godot/thirdparty/oidn/core/network.cpp
Juan Linietsky 1bea8e1eac New lightmapper
-Added LocalVector (needed it)
-Added stb_rect_pack (It's pretty cool, we could probably use it for other stuff too)
-Fixes and changes all around the place
-Added library for 128 bits fixed point (required for Delaunay3D)
2020-05-10 15:59:09 -03:00

435 lines
15 KiB
C++

// ======================================================================== //
// Copyright 2009-2019 Intel Corporation //
// //
// Licensed under the Apache License, Version 2.0 (the "License"); //
// you may not use this file except in compliance with the License. //
// You may obtain a copy of the License at //
// //
// http://www.apache.org/licenses/LICENSE-2.0 //
// //
// Unless required by applicable law or agreed to in writing, software //
// distributed under the License is distributed on an "AS IS" BASIS, //
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. //
// See the License for the specific language governing permissions and //
// limitations under the License. //
// ======================================================================== //
#include "network.h"
#include "upsample.h"
#include "weights_reorder.h"
#include <cstring>
namespace oidn {
template<int K>
Network<K>::Network(const Ref<Device>& device, const std::map<std::string, Tensor>& weightMap)
: device(device),
eng(engine::cpu, 0),
sm(eng),
weightMap(weightMap)
{
}
template<int K>
void Network<K>::execute(const Progress& progress, int taskIndex)
{
if (progress.func)
{
const double value = double(taskIndex) / double(progress.taskCount);
if (!progress.func(progress.userPtr, value))
throw Exception(Error::Cancelled, "execution was cancelled");
}
for (size_t i = 0; i < nodes.size(); ++i)
{
nodes[i]->execute(sm);
if (progress.func)
{
const double value = (double(taskIndex) + double(i+1) / double(nodes.size())) / double(progress.taskCount);
if (!progress.func(progress.userPtr, value))
throw Exception(Error::Cancelled, "execution was cancelled");
}
}
}
template<int K>
std::shared_ptr<memory> Network<K>::allocTensor(const memory::dims& dims,
memory::format_tag format,
void* data)
{
if (format == memory::format_tag::any)
{
if (dims.size() == 4)
format = BlockedFormat<K>::nChwKc;
else if (dims.size() == 1)
format = memory::format_tag::x;
else
assert(0);
}
memory::desc desc(dims, memory::data_type::f32, format);
if (data == nullptr)
{
const size_t bytes = getTensorSize(dims) * sizeof(float);
if (format == BlockedFormat<K>::nChwKc)
activationAllocBytes += bytes;
totalAllocBytes += bytes;
return std::make_shared<memory>(desc, eng);
}
else
{
return std::make_shared<memory>(desc, eng, data);
}
}
template<int K>
std::shared_ptr<memory> Network<K>::castTensor(const memory::dims& dims,
const std::shared_ptr<memory>& src,
size_t srcOffset,
memory::format_tag format)
{
const mkldnn_memory_desc_t& srcDesc = src->get_desc().data;
MAYBE_UNUSED(srcDesc);
assert(srcDesc.data_type == memory::data_type::f32);
assert(getTensorSize(src) >= srcOffset + getTensorSize(dims));
if (format == memory::format_tag::any)
{
if (dims.size() == 4)
format = BlockedFormat<K>::nChwKc;
else if (dims.size() == 1)
format = memory::format_tag::x;
else
assert(0);
}
memory::desc desc(dims, memory::data_type::f32, format);
float* srcPtr = (float*)src->get_data_handle() + srcOffset;
return std::make_shared<memory>(desc, eng, srcPtr);
}
template<int K>
std::shared_ptr<memory> Network<K>::castTensor(const memory::dims& dims,
const std::shared_ptr<memory>& src,
const memory::dims& srcOffset)
{
return castTensor(dims, src, getTensorSize(srcOffset));
}
template<int K>
void Network<K>::zeroTensor(const std::shared_ptr<memory>& dst)
{
assert(getTensorType(dst) == memory::data_type::f32);
memset(dst->get_data_handle(), 0, getTensorSize(dst)*sizeof(float));
}
template<int K>
memory::dims Network<K>::getInputReorderDims(const memory::dims& srcDims, int alignment)
{
memory::dims dstDims = srcDims;
dstDims[1] = getPadded<K>(srcDims[1]); // round up C
dstDims[2] = roundUp(srcDims[2], memory::dim(alignment)); // round up H
dstDims[3] = roundUp(srcDims[3], memory::dim(alignment)); // round up W
return dstDims;
}
template<int K>
std::shared_ptr<Node> Network<K>::addInputReorder(const Image& color,
const Image& albedo,
const Image& normal,
const std::shared_ptr<TransferFunction>& transferFunc,
int alignment,
const std::shared_ptr<memory>& userDst)
{
assert(color);
int inputC = 3;
if (albedo) inputC += 3;
if (normal) inputC += 3;
memory::dims srcDims = {1, inputC, color.height, color.width};
memory::dims dstDims = getInputReorderDims(srcDims, alignment);
// Allocate padded memory
auto dst = userDst;
if (!dst)
dst = allocTensor(dstDims);
// Push node
std::shared_ptr<Node> node;
if (auto tf = std::dynamic_pointer_cast<LinearTransferFunction>(transferFunc))
node = std::make_shared<InputReorderNode<K, LinearTransferFunction>>(color, albedo, normal, dst, tf);
else if (auto tf = std::dynamic_pointer_cast<GammaTransferFunction>(transferFunc))
node = std::make_shared<InputReorderNode<K, GammaTransferFunction>>(color, albedo, normal, dst, tf);
else if (auto tf = std::dynamic_pointer_cast<LogTransferFunction>(transferFunc))
node = std::make_shared<InputReorderNode<K, LogTransferFunction>>(color, albedo, normal, dst, tf);
else if (auto tf = std::dynamic_pointer_cast<PQXTransferFunction>(transferFunc))
node = std::make_shared<InputReorderNode<K, PQXTransferFunction>>(color, albedo, normal, dst, tf);
else
assert(0);
nodes.push_back(node);
return node;
}
template<int K>
std::shared_ptr<Node> Network<K>::addOutputReorder(const std::shared_ptr<memory>& src,
const std::shared_ptr<TransferFunction>& transferFunc,
const Image& output)
{
memory::dims srcDims = getTensorDims(src);
assert(srcDims[1] == K);
// Push node
std::shared_ptr<Node> node;
if (auto tf = std::dynamic_pointer_cast<LinearTransferFunction>(transferFunc))
node = std::make_shared<OutputReorderNode<K, LinearTransferFunction>>(src, output, tf);
else if (auto tf = std::dynamic_pointer_cast<GammaTransferFunction>(transferFunc))
node = std::make_shared<OutputReorderNode<K, GammaTransferFunction>>(src, output, tf);
else if (auto tf = std::dynamic_pointer_cast<LogTransferFunction>(transferFunc))
node = std::make_shared<OutputReorderNode<K, LogTransferFunction>>(src, output, tf);
else if (auto tf = std::dynamic_pointer_cast<PQXTransferFunction>(transferFunc))
node = std::make_shared<OutputReorderNode<K, PQXTransferFunction>>(src, output, tf);
else
assert(0);
nodes.push_back(node);
return node;
}
template<int K>
memory::dims Network<K>::getConvDims(const std::string& name, const memory::dims& srcDims)
{
auto b = weightMap[name + "/b"];
memory::dims dstDims = srcDims;
dstDims[1] = getPadded<K>(b.dims[0]); // dstDims[C] = getPadded(OC)
return dstDims;
}
template<int K>
std::shared_ptr<Node> Network<K>::addConv(const std::string& name,
const std::shared_ptr<memory>& src,
const std::shared_ptr<memory>& userDst,
bool relu)
{
const memory::dims strides = {1, 1};
const memory::dims padding = {1, 1};
memory::dims srcDims = getTensorDims(src);
// Get the weights
const auto& W = weightMap[name + "/W"];
if (W.ndims() != 4 || W.format != "oihw")
throw Exception(Error::InvalidOperation, "invalid convolution weights");
memory::dims weightsDims = W.dims;
auto userWeights = allocTensor(weightsDims, memory::format_tag::oihw, W.data);
// Pad the weights
memory::dims weightsPadDims = weightsDims;
weightsPadDims[1] = getPadded<K>(weightsDims[1]); // IC
weightsPadDims[0] = getPadded<K>(weightsDims[0]); // OC
assert(srcDims[1] == weightsPadDims[1]); // srcDims[C] == weightsPadDims[IC]
auto weightsPad = allocTensor(weightsPadDims, memory::format_tag::oihw);
WeightsReorderNode<K>(userWeights, weightsPad).execute(sm);
// Get the biases
const auto& b = weightMap[name + "/b"];
if (b.ndims() != 1)
throw Exception(Error::InvalidOperation, "invalid convolution biases");
memory::dims biasDims = b.dims;
// Copy/pad the biases
memory::dims biasPadDims = {getPadded<K>(biasDims[0])};
auto bias = allocTensor(biasPadDims);
if (biasDims[0] != biasPadDims[0])
memset(bias->get_data_handle(), 0, biasPadDims[0]*sizeof(float));
memcpy(bias->get_data_handle(), b.data, biasDims[0]*sizeof(float));
// Allocate memory for destination
memory::dims dstDims = srcDims;
dstDims[1] = weightsPadDims[0]; // dstDims[C] = weightsPadDims[OC]
std::shared_ptr<memory> dst;
if (!userDst)
dst = allocTensor(dstDims);
else if (getTensorDims(userDst) == dstDims)
dst = userDst;
else
dst = castTensor(dstDims, userDst);
// Create a convolution
// Let the convolution primitive choose the weights format
auto weightsDesc = memory::desc({ weightsPadDims }, memory::data_type::f32, memory::format_tag::any);
auto convAlgo = (K == 16) ? convolution_winograd : convolution_direct;
auto convDesc = convolution_forward::desc(
prop_kind::forward_inference, convAlgo,
src->get_desc(),
weightsDesc,
bias->get_desc(),
dst->get_desc(),
strides, padding, padding, padding_kind::zero);
// Incorporate relu
mkldnn::primitive_attr convAttr;
if (relu)
{
mkldnn::post_ops ops;
ops.append_eltwise(
1.f, // scale factor, not used
algorithm::eltwise_relu,
0.f, // max with
0.f // unused
);
convAttr.set_post_ops(ops);
}
convAttr.set_scratchpad_mode(scratchpad_mode_user);
auto convPrimDesc = convolution_forward::primitive_desc(convDesc, convAttr, eng);
// Reorder the weights to the final format, if necessary
auto weights = weightsPad;
if (convPrimDesc.weights_desc() != weightsPad->get_desc())
{
weights = std::make_shared<memory>(convPrimDesc.weights_desc(), eng);
ReorderNode(weightsPad, weights).execute(sm);
}
// Create convolution node and add it to the net
auto node = std::make_shared<ConvNode>(convPrimDesc, src, weights, bias, dst);
nodes.push_back(node);
return node;
}
template<int K>
memory::dims Network<K>::getPoolDims(const memory::dims& srcDims)
{
memory::dims dstDims = srcDims;
dstDims[2] /= 2; // H/2
dstDims[3] /= 2; // W/2
return dstDims;
}
template<int K>
std::shared_ptr<Node> Network<K>::addPool(const std::shared_ptr<memory>& src,
const std::shared_ptr<memory>& userDst)
{
const memory::dims kernel = {2, 2};
const memory::dims strides = {2, 2};
const memory::dims padding = {0, 0};
memory::dims srcDims = getTensorDims(src);
memory::dims dstDims = getPoolDims(srcDims);
std::shared_ptr<memory> dst;
if (!userDst)
dst = allocTensor(dstDims);
else if (getTensorDims(userDst) == dstDims)
dst = userDst;
else
dst = castTensor(dstDims, userDst);
auto poolDesc = pooling_forward::desc(
prop_kind::forward_inference, pooling_max,
src->get_desc(),
dst->get_desc(),
strides, kernel, padding, padding, padding_kind::zero);
mkldnn::primitive_attr poolAttr;
poolAttr.set_scratchpad_mode(scratchpad_mode_user);
auto poolPrimDesc = pooling_forward::primitive_desc(poolDesc, poolAttr, eng);
auto node = std::make_shared<PoolNode>(poolPrimDesc, src, dst);
nodes.push_back(node);
return node;
}
template<int K>
memory::dims Network<K>::getUpsampleDims(const memory::dims& srcDims)
{
memory::dims dstDims = srcDims;
dstDims[2] *= 2; // H*2
dstDims[3] *= 2; // W*2
return dstDims;
}
template<int K>
std::shared_ptr<Node> Network<K>::addUpsample(const std::shared_ptr<memory>& src,
const std::shared_ptr<memory>& userDst)
{
memory::dims srcDims = getTensorDims(src);
memory::dims dstDims = getUpsampleDims(srcDims);
std::shared_ptr<memory> dst;
if (!userDst)
dst = allocTensor(dstDims);
else if (getTensorDims(userDst) == dstDims)
dst = userDst;
else
dst = castTensor(dstDims, userDst);
// Create upsampling node and add it to net
auto node = std::make_shared<UpsampleNode<K>>(src, dst);
nodes.push_back(node);
return node;
}
template<int K>
memory::dims Network<K>::getConcatDims(const memory::dims& src1Dims, const memory::dims& src2Dims)
{
assert(src1Dims[0] == src2Dims[0]); // N
assert(src1Dims[2] == src2Dims[2]); // H
assert(src1Dims[3] == src2Dims[3]); // W
memory::dims dstDims = src1Dims;
dstDims[1] += src2Dims[1]; // C
return dstDims;
}
template<int K>
std::shared_ptr<Node> Network<K>::addAutoexposure(const Image& color,
const std::shared_ptr<HDRTransferFunction>& transferFunc)
{
auto node = std::make_shared<AutoexposureNode>(color, transferFunc);
nodes.push_back(node);
return node;
}
template <int K>
void Network<K>::finalize()
{
// Compute the size of the scratchpad
size_t scratchpadSize = 0;
for (const auto& node : nodes)
scratchpadSize = max(scratchpadSize, node->getScratchpadSize());
// Allocate the scratchpad
memory::dims scratchpadDims = { memory::dim(scratchpadSize) };
memory::desc scratchpadDesc(scratchpadDims, memory::data_type::u8, memory::format_tag::x);
auto scratchpad = std::make_shared<memory>(scratchpadDesc, eng);
activationAllocBytes += scratchpadSize;
totalAllocBytes += scratchpadSize;
// Set the scratchpad for the nodes
for (auto& node : nodes)
node->setScratchpad(scratchpad);
// Free the weights
weightMap.clear();
// Print statistics
if (device->isVerbose(2))
{
std::cout << "Activation bytes: " << activationAllocBytes << std::endl;
std::cout << "Scratchpad bytes: " << scratchpadSize << std::endl;
std::cout << "Total bytes : " << totalAllocBytes << std::endl;
}
}
template class Network<8>;
template class Network<16>;
} // namespace oidn