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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "precomp.hpp"
#include "net_impl.hpp"
#ifdef HAVE_CUDA
#include "cuda4dnn/primitives/eltwise.hpp" // required by fuseLayers
#endif
namespace cv {
namespace dnn {
CV__DNN_INLINE_NS_BEGIN
void Net::Impl::enableFusion(bool fusion_)
{
if (fusion != fusion_)
{
fusion = fusion_;
clear();
}
}
#if 0
#define printf_(args) printf args
#else
#define printf_(args)
#endif
void Net::Impl::fuseLayers(const std::vector<LayerPin>& blobsToKeep_)
{
CV_TRACE_FUNCTION();
if(!fusion || (preferableBackend != DNN_BACKEND_OPENCV &&
preferableBackend != DNN_BACKEND_CUDA &&
preferableBackend != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH &&
preferableBackend != DNN_BACKEND_TIMVX &&
preferableBackend != DNN_BACKEND_VKCOM))
return;
#if 0 // FIXIT mode without fusion is broken due to unsupported layers and handling of "custom" nodes
if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return;
#endif
// scan through all the layers. If there is convolution layer followed by the activation layer,
// we try to embed this activation into the convolution and disable separate execution of the activation
// FIXIT replace by layersToKeep to avoid hacks like "LayerPin(lid, 0)"
std::set<LayerPin> pinsToKeep(blobsToKeep_.begin(),
blobsToKeep_.end());
for (MapIdToLayerData::const_iterator it = layers.begin(); it != layers.end(); it++)
{
int lid = it->first;
LayerData& ld = layers[lid];
if (ld.skip)
{
printf_(("skipped %s: %s\n", ld.layerInstance->name.c_str(), ld.layerInstance->type.c_str()));
continue;
}
printf_(("analyzing %s: %s\n", ld.layerInstance->name.c_str(), ld.layerInstance->type.c_str()));
// the optimization #1. try to fuse batch norm, scaling and/or activation layers
// with the current layer if they follow it. Normally, the are fused with the convolution layer,
// but some of them (like activation) may be fused with fully-connected, elemwise (+) and
// some other layers.
Ptr<Layer>& currLayer = ld.layerInstance;
if (ld.consumers.size() == 1 && pinsToKeep.count(LayerPin(lid, 0)) == 0)
{
LayerData* nextData = &layers[ld.consumers[0].lid];
LayerPin lpNext(ld.consumers[0].lid, 0);
while (nextData)
{
#ifdef HAVE_INF_ENGINE
if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && pinsToKeep.count(lpNext) != 0)
{
CV_LOG_DEBUG(NULL, "DNN/IE: skip fusing with 'output' node: " << nextData->name << "@" << nextData->type);
break;
}
#endif
/* we use `tryFuse` member of convolution layer to fuse eltwise/naryEltwise later
* it's not intended to be fused here; hence, we stop when we encounter eltwise
*/
if (preferableBackend == DNN_BACKEND_CUDA && ld.type == "Convolution" &&
(nextData->type == "Eltwise" || nextData->type == "NaryEltwise"))
break;
Ptr<Layer> nextLayer = nextData->layerInstance;
if (currLayer->tryFuse(nextLayer))
{
printf_(("\tfused with %s\n", nextLayer->name.c_str()));
nextData->skip = true;
ld.outputBlobs = layers[lpNext.lid].outputBlobs;
ld.outputBlobsWrappers = layers[lpNext.lid].outputBlobsWrappers;
if (nextData->consumers.size() == 1)
{
int nextLayerId = nextData->consumers[0].lid;
nextData = &layers[nextLayerId];
lpNext = LayerPin(nextLayerId, 0);
}
else
{
nextData = 0;
break;
}
}
else
break;
}
if (preferableBackend != DNN_BACKEND_OPENCV && preferableBackend != DNN_BACKEND_CUDA
&& preferableBackend != DNN_BACKEND_VKCOM)
continue; // Go to the next layer.
// TODO: OpenCL target support more fusion styles.
if ( preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget) &&
(!cv::ocl::useOpenCL() || (ld.layerInstance->type != "Convolution" &&
ld.layerInstance->type != "MVN" && ld.layerInstance->type != "Pooling" &&
ld.layerInstance->type != "Concat")) )
continue;
if (preferableBackend == DNN_BACKEND_CUDA && IS_DNN_CUDA_TARGET(preferableTarget)
&& ld.layerInstance->type != "Convolution"
&& ld.layerInstance->type != "Concat")
continue;
while (nextData)
{
// For now, OpenCL target support fusion with activation of ReLU/ChannelsPReLU/Power/Tanh
if (IS_DNN_OPENCL_TARGET(preferableTarget) &&
nextData->type != "ReLU" &&
nextData->type != "ChannelsPReLU" &&
nextData->type != "ReLU6" &&
nextData->type != "TanH" &&
nextData->type != "Power")
break;
Ptr<ActivationLayer> nextActivLayer = nextData->layerInstance.dynamicCast<ActivationLayer>();
if (nextActivLayer.empty())
break;
// For now, Vulkan target support fusion with activation of ReLU/ReLU6
if (IS_DNN_VULKAN_TARGET(preferableTarget))
{
if (nextData->type == "ReLU")
{
Ptr<ReLULayer> nextReLULayer = nextData->layerInstance.dynamicCast<ReLULayer>();
CV_Assert(nextReLULayer);
if (nextReLULayer->negativeSlope != 0.0f)
break; // Skip LeakyReLU
}
else if (nextData->type == "ReLU6")
{
Ptr<ReLU6Layer> nextReLU6Layer = nextData->layerInstance.dynamicCast<ReLU6Layer>();
CV_Assert(nextReLU6Layer);
if( fabs(nextReLU6Layer->minValue) > FLT_EPSILON || fabs(nextReLU6Layer->maxValue - 6.0f) > FLT_EPSILON)
break; // Skip ReLU6 if the minValue != 0 or maxValue != 6.
}
else
break;
}
if (currLayer->setActivation(nextActivLayer))
{
printf_(("\tfused with %s\n", nextActivLayer->name.c_str()));
nextData->skip = true;
ld.outputBlobs = layers[lpNext.lid].outputBlobs;
ld.outputBlobsWrappers = layers[lpNext.lid].outputBlobsWrappers;
if (nextData->consumers.size() == 1)
{
int nextLayerId = nextData->consumers[0].lid;
nextData = &layers[nextLayerId];
lpNext = LayerPin(nextLayerId, 0);
}
else
{
nextData = 0;
break;
}
}
else
break;
}
// CPU: fuse Convolution 2D layer followed by Add + activation.
while (nextData && (IS_DNN_CPU_TARGET(preferableTarget)) && ld.layerInstance->type == "Convolution")
{
// Note that we can only deal with conv + Add + activ here.
// To avoid the order like: conv + activ + add, if we found the conv has been fused with activ, we break.
Ptr<ConvolutionLayer> convLayer = ld.layerInstance.dynamicCast<ConvolutionLayer>();
// Only Convolution layer without fusion Activation supports this fusion, other-wise, we skip.
if (convLayer->fusedActivation)
break;
// For now, there are currently two layers in OpenCV that run the Add operator.
Ptr<NaryEltwiseLayer> nextNaryEltwiseLayer = nextData->layerInstance.dynamicCast<NaryEltwiseLayer>();
Ptr<EltwiseLayer> nextEltwiseLayer = nextData->layerInstance.dynamicCast<EltwiseLayer>();
if (nextNaryEltwiseLayer.empty() && nextEltwiseLayer.empty())
break;
if (nextData->inputBlobsId.size() != 2)
break;
if (!nextData->params.has("operation") || toLowerCase(nextData->params.get<String>("operation")) != "add")
{
CV_LOG_DEBUG(NULL, "DNN/CPU: fusion with NaryEltwise or Eltwise Layer operation is not supported: "
<< toLowerCase(nextData->params.get<String>("operation", "sum")));
break;
}
// This optimization is for cases like
// some_layer conv
// | |
// +-- eltwise or (naryEltwise) --+
// |
// activ
// This way all the element-wise computations
// (i.e. some_layer+conv) would be done at [conv] layer.
// So we need to replace [conv]'s output blob to [eltwise]'s one
// considering that [activ] is an in-place layer.
// Also we need to move all the consumers' references.
// To prevent memory collisions (i.e. when input of
// [conv] and output of [eltwise or naryEltwise] is the same blob)
// we allocate a new blob.
{
LayerData *naryOrEltwiseData = nextData;
// Eltwise or NaryEltwise layer has two inputs. We need to determine which
// is a base convolution layer and which could be used as it's bias.
LayerData* biasLayerData = 0;
for (int i = 0; i < 2; ++i)
{
LayerData *downLayerData = &layers[naryOrEltwiseData->inputBlobsId[i].lid];
CV_Assert(downLayerData);
// If the current downLayerData is skip, it means it is fused into the parent node.
while (downLayerData->skip)
{
if (downLayerData->inputBlobsId.size() == 1)
downLayerData = &layers[downLayerData->inputBlobsId[0].lid];
else
{
downLayerData = 0;
break;
}
}
if (downLayerData && ld.id == downLayerData->id)
{
biasLayerData = &layers[naryOrEltwiseData->inputBlobsId[1 - i].lid];
break;
}
}
// We check if biasLayerData is expected layer.
if (!biasLayerData)
break;
// We check if the bias output shape and the ld output shape are the same.
MatShape biasOutShape = shape(biasLayerData->outputBlobs[0]);
MatShape ldOutShape = shape(ld.outputBlobs[0]);
if (biasOutShape != ldOutShape)
break;
CV_Assert(biasLayerData);
{
// fuse naryEltwise layer
// bias must already be computed to fuse => bias layer must appear before convolution
if (biasLayerData->id < ld.id && biasLayerData->consumers.size() == 1)
{
// conv + naryEltwise.
CV_Assert_N(biasLayerData->outputBlobs.size() == 1, ld.inputBlobs.size() == 1);
CV_Assert_N(biasLayerData->outputBlobsWrappers.size() == 1, ld.inputBlobsWrappers.size() == 1);
printf_(("\tfused with %s\n", nextNaryEltwiseLayer->name.c_str()));
naryOrEltwiseData->skip = true;
CV_Assert_N(ld.outputBlobs.size() == 1, ld.outputBlobsWrappers.size() == 1);
// Note: Here's a trick. We set the output of conv as the output of biasLayer.
ld.outputBlobs[0] = ld.outputBlobs[0].clone();
ld.outputBlobsWrappers[0] = wrap(ld.outputBlobs[0]);
// Recursively modifies the output data of biasLayerData and its parent.
std::vector<LayerData*> skipDataList;
skipDataList.push_back(biasLayerData);
while (!skipDataList.empty())
{
LayerData* skipData = skipDataList.back();
skipDataList.pop_back();
CV_Assert(skipData->outputBlobs.size() == 1);
skipData->outputBlobs[0] = ld.outputBlobs[0];
skipData->outputBlobsWrappers[0] = ld.outputBlobsWrappers[0];
if (skipData->skip)
{
for (auto& inputLayerId : skipData->inputLayersId)
{
LayerData* inputld = &layers[inputLayerId];
if (inputld && inputld->outputBlobs.size() == 1)
skipDataList.push_back(inputld);
}
}
}
naryOrEltwiseData->outputBlobs = ld.outputBlobs;
naryOrEltwiseData->outputBlobsWrappers = ld.outputBlobsWrappers;
// set the fusedAdd flag in [Conv];
convLayer->fusedAdd = true;
LayerData* finalData = naryOrEltwiseData;
/* After fused Conv + naryEltwise or eltwise, we can fuse activation if:
* => activation layer that follows is the only consumer of eltwise output
* => activation layer does not process multiple inputs
* => we do not require to keep the output of eltwise
*/
if (naryOrEltwiseData->consumers.size() == 1)
{
Ptr<ActivationLayer> nextFusabeleActivLayer;
LayerData* nextAct = &layers[naryOrEltwiseData->consumers[0].lid];
if (nextData->outputBlobs.size() == 1)
nextFusabeleActivLayer = nextAct->layerInstance.dynamicCast<ActivationLayer>();
if (!nextFusabeleActivLayer.empty())
{
convLayer->setActivation(nextFusabeleActivLayer);
nextAct->skip = true;
nextAct->outputBlobs = ld.outputBlobs;
nextAct->outputBlobsWrappers = ld.outputBlobsWrappers;
}
}
// Move references of finalData (eltwise or activation) layer consumers to the newly allocated blob.
for (int i = 0; i < finalData->consumers.size(); ++i)
{
LayerData& consumer = layers[finalData->consumers[i].lid];
for (int j = 0; j < consumer.inputBlobsId.size(); ++j)
{
if (consumer.inputBlobsId[j].lid == finalData->id)
{
consumer.inputBlobs[j] = &ld.outputBlobs[0];
consumer.inputBlobsWrappers[j] = ld.outputBlobsWrappers[0];
break;
}
}
}
}
}
}
break;
}
// OpenCL: fuse convolution layer followed by eltwise + relu
// CUDA: fuse convolution layer followed by eltwise/naryEltwise (and optional activation)
while (nextData &&
(IS_DNN_OPENCL_TARGET(preferableTarget) || IS_DNN_CUDA_TARGET(preferableTarget)) &&
ld.layerInstance->type == "Convolution"
) // semantic of 'if'
{
Ptr<EltwiseLayer> nextEltwiseLayer = nextData->layerInstance.dynamicCast<EltwiseLayer>();
Ptr<NaryEltwiseLayer> nextNaryEltwiseLayer = nextData->layerInstance.dynamicCast<NaryEltwiseLayer>();
if (nextEltwiseLayer.empty() && nextNaryEltwiseLayer.empty())
break;
// TODO: fused the Conv+NaryEltwise on OpenCL backend. At present, we can only support it at CUDA backend.
if (IS_DNN_OPENCL_TARGET(preferableTarget) && nextNaryEltwiseLayer)
break;
#ifdef HAVE_CUDA
// CUDA backend supports fusion with eltwise sum (without variable channels)
if (IS_DNN_CUDA_TARGET(preferableTarget) && (!nextEltwiseLayer.empty() || !nextNaryEltwiseLayer.empty()))
{
// we create a temporary backend node for eltwise layer to obtain the eltwise configuration
cuda4dnn::csl::CSLContext context; // assume that initCUDA and EltwiseOp do not use the context during init
if (!nextData->layerInstance->supportBackend(DNN_BACKEND_CUDA))
break;
const auto node = nextData->layerInstance->initCUDA(&context, nextData->inputBlobsWrappers, nextData->outputBlobsWrappers);
auto eltwiseNode = node.dynamicCast<cuda4dnn::EltwiseOpBase>();
// broadcasting not supported in fused ops
auto required_shape = shape(nextData->outputBlobs[0]);
for (int i = 0; i < nextData->inputBlobs.size(); i++)
{
if (shape(*nextData->inputBlobs[i]) != required_shape)
{
eltwiseNode.reset();
break;
}
}
// CUDA backend uses EltwiseOp when all operands have the same number of channels; otherwise, ShortcutOp is used.
// Hence, a successful cast to EltwiseOp implies that the number of channels is same in all operand tensors.
if (eltwiseNode.empty() || eltwiseNode->op != cuda4dnn::EltwiseOpType::SUM || !eltwiseNode->coeffs.empty())
break;
}
#endif
if (IS_DNN_OPENCL_TARGET(preferableTarget) && pinsToKeep.count(lpNext) != 0)
break;
if (nextData->inputBlobsId.size() != 2)
break;
if (IS_DNN_OPENCL_TARGET(preferableTarget))
{
if (!nextData->params.has("operation") || toLowerCase(nextData->params.get<String>("operation")) == "sum")
{
if (nextData->params.has("coeff"))
{
DictValue paramCoeff = nextData->params.get("coeff");
int n = paramCoeff.size();
bool isCoeffOneOne = (n == 2);
for (int i = 0; isCoeffOneOne && i < n; i++)
{
float c = paramCoeff.get<float>(i);
isCoeffOneOne &= (c == 1.0f);
}
if (!isCoeffOneOne)
{
CV_LOG_DEBUG(NULL, "DNN/OpenCL: fusion of 'Sum' without coeffs (or {1.0, 1.0}) is supported only");
break;
}
}
}
else
{
CV_LOG_DEBUG(NULL, "DNN/OpenCL: fusion with eltwise operation is not supported: " << nextData->params.get<String>("operation"));
break;
}
}
{
LayerData *eltwiseData = nextData;
// Eltwise/NaryEltwise layer has two inputs. We need to determine which
// is a base convolution layer and which could be used as it's bias.
LayerData* biasLayerData = 0;
for (int i = 0; i < 2; ++i)
{
LayerData *downLayerData = &layers[eltwiseData->inputBlobsId[i].lid];
CV_Assert(downLayerData);
while (downLayerData->skip)
{
if (downLayerData->inputBlobsId.size() == 1)
downLayerData = &layers[downLayerData->inputBlobsId[0].lid];
else
{
downLayerData = 0;
break;
}
}
if (downLayerData && ld.id == downLayerData->id)
{
biasLayerData = &layers[eltwiseData->inputBlobsId[1 - i].lid];
break;
}
}
CV_Assert(biasLayerData);
{
// fuse eltwise + activation layer
// bias must already be computed to fuse => bias layer must appear before convolution
if (biasLayerData->id < ld.id)
{
/* we can fuse activation if:
* => activation layer that follows is the only consumer of eltwise output
* => activation layer does not process multiple inputs
* => we do not require to keep the output of eltwise
*/
Ptr<ActivationLayer> nextFusabeleActivLayer;
if (eltwiseData->consumers.size() == 1 && pinsToKeep.count(lpNext) == 0)
{
nextData = &layers[eltwiseData->consumers[0].lid];
lpNext = LayerPin(eltwiseData->consumers[0].lid, 0);
CV_Assert(nextData);
if (nextData->outputBlobs.size() == 1)
nextFusabeleActivLayer = nextData->layerInstance.dynamicCast<ActivationLayer>();
}
else
{
// OCL backend cannot fuse in this case but the CUDA backend can continue with just eltwise
nextData = 0;
}
// the requirements of OCV OpenCL backend and CUDA backend are different
// we need to check them separately; hence, the fuse variables
bool fuse_eltwise = false, fuse_activation = false;
Ptr<PowerLayer> activ_power;
if (IS_DNN_OPENCL_TARGET(preferableTarget) && !nextFusabeleActivLayer.empty() &&
nextData &&
(!nextData->type.compare("ReLU") ||
!nextData->type.compare("ChannelsPReLU") ||
(!nextData->type.compare("Power") && (activ_power = nextFusabeleActivLayer.dynamicCast<PowerLayer>()) && activ_power->scale == 1.0f)
) &&
currLayer->setActivation(nextFusabeleActivLayer))
{
fuse_eltwise = true;
fuse_activation = true;
}
if (IS_DNN_CUDA_TARGET(preferableTarget))
{
/* supported fusion options:
* => convolution + eltwise
* => activation(convolution) + eltwise
* > convolution + activation would have been fused already; we have to fuse eltwise
* => activation(convolution + eltwise)
* > fuse eltwise and then activation
*/
Ptr<Layer> layer = nullptr;
if (nextNaryEltwiseLayer)
layer = nextNaryEltwiseLayer.staticCast<Layer>();
else if (nextEltwiseLayer)
layer = nextEltwiseLayer.staticCast<Layer>();
else
CV_Error(Error::StsError, "Both nextNaryEltwiseLayer and nextEltwiseLayer are empty!");
if (currLayer->tryFuse(layer))
{
fuse_eltwise = true; /* eltwise was successfully fused */
if (!nextFusabeleActivLayer.empty() && nextData)
{
if ((!nextData->type.compare("ReLU") ||
!nextData->type.compare("ReLU6") ||
!nextData->type.compare("Power") ||
!nextData->type.compare("TanH") ||
!nextData->type.compare("Sigmoid") ||
!nextData->type.compare("Swish") ||
!nextData->type.compare("Mish")) &&
currLayer->setActivation(nextFusabeleActivLayer))
{
// activation was fused
fuse_activation = true;
}
}
}
}
CV_Assert(!fuse_activation || fuse_eltwise); /* cannot fuse activation without eltwise */
if(fuse_eltwise && fuse_activation)
{
CV_Assert(nextData);
CV_Assert_N(biasLayerData->outputBlobsWrappers.size() == 1, ld.inputBlobsWrappers.size() == 1);
ld.inputBlobsWrappers.push_back(biasLayerData->outputBlobsWrappers[0]);
if (nextEltwiseLayer)
printf_(("\tfused with %s\n", nextEltwiseLayer->name.c_str()));
else if (nextNaryEltwiseLayer)
printf_(("\tfused with %s\n", nextEltwiseLayer->name.c_str()));
else
CV_Error(Error::StsError, "Both nextNaryEltwiseLayer and nextEltwiseLayer are empty!");
printf_(("\tfused with %s\n", nextFusabeleActivLayer->name.c_str()));
eltwiseData->skip = true;
nextData->skip = true;
// This optimization for cases like
// some_layer conv
// | |
// +-- eltwise --+
// |
// activ
// This way all the element-wise computations
// (i.e. some_layer+conv or some_layer*conv)
// would be done at [conv] layer. So we need to
// replace [conv]'s output blob to [eltwise]'s one
// considering that [activ] is an in-place layer.
// Also we need to move all the consumers' references.
// To prevent memory collisions (i.e. when input of
// [conv] and output of [eltwise] is the same blob)
// we allocate a new blob.
CV_Assert_N(ld.outputBlobs.size() == 1, ld.outputBlobsWrappers.size() == 1);
ld.outputBlobs[0] = ld.outputBlobs[0].clone();
ld.outputBlobsWrappers[0] = wrap(ld.outputBlobs[0]);
eltwiseData->outputBlobs = ld.outputBlobs;
nextData->outputBlobs = ld.outputBlobs;
eltwiseData->outputBlobsWrappers = ld.outputBlobsWrappers;
nextData->outputBlobsWrappers = ld.outputBlobsWrappers;
// Move references of [activ] layer consumers to the newly allocated blob.
for (int i = 0; i < nextData->consumers.size(); ++i)
{
LayerData& consumer = layers[nextData->consumers[i].lid];
for (int j = 0; j < consumer.inputBlobsId.size(); ++j)
{
if (consumer.inputBlobsId[j].lid == lpNext.lid)
{
consumer.inputBlobs[j] = &ld.outputBlobs[0];
consumer.inputBlobsWrappers[j] = ld.outputBlobsWrappers[0];
break;
}
}
}
}
else if (fuse_eltwise) // conv + eltwise/naryEltwise (note: conv could have fused activations before eltwise)
{
CV_Assert(IS_DNN_CUDA_TARGET(preferableTarget));
CV_Assert_N(biasLayerData->outputBlobsWrappers.size() == 1, ld.inputBlobsWrappers.size() == 1);
ld.inputBlobsWrappers.push_back(biasLayerData->outputBlobsWrappers[0]);
if (nextEltwiseLayer)
printf_(("\tfused with %s\n", nextEltwiseLayer->name.c_str()));
else if (nextNaryEltwiseLayer)
printf_(("\tfused with %s\n", nextEltwiseLayer->name.c_str()));
else
CV_Error(Error::StsError, "Both nextNaryEltwiseLayer and nextEltwiseLayer are empty!");
eltwiseData->skip = true;
// This optimization is for cases like
// some_layer conv (maybe fused with activ)
// | |
// +-- eltwise --+
//
// This way all the element-wise computations
// (i.e. some_layer+conv or some_layer*conv)
// would be done at [conv] layer. So we need to
// replace [conv]'s output blob to [eltwise]'s one.
// Also, we need to move all the consumers' references.
// To prevent memory collisions (i.e. when input of
// [conv] and output of [eltwise] is the same blob)
// we allocate a new blob.
CV_Assert_N(ld.outputBlobs.size() == 1, ld.outputBlobsWrappers.size() == 1);
ld.outputBlobs[0] = ld.outputBlobs[0].clone();
ld.outputBlobsWrappers[0] = wrap(ld.outputBlobs[0]);
eltwiseData->outputBlobs = ld.outputBlobs;
eltwiseData->outputBlobsWrappers = ld.outputBlobsWrappers;
// Move references of [eltwise] layer consumers to the newly allocated blob.
for (int i = 0; i < eltwiseData->consumers.size(); ++i)
{
LayerData& consumer = layers[eltwiseData->consumers[i].lid];
for (int j = 0; j < consumer.inputBlobsId.size(); ++j)
{
if (consumer.inputBlobsId[j].lid == eltwiseData->id)
{
consumer.inputBlobs[j] = &ld.outputBlobs[0];
consumer.inputBlobsWrappers[j] = ld.outputBlobsWrappers[0];
break;
}
}
}
}
}
}
}
break;
}
}
if (preferableBackend != DNN_BACKEND_OPENCV && preferableBackend != DNN_BACKEND_CUDA)
continue; // Go to the next layer.
// the optimization #2. if there is concat layer that concatenates channels
// from the inputs together (i.e. axis == 1) then we make the inputs of
// the concat layer to write to the concatenation output buffer
// (and so we eliminate the concatenation layer, because the channels
// are concatenated implicitly).
Ptr<ConcatLayer> concatLayer = ld.layerInstance.dynamicCast<ConcatLayer>();
if( !concatLayer.empty() && !concatLayer->padding && ld.outputBlobs.size() == 1 )
{
Mat& output = ld.outputBlobs[0];
UMat umat_output;
#ifdef HAVE_OPENCL
if (!ld.outputBlobsWrappers.empty() &&
(preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget)))
{
size_t i, ninputs = ld.inputBlobsId.size();
bool conv_layer = true;
for( i = 0; i < ninputs; i++ )
{
LayerPin pin = ld.inputBlobsId[i];
LayerData* inp_i_data = &layers[pin.lid];
while(inp_i_data->skip &&
inp_i_data->inputBlobsId.size() == 1 &&
inp_i_data->consumers.size() == 1)
{
pin = inp_i_data->inputBlobsId[0];
inp_i_data = &layers[pin.lid];
}
conv_layer = conv_layer && (getLayerInstance(*inp_i_data)->type == "Convolution");
}
if (!conv_layer)
continue;
std::vector<UMat> umat_outputBlobs;
umat_outputBlobs = OpenCLBackendWrapper::getUMatVector(ld.outputBlobsWrappers);
umat_output = umat_outputBlobs[0];
}
#endif
// TODO: in general, this optimization can always be done, but
// many layers currently check that the input/output blobs are
// continuous arrays. Unfortunately, this is not true when
// the concatenation optimization is applied with batch_size > 1.
// so, for now, we only apply this optimization in the most popular
// case batch_size == 1.
int axis = normalize_axis(concatLayer->axis, output.dims);
if( output.total(0, axis) == 1 )
{
size_t i, ninputs = ld.inputBlobsId.size();
std::vector<LayerPin> realinputs(ninputs);
for( i = 0; i < ninputs; i++ )
{
LayerPin pin = ld.inputBlobsId[i];
LayerData* inp_i_data = &layers[pin.lid];
while(inp_i_data->skip &&
inp_i_data->inputBlobsId.size() == 1 &&
inp_i_data->consumers.size() == 1)
{
pin = inp_i_data->inputBlobsId[0];
inp_i_data = &layers[pin.lid];
}
printf_(("\treal input for %s is %s\n",
layers[ld.inputBlobsId[i].lid].getLayerInstance()->name.c_str(),
inp_i_data->getLayerInstance()->name.c_str()));
if(inp_i_data->skip || inp_i_data->consumers.size() != 1)
break;
#ifdef HAVE_CUDA
/* Risk: Not every operation in "NaryEltwise" is supported in the CUDA backend. There is a chance
that Concat's output is filled with data in both host and device, leading to data missing.
See https://github.com/opencv/opencv/issues/24721 for more details.
*/
if (preferableBackend == DNN_BACKEND_CUDA &&
(inp_i_data->layerInstance->supportBackend(DNN_BACKEND_CUDA) == false ||
(inp_i_data->layerInstance->type != "Convolution" &&
inp_i_data->layerInstance->type != "Pooling" &&
inp_i_data->layerInstance->type != "Resize" &&
inp_i_data->layerInstance->type != "Flatten" &&
inp_i_data->layerInstance->type != "Permute" &&
inp_i_data->layerInstance->type != "Reorg" &&
inp_i_data->layerInstance->type != "Eltwise" &&
inp_i_data->layerInstance->type != "NaryEltwise" &&
inp_i_data->layerInstance.dynamicCast<ActivationLayer>().empty())))
{
break;
}
#endif
realinputs[i] = pin;
}
if( i >= ninputs )
{
// Allocate new memory to prevent collisions during memory
// reusing (see https://github.com/opencv/opencv/pull/10456).
output = output.clone();
#ifdef HAVE_OPENCL
if (preferableBackend == DNN_BACKEND_OPENCV &&
IS_DNN_OPENCL_TARGET(preferableTarget))
{
std::vector<UMat> umats(1);
umat_output = umat_output.clone();
umats[0] = umat_output;
OpenCLBackendWrapper::update(ld.outputBlobsWrappers, umats);
}
#endif
#ifdef HAVE_CUDA
if (preferableBackend == DNN_BACKEND_CUDA)
ld.outputBlobsWrappers[0] = wrap(output);
#endif
std::vector<Range> chrange(output.dims, Range::all());
int ofs = 0;
for( i = 0; i < ninputs; i++ )
{
LayerPin pin = realinputs[i];
LayerData* inp_i_data = &layers[pin.lid];
int channels_i = ld.inputBlobs[i]->size[axis];
chrange[axis] = Range(ofs, ofs + channels_i);
printf_(("\toutput %s(%d) to channels (%d, %d)\n", inp_i_data->layerInstance->name.c_str(),
pin.oid, ofs, ofs + channels_i));
ofs += channels_i;
Mat output_slice = output(chrange);
Mat& curr_output = inp_i_data->outputBlobs[pin.oid];
CV_Assert(output_slice.isContinuous() && output_slice.size == curr_output.size);
Mat* oldPtr = &curr_output;
curr_output = output_slice;
#ifdef HAVE_OPENCL
if (preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget))
{
std::vector<UMat> umats(inp_i_data->outputBlobsWrappers.size());
umats[pin.oid] = umat_output(chrange);
OpenCLBackendWrapper::update(inp_i_data->outputBlobsWrappers, umats);
}
#endif
#ifdef HAVE_CUDA
if (preferableBackend == DNN_BACKEND_CUDA)
{
auto cuda_wrapper = wrap(output).dynamicCast<CUDABackendWrapper>();
auto offset = chrange[axis].start * output_slice.total(axis + 1, output.dims);
auto new_shape = shape(output_slice);
cuda_wrapper->update(new_shape, offset);
inp_i_data->outputBlobsWrappers[pin.oid] = cuda_wrapper.staticCast<BackendWrapper>();
}
#endif
// Layers that refer old input Mat will refer to the
// new data but the same Mat object.
CV_Assert_N(curr_output.data == output_slice.data, oldPtr == &curr_output);
}
#ifdef HAVE_CUDA
if (preferableBackend == DNN_BACKEND_CUDA)
{
for (int i = 0; i < ld.consumers.size(); i++)
{
LayerData& consumer = layers[ld.consumers[i].lid];
for (int j = 0; j < consumer.inputBlobsId.size(); j++)
{
if (consumer.inputBlobsId[j].lid == ld.id)
{
CV_Assert(consumer.inputBlobs[j]->data == ld.outputBlobs[0].data);
consumer.inputBlobsWrappers[j] = ld.outputBlobsWrappers[0];
break;
}
}
}
}
#endif
ld.skip = true;
printf_(("\toptimized out Concat layer %s\n", concatLayer->name.c_str()));
}
}
}
}
}
CV__DNN_INLINE_NS_END
}} // namespace cv::dnn
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