[CPU] Added eltwise Round-5 (#2347)
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@ -600,7 +600,8 @@ void MKLDNNGraphOptimizer::FuseConvolutionAndActivation(MKLDNNGraph &graph) {
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return eltwiseNode &&
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return eltwiseNode &&
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(eltwiseNode->getOpType() == Relu ||
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(eltwiseNode->getOpType() == Relu ||
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(conv->getCnnLayer()->precision == Precision::FP32 &&
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(conv->getCnnLayer()->precision == Precision::FP32 &&
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IsOneOf(eltwiseNode->getOpType(), {Elu, Logistic, BoundedRelu, Clamp, Swish, Hswish, Mish, Hsigmoid})));
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IsOneOf(eltwiseNode->getOpType(), {Elu, Logistic, BoundedRelu, Clamp, Swish, Hswish, Mish, Hsigmoid,
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Round})));
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};
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};
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for (int i = 0; i < graphNodes.size(); i++) {
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for (int i = 0; i < graphNodes.size(); i++) {
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@ -678,7 +679,8 @@ void MKLDNNGraphOptimizer::FuseFullyConnectedAndSimpleOperation(MKLDNNGraph &gra
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if (eltwiseNode == nullptr)
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if (eltwiseNode == nullptr)
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THROW_IE_EXCEPTION << "Cannot get Eltwise node " << childNode->getName();
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THROW_IE_EXCEPTION << "Cannot get Eltwise node " << childNode->getName();
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if (IsOneOf(eltwiseNode->getOpType(), {Relu, Gelu, Elu, Logistic, BoundedRelu, Clamp, Swish, Hswish, Mish, Hsigmoid})) {
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if (IsOneOf(eltwiseNode->getOpType(), {Relu, Gelu, Elu, Logistic, BoundedRelu, Clamp, Swish, Hswish, Mish,
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Hsigmoid, Round})) {
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return true;
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return true;
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} else if (IsOneOf(eltwiseNode->getOpType(), {MulAdd, Prelu})) {
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} else if (IsOneOf(eltwiseNode->getOpType(), {MulAdd, Prelu})) {
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if (eltwiseNode->getOpType() == MulAdd && eltwiseNode->getCnnLayer()->blobs.size() != 2)
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if (eltwiseNode->getOpType() == MulAdd && eltwiseNode->getCnnLayer()->blobs.size() != 2)
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@ -1044,7 +1046,8 @@ void MKLDNNGraphOptimizer::FuseConvolutionAndSimpleOperation(MKLDNNGraph &graph)
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return ((eltwiseNode->getOpType() == MulAdd && node->getCnnLayer()->blobs.size() == 2) ||
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return ((eltwiseNode->getOpType() == MulAdd && node->getCnnLayer()->blobs.size() == 2) ||
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(eltwiseNode->getOpType() == Prelu) ||
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(eltwiseNode->getOpType() == Prelu) ||
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IsOneOf(eltwiseNode->getOpType(), {Relu, Elu, Logistic, BoundedRelu, Clamp, Swish, Hswish, Mish, Hsigmoid}));
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IsOneOf(eltwiseNode->getOpType(), {Relu, Elu, Logistic, BoundedRelu, Clamp, Swish, Hswish, Mish,
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Hsigmoid, Round}));
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}
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}
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return false;
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return false;
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@ -1258,7 +1261,8 @@ void MKLDNNGraphOptimizer::FuseConvolutionSumAndConvolutionSumActivation(MKLDNNG
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return eltwiseNode &&
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return eltwiseNode &&
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(eltwiseNode->getOpType() == Relu ||
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(eltwiseNode->getOpType() == Relu ||
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(conv->getCnnLayer()->precision == Precision::FP32 &&
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(conv->getCnnLayer()->precision == Precision::FP32 &&
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IsOneOf(eltwiseNode->getOpType(), {Elu, Logistic, BoundedRelu, Clamp, Swish, Hswish, Mish, Hsigmoid})));
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IsOneOf(eltwiseNode->getOpType(), {Elu, Logistic, BoundedRelu, Clamp, Swish, Hswish, Mish, Hsigmoid,
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Round})));
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};
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};
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for (auto &graphNode : graphNodes) {
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for (auto &graphNode : graphNodes) {
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@ -1611,7 +1615,7 @@ void MKLDNNGraphOptimizer::FuseNormalizeAndSimpleOperation(MKLDNNGraph &graph) {
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if (eltwiseNode == nullptr)
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if (eltwiseNode == nullptr)
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THROW_IE_EXCEPTION << "Cannot get Eltwise node " << node->getName();
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THROW_IE_EXCEPTION << "Cannot get Eltwise node " << node->getName();
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return IsOneOf(eltwiseNode->getOpType(), {Relu, Gelu, Elu, Logistic, BoundedRelu, Clamp, Tanh, Swish,
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return IsOneOf(eltwiseNode->getOpType(), {Relu, Gelu, Elu, Logistic, BoundedRelu, Clamp, Tanh, Swish,
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Hswish, Mish, Hsigmoid, Linear, Abs, Square, Sqrt}) ||
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Hswish, Mish, Hsigmoid, Round, Linear, Abs, Square, Sqrt}) ||
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((eltwiseNode->getOpType() == MulAdd && eltwiseNode->getCnnLayer()->blobs.size() == 2) ||
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((eltwiseNode->getOpType() == MulAdd && eltwiseNode->getCnnLayer()->blobs.size() == 2) ||
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(eltwiseNode->getOpType() == Prelu));
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(eltwiseNode->getOpType() == Prelu));
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}
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}
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@ -75,6 +75,7 @@ static const InferenceEngine::details::caseless_unordered_map<std::string, Type>
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{ "HSwish", Eltwise },
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{ "HSwish", Eltwise },
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{ "Mish", Eltwise },
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{ "Mish", Eltwise },
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{ "HSigmoid", Eltwise },
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{ "HSigmoid", Eltwise },
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{ "Round", Eltwise },
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{ "ScaleShift", Eltwise },
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{ "ScaleShift", Eltwise },
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{ "PReLU", Eltwise },
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{ "PReLU", Eltwise },
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{ "Norm", Lrn },
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{ "Norm", Lrn },
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@ -312,7 +312,8 @@ private:
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auto& eltwiseNode = dynamic_cast<const MKLDNNEltwiseNode&>(node);
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auto& eltwiseNode = dynamic_cast<const MKLDNNEltwiseNode&>(node);
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switch (eltwiseNode.getOpType()) {
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switch (eltwiseNode.getOpType()) {
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case Relu: case Gelu: case Elu: case Tanh: case Logistic: case Square: case Abs: case Sqrt:
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case Relu: case Gelu: case Elu: case Tanh: case Logistic: case Square: case Abs: case Sqrt:
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case Linear: case BoundedRelu: case SoftRelu: case Relu6: case Exp: case Clamp: case Swish: case Hswish: case Mish: case Hsigmoid:
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case Linear: case BoundedRelu: case SoftRelu: case Relu6: case Exp: case Clamp: case Swish: case Hswish:
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case Mish: case Hsigmoid: case Round:
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return jit_mkldnn_emitter::get_supported_precisions();
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return jit_mkldnn_emitter::get_supported_precisions();
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case Add: return jit_add_emitter::get_supported_precisions();
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case Add: return jit_add_emitter::get_supported_precisions();
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case MulAdd: return jit_mul_add_emitter::get_supported_precisions();
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case MulAdd: return jit_mul_add_emitter::get_supported_precisions();
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@ -345,7 +346,8 @@ private:
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auto& eltwiseNode = dynamic_cast<const MKLDNNEltwiseNode&>(node);
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auto& eltwiseNode = dynamic_cast<const MKLDNNEltwiseNode&>(node);
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switch (eltwiseNode.getOpType()) {
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switch (eltwiseNode.getOpType()) {
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case Relu: case Gelu: case Elu: case Tanh: case Logistic: case Square: case Abs: case Sqrt:
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case Relu: case Gelu: case Elu: case Tanh: case Logistic: case Square: case Abs: case Sqrt:
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case Linear: case BoundedRelu: case SoftRelu: case Relu6: case Exp: case Clamp: case Swish: case Hswish: case Mish: case Hsigmoid:
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case Linear: case BoundedRelu: case SoftRelu: case Relu6: case Exp: case Clamp: case Swish: case Hswish:
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case Mish: case Hsigmoid: case Round:
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return std::make_shared<jit_mkldnn_emitter>(this, isa, eltwiseNode, exec_prec);
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return std::make_shared<jit_mkldnn_emitter>(this, isa, eltwiseNode, exec_prec);
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case Add: return std::make_shared<jit_add_emitter>(this, isa, eltwiseNode, exec_prec);
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case Add: return std::make_shared<jit_add_emitter>(this, isa, eltwiseNode, exec_prec);
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case MulAdd: return std::make_shared<jit_mul_add_emitter>(this, isa, eltwiseNode, exec_prec);
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case MulAdd: return std::make_shared<jit_mul_add_emitter>(this, isa, eltwiseNode, exec_prec);
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@ -764,6 +766,18 @@ MKLDNNEltwiseNode::initializers = {
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opType = Hsigmoid;
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opType = Hsigmoid;
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algorithm = mkldnn::eltwise_hsigmoid;
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algorithm = mkldnn::eltwise_hsigmoid;
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}},
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}},
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{"round", [](GenericLayer* activationLayer, EltwiseOpType& opType, mkldnn::algorithm& algorithm, float& alpha, float& beta) {
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alpha = 0.0f;
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beta = 0.0f;
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opType = Round;
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std::string mode = activationLayer->GetParamAsString("mode", "half_to_even");
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if (mode == "half_to_even")
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algorithm = mkldnn::eltwise_round_half_to_even;
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else if (mode == "half_away_from_zero")
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algorithm = mkldnn::eltwise_round_half_away_from_zero;
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else
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THROW_IE_EXCEPTION << "Round layer with name " << activationLayer->name << " doesn't support mode " << mode;
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}},
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};
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};
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void MKLDNNEltwiseNode::init() {
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void MKLDNNEltwiseNode::init() {
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@ -833,7 +847,8 @@ void MKLDNNEltwiseNode::init() {
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comparator(layerType, "swish") ||
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comparator(layerType, "swish") ||
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comparator(layerType, "hswish") ||
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comparator(layerType, "hswish") ||
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comparator(layerType, "mish") ||
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comparator(layerType, "mish") ||
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comparator(layerType, "hsigmoid")) {
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comparator(layerType, "hsigmoid") ||
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comparator(layerType, "round")) {
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initializers[layerType](getCnnLayer().get(), eltwiseOp, eltwiseAlgorithm, alpha, beta);
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initializers[layerType](getCnnLayer().get(), eltwiseOp, eltwiseAlgorithm, alpha, beta);
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} else {
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} else {
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THROW_IE_EXCEPTION << "Unsupported algorithm for Eltwise node with name `" << getName() << "`.";
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THROW_IE_EXCEPTION << "Unsupported algorithm for Eltwise node with name `" << getName() << "`.";
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@ -843,7 +858,8 @@ void MKLDNNEltwiseNode::init() {
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size_t MKLDNNEltwiseNode::getOpInputsNum() const {
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size_t MKLDNNEltwiseNode::getOpInputsNum() const {
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switch (getOpType()) {
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switch (getOpType()) {
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case Relu: case Gelu: case Elu: case Tanh: case Logistic: case Square: case Abs: case Sqrt: case PowerStatic:
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case Relu: case Gelu: case Elu: case Tanh: case Logistic: case Square: case Abs: case Sqrt: case PowerStatic:
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case Linear: case BoundedRelu: case SoftRelu: case Relu6: case Exp: case Clamp: case Swish: case Hswish: case Mish: case Hsigmoid:
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case Linear: case BoundedRelu: case SoftRelu: case Relu6: case Exp: case Clamp: case Swish: case Hswish:
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case Mish: case Hsigmoid: case Round:
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case LogicalNot:
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case LogicalNot:
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return 1;
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return 1;
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case Add: case Subtract: case Multiply: case Divide: case FloorMod: case Mod: case Maximum: case Minimum: case SquaredDifference:
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case Add: case Subtract: case Multiply: case Divide: case FloorMod: case Mod: case Maximum: case Minimum: case SquaredDifference:
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@ -1469,7 +1485,8 @@ void MKLDNNEltwiseNode::executeReference(const std::vector<const uint8_t *>& src
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switch (getOpType()) {
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switch (getOpType()) {
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case Relu: case Gelu: case Elu: case Tanh: case Logistic: case Square: case Abs: case Sqrt:
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case Relu: case Gelu: case Elu: case Tanh: case Logistic: case Square: case Abs: case Sqrt:
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case Linear: case BoundedRelu: case SoftRelu: case Relu6: case Exp: case Clamp: case Swish: case Hswish: case Mish: case Hsigmoid:
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case Linear: case BoundedRelu: case SoftRelu: case Relu6: case Exp: case Clamp: case Swish: case Hswish:
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case Mish: case Hsigmoid: case Round:
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*dst_ptr_f = ref_eltwise_injector->compute_scalar(src_f[0]); break;
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*dst_ptr_f = ref_eltwise_injector->compute_scalar(src_f[0]); break;
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case Add: *dst_ptr_f = src_f[0] + src_f[1]; break;
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case Add: *dst_ptr_f = src_f[0] + src_f[1]; break;
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case MulAdd: *dst_ptr_f = src_f[0] * src_f[1] + src_f[2]; break;
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case MulAdd: *dst_ptr_f = src_f[0] * src_f[1] + src_f[2]; break;
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@ -1570,6 +1587,8 @@ void MKLDNNEltwiseNode::appendPostOps(mkldnn::post_ops& ops) {
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case mkldnn::eltwise_hswish:
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case mkldnn::eltwise_hswish:
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case mkldnn::eltwise_mish:
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case mkldnn::eltwise_mish:
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case mkldnn::eltwise_hsigmoid:
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case mkldnn::eltwise_hsigmoid:
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case mkldnn::eltwise_round_half_to_even:
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case mkldnn::eltwise_round_half_away_from_zero:
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ops.append_eltwise(1.0, getAlgorithm(), getAlpha(), getBeta());
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ops.append_eltwise(1.0, getAlgorithm(), getAlpha(), getBeta());
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break;
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break;
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case mkldnn::depthwise_scale_shift:
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case mkldnn::depthwise_scale_shift:
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@ -59,7 +59,8 @@ enum EltwiseOpType {
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Prelu,
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Prelu,
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Mish,
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Mish,
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Hswish,
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Hswish,
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Hsigmoid
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Hsigmoid,
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Round
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};
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};
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struct jit_eltwise_params {
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struct jit_eltwise_params {
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@ -2123,7 +2123,7 @@ bool MKLDNNInterpolateNode::canFuse(const MKLDNNNodePtr& node) const {
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if (eltwiseNode == nullptr)
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if (eltwiseNode == nullptr)
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THROW_IE_EXCEPTION << "Cannot get eltwise node " << node->getName();
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THROW_IE_EXCEPTION << "Cannot get eltwise node " << node->getName();
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return isOneOf(eltwiseNode->getOpType(), {MulAdd, Prelu, Relu, Gelu, Elu, Logistic, BoundedRelu, Clamp,
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return isOneOf(eltwiseNode->getOpType(), {MulAdd, Prelu, Relu, Gelu, Elu, Logistic, BoundedRelu, Clamp,
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Tanh, Swish, Hswish, Mish, Hsigmoid, Linear, Abs, Square, Sqrt});
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Tanh, Swish, Hswish, Mish, Hsigmoid, Round, Linear, Abs, Square, Sqrt});
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}
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}
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return false;
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return false;
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@ -23,34 +23,36 @@ const std::vector<InferenceEngine::Precision> netPrecisions = {
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};
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};
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const std::map<ActivationTypes, std::vector<std::vector<float>>> activationTypes = {
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const std::map<ActivationTypes, std::vector<std::vector<float>>> activationTypes = {
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{Sigmoid, {}},
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{Sigmoid, {}},
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{Tanh, {}},
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{Tanh, {}},
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{Relu, {}},
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{Relu, {}},
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{Exp, {}},
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{Exp, {}},
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{Log, {}},
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{Log, {}},
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{Sign, {}},
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{Sign, {}},
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{Abs, {}},
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{Abs, {}},
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{Clamp, {{-2.0f, 2.0f}}},
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{Clamp, {{-2.0f, 2.0f}}},
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{Negative, {}},
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{Negative, {}},
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{Acos, {}},
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{Acos, {}},
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{Asin, {}},
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{Asin, {}},
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{Atan, {}},
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{Atan, {}},
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{Cos, {}},
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{Cos, {}},
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{Cosh, {}},
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{Cosh, {}},
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{Floor, {}},
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{Floor, {}},
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{Sin, {}},
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{Sin, {}},
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{Sinh, {}},
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{Sinh, {}},
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{Sqrt, {}},
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{Sqrt, {}},
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{Tan, {}},
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{Tan, {}},
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{Elu, {{0.1f}}},
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{Elu, {{0.1f}}},
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{Erf, {}},
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{Erf, {}},
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{HardSigmoid, {{0.2f, 0.5f}}},
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{HardSigmoid, {{0.2f, 0.5f}}},
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{Selu, {{1.6732f, 1.0507f}}},
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{Selu, {{1.6732f, 1.0507f}}},
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{Ceiling, {}},
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{Ceiling, {}},
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{Mish, {}},
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{Mish, {}},
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{HSwish, {}},
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{HSwish, {}},
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{SoftPlus, {}},
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{SoftPlus, {}},
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{HSigmoid, {}}
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{HSigmoid, {}},
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{RoundHalfToEven, {}},
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{RoundHalfAwayFromZero, {}}
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};
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};
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const std::map<ActivationTypes, std::vector<std::vector<float>>> activationParamTypes = {
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const std::map<ActivationTypes, std::vector<std::vector<float>>> activationParamTypes = {
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2
inference-engine/thirdparty/mkl-dnn
vendored
2
inference-engine/thirdparty/mkl-dnn
vendored
@ -1 +1 @@
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Subproject commit d7d8ed46078b637794bc91215e1a982bb0f1683a
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Subproject commit 5ef085d5af65e8966e03cdfcbaa65761d61a5c9a
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@ -153,13 +153,11 @@ def test_round_even():
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assert list(node.get_output_shape(0)) == [3, 10]
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assert list(node.get_output_shape(0)) == [3, 10]
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assert node.get_output_element_type(0) == Type.f32
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assert node.get_output_element_type(0) == Type.f32
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# Excluded because this part needs mklddn implementation of Round operation
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input_tensor = np.array([-2.5, -1.5, -0.5, 0.5, 0.9, 1.5, 2.3, 2.5, 3.5], dtype=np.float32)
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# Need to uncomment and check when 37651 will be done.
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expected = [-2.0, -2.0, 0.0, 0.0, 1.0, 2.0, 2.0, 2.0, 4.0]
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# input_tensor = np.array([-2.5, -1.5, -0.5, 0.5, 0.9, 1.5, 2.3, 2.5, 3.5], dtype=np.float32)
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# expected = [-2.0, -2.0, 0.0, 0.0, 1.0, 2.0, 2.0, 2.0, 4.0]
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# result = run_op_node([input_tensor], ng.round, "HALF_TO_EVEN")
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result = run_op_node([input_tensor], ng.round, "HALF_TO_EVEN")
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# assert np.allclose(result, expected)
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assert np.allclose(result, expected)
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def test_round_away():
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def test_round_away():
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@ -172,13 +170,11 @@ def test_round_away():
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assert list(node.get_output_shape(0)) == [3, 10]
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assert list(node.get_output_shape(0)) == [3, 10]
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assert node.get_output_element_type(0) == Type.f32
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assert node.get_output_element_type(0) == Type.f32
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# Excluded because this part needs mklddn implementation of Round operation
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input_tensor = np.array([-2.5, -1.5, -0.5, 0.5, 0.9, 1.5, 2.3, 2.5, 3.5], dtype=np.float32)
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# Need to uncomment and check when 37651 will be done.
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expected = [-3.0, -2.0, -1.0, 1.0, 1.0, 2.0, 2.0, 3.0, 4.0]
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# input_tensor = np.array([-2.5, -1.5, -0.5, 0.5, 0.9, 1.5, 2.3, 2.5, 3.5], dtype=np.float32)
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# expected = [-3.0, -2.0, -1.0, 1.0, 1.0, 2.0, 2.0, 3.0, 4.0]
|
|
||||||
|
|
||||||
# result = run_op_node([input_tensor], ng.round, "HALF_AWAY_FROM_ZERO")
|
result = run_op_node([input_tensor], ng.round, "HALF_AWAY_FROM_ZERO")
|
||||||
# assert np.allclose(result, expected)
|
assert np.allclose(result, expected)
|
||||||
|
|
||||||
|
|
||||||
def test_hsigmoid():
|
def test_hsigmoid():
|
||||||
|
@ -338,7 +338,6 @@ tests_expected_to_fail = [
|
|||||||
"OnnxBackendNodeModelTest.test_clip_default_int8_max_cpu"),
|
"OnnxBackendNodeModelTest.test_clip_default_int8_max_cpu"),
|
||||||
(xfail_issue_38091,
|
(xfail_issue_38091,
|
||||||
"OnnxBackendNodeModelTest.test_gather_negative_indices_cpu",
|
"OnnxBackendNodeModelTest.test_gather_negative_indices_cpu",
|
||||||
"OnnxBackendNodeModelTest.test_round_cpu",
|
|
||||||
"OnnxBackendNodeModelTest.test_mvn_cpu",
|
"OnnxBackendNodeModelTest.test_mvn_cpu",
|
||||||
"OnnxBackendNodeModelTest.test_elu_example_cpu"),
|
"OnnxBackendNodeModelTest.test_elu_example_cpu"),
|
||||||
(xfail_issue_35929,
|
(xfail_issue_35929,
|
||||||
|
Loading…
Reference in New Issue
Block a user