SRV, 1, 0); // t0
descRange[1].Init(D3D12_DESCRIPTOR_RANGE_TYPE_UAV, 1, 0); // u0
CD3DX12_ROOT_PARAMETER rootParameters[3];
rootParameters[e_crpIdxCB].InitAsConstants(3, 0);
rootParameters[e_crpIdxSRV].InitAsDescriptorTable(1, &descRange[0], D3D12_SHADER_VISIBILITY_ALL);
rootParameters[e_crpIdxUAV].InitAsDescriptorTable(1, &descRange[1], D3D12_SHADER_VISIBILITY_ALL);
CD3DX12_ROOT_SIGNATURE_DESC rootSignature(_countof(rootParameters), rootParameters);
ComPtr<ID3DBlob> serializedSignature;
DX::ThrowIfFailed(
D3D12SerializeRootSignature(&rootSignature, D3D_ROOT_SIGNATURE_VERSION_1, serializedSignature.GetAddressOf(), nullptr));
// Create the root signature
DX::ThrowIfFailed(
device->CreateRootSignature(
0,
serializedSignature->GetBufferPointer(),
serializedSignature->GetBufferSize(),
IID_PPV_ARGS(m_computeRootSignature.ReleaseAndGetAddressOf())));
m_computeRootSignature->SetName(L"Compute RS");
// Create compute pipeline state
D3D12_COMPUTE_PIPELINE_STATE_DESC descComputePSO = {};
descComputePSO.pRootSignature = m_computeRootSignature.Get();
descComputePSO.CS.pShaderBytecode = computeShaderBlob.data();
descComputePSO.CS.BytecodeLength = computeShaderBlob.size();
DX::ThrowIfFailed(
device->CreateComputePipelineState(&descComputePSO, IID_PPV_ARGS(m_computePSO.ReleaseAndGetAddressOf())));
m_computePSO->SetName(L"Compute PSO");
}
// Shader for rendering DML result tensor to texture
// This can also be done with a compute shader, depending on the app's needs.
{
auto vsShaderBlob = DX::ReadData(L"TensorToImageVS.cso");
auto psShaderBlob = DX::ReadData(L"TensorToImagePS.cso");
static const D3D12_INPUT_ELEMENT_DESC s_inputElementDesc[1] =
{
{ "POSITION", 0, DXGI_FORMAT_R32G32B32_FLOAT, 0, 0, D3D12_INPUT_CLASSIFICATION_PER_VERTEX_DATA, 0 },
};
// Define root table layout
CD3DX12_DESCRIPTOR_RANGE descRange[1];
descRange[0].Init(D3D12_DESCRIPTOR_RANGE_TYPE_SRV, 1, 0, 0, D3D12_DESCRIPTOR_RANGE_FLAG_NONE); // t0
CD3DX12_ROOT_PARAMETER rootParameters[2];
rootParameters[e_rrpIdxCB].InitAsConstants(3, 0, 0, D3D12_SHADER_VISIBILITY_PIXEL);
rootParameters[e_rrpIdxSRV].InitAsDescriptorTable(1, &descRange[0], D3D12_SHADER_VISIBILITY_PIXEL);
CD3DX12_ROOT_SIGNATURE_DESC rootSignature(_countof(rootParameters), rootParameters,
0, nullptr, D3D12_ROOT_SIGNATURE_FLAG_ALLOW_INPUT_ASSEMBLER_INPUT_LAYOUT);
ComPtr<ID3DBlob> serializedSignature;
DX::ThrowIfFailed(
D3D12SerializeRootSignature(&rootSignature, D3D_ROOT_SIGNATURE_VERSION_1, serializedSignature.GetAddressOf(), nullptr));
// Create the root signature
DX::ThrowIfFailed(
device->CreateRootSignature(
0,
serializedSignature->GetBufferPointer(),
serializedSignature->GetBufferSize(),
IID_PPV_ARGS(m_tensorRenderRootSignature.ReleaseAndGetAddressOf())));
m_tensorRenderRootSignature->SetName(L"Tensor Render RS");
// Create pipeline state
D3D12_GRAPHICS_PIPELINE_STATE_DESC psoDesc = {};
psoDesc.InputLayout = { s_inputElementDesc, _countof(s_inputElementDesc) };
psoDesc.pRootSignature = m_tensorRenderRootSignature.Get();
psoDesc.VS = { vsShaderBlob.data(), vsShaderBlob.size() };
psoDesc.PS = { psShaderBlob.data(), psShaderBlob.size() };
psoDesc.RasterizerState = CD3DX12_RASTERIZER_DESC(D3D12_DEFAULT);
psoDesc.BlendState = CD3DX12_BLEND_DESC(D3D12_DEFAULT);
psoDesc.DepthStencilState.DepthEnable = FALSE;
psoDesc.DepthStencilState.StencilEnable = FALSE;
psoDesc.DSVFormat = m_deviceResources->GetDepthBufferFormat();
psoDesc.SampleMask = UINT_MAX;
psoDesc.PrimitiveTopologyType = D3D12_PRIMITIVE_TOPOLOGY_TYPE_TRIANGLE;
psoDesc.NumRenderTargets = 1;
psoDesc.RTVFormats[0] = DXGI_FORMAT_B8G8R8A8_UNORM;
psoDesc.SampleDesc.Count = 1;
DX::ThrowIfFailed(
device->CreateGraphicsPipelineState(&psoDesc,
IID_PPV_ARGS(m_tensorRenderPipelineState.ReleaseAndGetAddressOf())));
m_tensorRenderPipelineState->SetName(L"Tensor Render PSO");
}
// DirectML device
{
#if _DEBUG
DX::ThrowIfFailed(DMLCreateDevice(device, DML_CREATE_DEVICE_FLAG_DEBUG, IID_PPV_ARGS(&m_dmlDevice)));
#else
DX::ThrowIfFailed(DMLCreateDevice(device, DML_CREATE_DEVICE_FLAG_NONE, IID_PPV_ARGS(&m_dmlDevice)));
#endif
DX::ThrowIfFailed(m_dmlDevice->CreateCommandRecorder(IID_PPV_ARGS(&m_dmlCommandRecorder)));
}
// Build the DirectML graph
{
dml::Graph graph(m_dmlDevice.Get());
dml::TensorDesc::Dimensions inputSizes = { 1, 3, m_origTextureHeight, m_origTextureWidth };
auto input = dml::InputTensor(graph, 0, dml::TensorDesc(DML_TENSOR_DATA_TYPE_FLOAT32, inputSizes));
uint64_t modelInputBufferSize = input.GetOutputDesc().totalTensorSizeInBytes;
// Bilinearly rescale the input image to 608x608, which is what yolov4 expects
auto modelInputSizes = { 1u, 3u, YoloV4Constants::c_inputHeight, YoloV4Constants::c_inputWidth };
input = dml::Resample(input, modelInputSizes, DML_INTERPOLATION_MODE_LINEAR);
// Construct the yolov4 model
YoloV4 model(&graph, input, YoloV4Constants::c_numClasses);
auto [convSBBox, convMBBox, convLBBox] = model.GetModelOutputs();
// Decode the outputs of the model
auto sbbox = DecodeModelOutput(convSBBox, YoloV4Constants::c_numClasses);
auto mbbox = DecodeModelOutput(convMBBox, YoloV4Constants::c_numClasses);
auto lbbox = DecodeModelOutput(convLBBox, YoloV4Constants::c_numClasses);
// Load the model weights from file
m_modelWeights = model.LoadWeightDataFromFile(LR"(.\Data\yolov4.weights)", m_deviceResources.get());
// Compile the model into a DML graph
DML_EXECUTION_FLAGS executionFlags = DML_EXECUTION_FLAG_ALLOW_HALF_PRECISION_COMPUTATION;
m_dmlGraph = graph.Compile(executionFlags, { sbbox, mbbox, lbbox });
// Buffers for DML inputs and outputs
// Resource for input tensor
DX::ThrowIfFailed(device->CreateCommittedResource(
&CD3DX12_HEAP_PROPERTIES(D3D12_HEAP_TYPE_DEFAULT),
D3D12_HEAP_FLAG_NONE,
&CD3DX12_RESOURCE_DESC::Buffer(modelInputBufferSize, D3D12_RESOURCE_FLAG_ALLOW_UNORDERED_ACCESS),
D3D12_RESOURCE_STATE_COMMON,
nullptr,
IID_PPV_ARGS(&m_modelInput)));
// Describe and create a UAV for the original input tensor.
D3D12_UNORDERED_ACCESS_VIEW_DESC uavDesc = {};
uavDesc.Format = DXGI_FORMAT_R32_FLOAT;
uavDesc.ViewDimension = D3D12_UAV_DIMENSION_BUFFER;
uavDesc.Buffer.FirstElement = 0;
uavDesc.Buffer.NumElements = static_cast<UINT>(modelInputBufferSize / sizeof(float));
uavDesc.Buffer.StructureByteStride = 0;
uavDesc.Buffer.CounterOffsetInBytes = 0;
uavDesc.Buffer.Flags = D3D12_BUFFER_UAV_FLAG_NONE;
device->CreateUnorderedAccessView(m_modelInput.Get(), nullptr, &uavDesc, m_SRVDescriptorHeap->GetCpuHandle(e_descModelInput));
// Create resources to hold the model outputs and to read them back from the GPU
m_modelSOutput.desc = sbbox.GetOutputDesc();
uint64_t sbboxResourceSize = m_modelSOutput.desc.totalTensorSizeInBytes;
DX::ThrowIfFailed(device->CreateCommittedResource(
&CD3DX12_HEAP_PROPERTIES(D3D12_HEAP_TYPE_DEFAULT),
D3D12_HEAP_FLAG_NONE,
&CD3DX12_RESOURCE_DESC::Buffer(sbboxResourceSize, D3D12_RESOURCE_FLAG_ALLOW_UNORDERED_ACCESS),
D3D12_RESOURCE_STATE_COMMON,
nullptr,
IID_PPV_ARGS(&m_modelSOutput.output)));
DX::ThrowIfFailed(device->CreateCommittedResource(
&CD3DX12_HEAP_PROPERTIES(D3D12_HEAP_TYPE_READBACK),
D3D12_HEAP_FLAG_NONE,
&CD3DX12_RESOURCE_DESC::Buffer(sbboxResourceSize),
D3D12_RESOURCE_STATE_COPY_DEST,
nullptr,
IID_PPV_ARGS(&m_modelSOutput.readback)));
m_modelMOutput.desc = mbbox.GetOutputDesc();
uint64_t mbboxResourceSize = m_modelMOutput.desc.totalTensorSizeInBytes;
DX::ThrowIfFailed(device->CreateCommittedResource(
&CD3DX12_HEAP_PROPERTIES(D3D12_HEAP_TYPE_DEFAULT),
D3D12_HEAP_FLAG_NONE,
&CD3DX12_RESOURCE_DESC::Buffer(mbboxResourceSize, D3D12_RESOURCE_FLAG_ALLOW_UNORDERED_ACCESS),
D3D12_RESOURCE_STATE_COMMON,
nullptr,
IID_PPV_ARGS(&m_modelMOutput.output)));
DX::ThrowIfFailed(device->CreateCommittedResource(
&CD3DX12_HEAP_PROPERTIES(D3D12_HEAP_TYPE_READBACK),
D3D12_HEAP_FLAG_NONE,
&CD3DX12_RESOURCE_DESC::Buffer(mbboxResourceSize),
D3D12_RESOURCE_STATE_COPY_DEST,
nullptr,
IID_PPV_ARGS(&m_modelMOutput.readback)));
m_modelLOutput.desc = lbbox.GetOutputDesc();
uint64_t lbboxResourceSize = m_modelLOutput.desc.totalTensorSizeInBytes;
DX::ThrowIfFailed(device->CreateCommittedResource(
&CD3DX12_HEAP_PROPERTIES(D3D12_HEAP_TYPE_DEFAULT),
D3D12_HEAP_FLAG_NONE,
&CD3DX12_RESOURCE_DESC::Buffer(lbboxResourceSize, D3D12_RESOURCE_FLAG_ALLOW_UNORDERED_ACCESS),
D3D12_RESOURCE_STATE_COMMON,
nullptr,
IID_PPV_ARGS(&m_modelLOutput.output)));
DX::ThrowIfFailed(device->CreateCommittedResource(
&CD3DX12_HEAP_PROPERTIES(D3D12_HEAP_TYPE_READBACK),
D3D12_HEAP_FLAG_NONE,
&CD3DX12_RESOURCE_DESC::Buffer(lbboxResourceSize),
D3D12_RESOURCE_STATE_COPY_DEST,
nullptr,
IID_PPV_ARGS(&m_modelLOutput.readback)));
}
}
void Sample::InitializeDirectMLResources()
{
auto commandList = m_deviceResources->GetCommandList();
commandList->Reset(m_deviceResources->GetCommandAllocator(), nullptr);
DX::ThrowIfFailed(m_dmlDevice->CreateOperatorInitializer(1, m_dmlGraph.GetAddressOf(), IID_PPV_ARGS(&m_dmlOpInitializer)));
DML_BINDING_PROPERTIES initBindingProps = m_dmlOpInitializer->GetBindingProperties();
DML_BINDING_PROPERTIES executeBindingProps = m_dmlGraph->GetBindingProperties();
m_dmlDescriptorHeap = std::make_unique<DescriptorHeap>(
m_deviceResources->GetD3DDevice(),
D3D12_DESCRIPTOR_HEAP_TYPE_CBV_SRV_UAV,
D3D12_DESCRIPTOR_HEAP_FLAG_SHADER_VISIBLE,
std::max(executeBindingProps.RequiredDescriptorCount, 1u));
auto initDescriptorHeap = std::make_unique<DescriptorHeap>(
m_deviceResources->GetD3DDevice(),
D3D12_DESCRIPTOR_HEAP_TYPE_CBV_SRV_UAV,
D3D12_DESCRIPTOR_HEAP_FLAG_SHADER_VISIBLE,
std::max(initBindingProps.RequiredDescriptorCount, 1u));
// Operator initialization dispatches will use this heap right away
ID3D12DescriptorHeap* pHeaps[] = { initDescriptorHeap->Heap() };
commandList->SetDescriptorHeaps(_countof(pHeaps), pHeaps);
// Create any persistent resources required for the operators.
if (executeBindingProps.PersistentResourceSize > 0)
{
D3D12_RESOURCE_DESC resourceDesc = CD3DX12_RESOURCE_DESC::Buffer(
executeBindingProps.PersistentResourceSize,
D3D12_RESOURCE_FLAG_ALLOW_UNORDERED_ACCESS);
DX::ThrowIfFailed(m_deviceResources->GetD3DDevice()->CreateCommittedResource(
&CD3DX12_HEAP_PROPERTIES(D3D12_HEAP_TYPE_DEFAULT),
D3D12_HEAP_FLAG_NONE,
&resourceDesc,
D3D12_RESOURCE_STATE_COMMON,
nullptr,
IID_PPV_ARGS(&m_modelPersistentResource)));
}
// Temporary resource for execution
if (executeBindingProps.TemporaryResourceSize > 0)
{
D3D12_RESOURCE_DESC resourceDesc = CD3DX12_RESOURCE_DESC::Buffer(
executeBindingProps.TemporaryResourceSize,
D3D12_RESOURCE_FLAG_ALLOW_UNORDERED_ACCESS);
DX::ThrowIfFailed(m_deviceResources->GetD3DDevice()->CreateCommittedResource(
&CD3DX12_HEAP_PROPERTIES(D3D12_HEAP_TYPE_DEFAULT),
D3D12_HEAP_FLAG_NONE,
&resourceDesc,
D3D12_RESOURCE_STATE_COMMON,
nullptr,
IID_PPV_ARGS(&m_modelTemporaryResource)));
}
// If the execute temporary resource isn't big enough for initialization, create a bigger buffer
ComPtr<ID3D12Resource> initTemporaryResource;
if (initBindingProps.TemporaryResourceSize > executeBindingProps.TemporaryResourceSize)
{
D3D12_RESOURCE_DESC resourceDesc = CD3DX12_RESOURCE_DESC::Buffer(
initBindingProps.TemporaryResourceSize,
D3D12_RESOURCE_FLAG_ALLOW_UNORDERED_ACCESS);
DX::ThrowIfFailed(m_deviceResources->GetD3DDevice()->CreateCommittedResource(
&CD3DX12_HEAP_PROPERTIES(D3D12_HEAP_TYPE_DEFAULT),
D3D12_HEAP_FLAG_NONE,
&resourceDesc,
D3D12_RESOURCE_STATE_COMMON,
nullptr,
IID_PPV_ARGS(&initTemporaryResource)));
}
else if (initBindingProps.TemporaryResourceSize > 0)
{
initTemporaryResource = m_modelTemporaryResource;
}
Microsoft::WRL::ComPtr<IDMLBindingTable> initBindingTable;
assert(initBindingProps.PersistentResourceSize == 0);
DML_BINDING_TABLE_DESC tableDesc =
{
m_dmlOpInitializer.Get(),
initDescriptorHeap->GetCpuHandle(0),
initDescriptorHeap->GetGpuHandle(0),
initBindingProps.RequiredDescriptorCount
};
DX::ThrowIfFailed(m_dmlDevice->CreateBindingTable(&tableDesc, IID_PPV_ARGS(&initBindingTable)));
// Create the binding table for execution
tableDesc =
{
m_dmlGraph.Get(),
m_dmlDescriptorHeap->GetCpuHandle(0),
m_dmlDescriptorHeap->GetGpuHandle(0),
executeBindingProps.RequiredDescriptorCount
};
DX::ThrowIfFailed(m_dmlDevice->CreateBindingTable(&tableDesc, IID_PPV_ARGS(&m_dmlBindingTable)));
DML_BUFFER_BINDING inputBufferBinding{ m_modelInput.Get(), 0, m_modelInput->GetDesc().Width };
dml::Span<const DML_BUFFER_BINDING> weightBufferBindings = m_modelWeights->GetBindings();
// Bind inputs for initialization, which is only necessary if we're using OWNED_BY_DML
#if DML_MANAGED_WEIGHTS
{
std::vector<DML_BUFFER_BINDING> initBufferBindings;
initBufferBindings.push_back(DML_BUFFER_BINDING{}); // Model input
initBufferBindings.insert(initBufferBindings.end(), weightBufferBindings.begin(), weightBufferBindings.end()); // Weights
DML_BUFFER_ARRAY_BINDING initInputBinding = { (UINT)initBufferBindings.size(), initBufferBindings.data() };
initBindingTable->BindInputs(1, &DML_BINDING_DESC{ DML_BINDING_TYPE_BUFFER_ARRAY, &initInputBinding });
}
#else
initBindingTable->BindInputs(0, nullptr);
#endif
if (initTemporaryResource)
{
DML_BUFFER_BINDING binding = { initTemporaryResource.Get(), 0, initTemporaryResource->GetDesc().Width };
initBindingTable->BindTemporaryResource(&DML_BINDING_DESC{ DML_BINDING_TYPE_BUFFER, &binding });
}
// If the operator requires a persistent resource, it must be bound as output for the initializer.
if (m_modelPersistentResource)
{
DML_BUFFER_BINDING binding = { m_modelPersistentResource.Get(), 0, m_modelPersistentResource->GetDesc().Width };
initBindingTable->BindOutputs(1, &DML_BINDING_DESC{ DML_BINDING_TYPE_BUFFER, &binding });
m_dmlBindingTable->BindPersistentResource(&DML_BINDING_DESC{ DML_BINDING_TYPE_BUFFER, &binding });
}
if (m_modelTemporaryResource)
{
DML_BUFFER_BINDING binding = { m_modelTemporaryResource.Get(), 0, m_modelTemporaryResource->GetDesc().Width };
m_dmlBindingTable->BindTemporaryResource(&DML_BINDING_DESC{ DML_BINDING_TYPE_BUFFER, &binding });
}
// Bind model inputs and outputs
std::vector<DML_BINDING_DESC> inputBindings(1 + weightBufferBindings.size());
#if DML_MANAGED_WEIGHTS
// Bind only the model input
inputBindings[0] = { DML_BINDING_TYPE_BUFFER, &inputBufferBinding };
m_dmlBindingTable->BindInputs((UINT)inputBindings.size(), inputBindings.data());
#else
// Bind everything
inputBindings[0] = { DML_BINDING_TYPE_BUFFER, &inputBufferBinding };
for (size_t i = 0; i < weightBufferBindings.size(); ++i)
{
inputBindings[i + 1] = { DML_BINDING_TYPE_BUFFER, &weightBufferBindings[i] };
}
m_dmlBindingTable->BindInputs((UINT)inputBindings.size(), inputBindings.data());
#endif
DML_BUFFER_BINDING outputBufferBindings[] =
{
{ m_modelSOutput.output.Get(), 0, m_modelSOutput.output->GetDesc().Width },
{ m_modelMOutput.output.Get(), 0, m_modelMOutput.output->GetDesc().Width },
{ m_modelLOutput.output.Get(), 0, m_modelLOutput.output->GetDesc().Width },
};
DML_BINDING_DESC outputBindings[] =
{
{ DML_BINDING_TYPE_BUFFER, &outputBufferBindings[0] },
{ DML_BINDING_TYPE_BUFFER, &outputBufferBindings[1] },
{ DML_BINDING_TYPE_BUFFER, &outputBufferBindings[2] },
};
m_dmlBindingTable->BindOutputs(ARRAYSIZE(outputBindings), outputBindings);
// Record the initialization
m_dmlCommandRecorder->RecordDispatch(commandList, m_dmlOpInitializer.Get(), initBindingTable.Get());
DX::ThrowIfFailed(commandList->Close());
m_deviceResources->GetCommandQueue()->ExecuteCommandLists(1, CommandListCast(&commandList));
// Wait until initialization has been finished on the GPU.
m_deviceResources->WaitForGpu();
#if DML_MANAGED_WEIGHTS
m_modelWeights.reset();
#endif
}