Add unidirectional sequence lstm support
Signed-off-by: xiang.zhang <xiang.zhang@verisilicon.com>
This commit is contained in:
xiang.zhang
Kainan Cha
parent
d4a13e18a9
commit
e27e15925c
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@ -335,3 +335,4 @@ ASALocalRun/
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# IDE
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.settings/
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build/
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@ -48,6 +48,7 @@ class Operation {
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RoundType down_scale_size_rounding = RoundType::FLOOR,
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uint32_t accumulator_bits = 0);
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std::unique_ptr<OperationImpl>& impl();
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const std::unique_ptr<OperationImpl>& impl() const;
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protected:
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std::unique_ptr<OperationImpl> impl_;
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@ -0,0 +1,65 @@
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/****************************************************************************
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*
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* Copyright (c) 2020 Vivante Corporation
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*
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* Permission is hereby granted, free of charge, to any person obtaining a
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* copy of this software and associated documentation files (the "Software"),
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* to deal in the Software without restriction, including without limitation
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* the rights to use, copy, modify, merge, publish, distribute, sublicense,
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* and/or sell copies of the Software, and to permit persons to whom the
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* Software is furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in
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* all copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
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* DEALINGS IN THE SOFTWARE.
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*
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*****************************************************************************/
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#ifndef TIM_VX_OPS_UNIDIRECTIONAL_SEQUENCE_LSTM_H_
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#define TIM_VX_OPS_UNIDIRECTIONAL_SEQUENCE_LSTM_H_
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#include "tim/vx/operation.h"
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namespace tim {
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namespace vx {
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namespace ops {
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/**
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* ## Unidirectional sequence lstm
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* how to bind input/output: take unidirectional_sequence_lstm_test.cc
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*/
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class UnidirectionalSequenceLstm: public Operation {
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public:
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enum ActivationType {
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kNONE = 0,
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kRELU = 1,
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kRELU6 = 2,
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kTANH = 3,
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kSIGMOID = 4,
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kHARDSIGMOID = 5,
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kCOUNT
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};
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UnidirectionalSequenceLstm(
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Graph* graph, float cell_clip, float proj_clip,
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ActivationType act_type, float forget_bias, bool time_major = false,
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ActivationType recurrent_act_type = ActivationType::kNONE,
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bool return_sequences = false /*False: only return last state*/
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);
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std::shared_ptr<Operation> Clone(
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std::shared_ptr<Graph>& graph) const override;
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protected:
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ActivationType act_type_;
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ActivationType recurrent_act_type_;
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};
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}
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} // namespace vx
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} // namespace tim
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#endif
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@ -90,6 +90,7 @@ Operation::Operation(Graph* graph, uint32_t operation_id,
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Operation::~Operation() {}
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std::unique_ptr<OperationImpl>& Operation::impl() { return impl_; }
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const std::unique_ptr<OperationImpl>& Operation::impl() const { return impl_; }
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Operation& Operation::BindInput(const std::shared_ptr<Tensor>& tensor) {
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impl_->BindInput(tensor);
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@ -108,8 +108,8 @@ SpatialTransformer|SPATIAL_TRANSFORMER|Mapped|[SpatialTransformer](https://githu
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|BidirectionalSequenceRNN|BIDIRECTIONAL_SEQUENCE_RNN|Planned 21Q4|[ANEURALNETWORKS_BIDIRECTIONAL_SEQUENCE_RNN](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a487fc5ae247de828f13e62b99f259f3c)
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|RNNCell|RNNCELL_OVXLIB|Planned 21Q3|[ANEURALNETWORKS_RNN](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0acd2684ac9c73bb29767b534e78a332e8)
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|BidirectionalSequenceLSTM|BIDIRECTIONAL_SEQUENCE_LSTM|Planned 21Q4|[ANEURALNETWORKS_BIDIRECTIONAL_SEQUENCE_LSTM](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a492a71cb7aa50b9a1a834a3cb269d778)
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|UnidirectionalSequenceLSTM|LSTM_OVXLIB|Planned 21Q4|[ANEURALNETWORKS_UNIDIRECTIONAL_SEQUENCE_LSTM](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0aaf30e491ad0b1fc7602cbde695b2c859)
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|LSTMCell|LSTMUNIT_OVXLIB|Planned 21Q3|[ANEURALNETWORKS_LSTM](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0ad0377e8c305e596fb7f64ff896671fc5)
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|UnidirectionalSequenceLSTM|LSTM_OVXLIB|Mapped|[ANEURALNETWORKS_UNIDIRECTIONAL_SEQUENCE_LSTM](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0aaf30e491ad0b1fc7602cbde695b2c859)
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|LSTMCell|LSTMUNIT_OVXLIB|replace with UnidirectionalSequenceLSTM by set n_step = 1 |[ANEURALNETWORKS_LSTM](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0ad0377e8c305e596fb7f64ff896671fc5)
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||PRE_PROCESS|Planned 21Q4|Image Preprocessing (YUV2RGB, Input Normalization, Resizing, etc)
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||HASHTABLE_LOOKUP|Planned 21Q4|[ANEURALNETWORKS_HASHTABLE_LOOKUP](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0aca92716c8c73c1f0fa7f0757916fee26)
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||EMBEDDING_LOOKUP|Planned 21Q4|[ANEURALNETWORKS_EMBEDDING_LOOKUP](developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a8d2ada77adb74357fc0770405bca0e3)
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@ -0,0 +1,86 @@
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/****************************************************************************
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*
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* Copyright (c) 2020 Vivante Corporation
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*
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* Permission is hereby granted, free of charge, to any person obtaining a
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* copy of this software and associated documentation files (the "Software"),
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* to deal in the Software without restriction, including without limitation
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* the rights to use, copy, modify, merge, publish, distribute, sublicense,
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* and/or sell copies of the Software, and to permit persons to whom the
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* Software is furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in
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* all copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
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* DEALINGS IN THE SOFTWARE.
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*
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*****************************************************************************/
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#include "tim/vx/ops/unidirectional_sequence_lstm.h"
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#include "operation_private.h"
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#include "vsi_nn_pub.h"
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namespace tim {
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namespace vx {
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namespace ops {
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vsi_nn_activation_e downcast_act_type(UnidirectionalSequenceLstm::ActivationType act) {
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switch (act) {
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case UnidirectionalSequenceLstm::ActivationType::kRELU:
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return VSI_NN_LSTMUNIT_ACT_RELU;
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case UnidirectionalSequenceLstm::ActivationType::kRELU6:
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return VSI_NN_LSTMUNIT_ACT_RELU6;
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case UnidirectionalSequenceLstm::ActivationType::kTANH:
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return VSI_NN_LSTMUNIT_ACT_TANH;
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case UnidirectionalSequenceLstm::ActivationType::kSIGMOID:
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return VSI_NN_LSTMUNIT_ACT_SIGMOID;
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case UnidirectionalSequenceLstm::ActivationType::kHARDSIGMOID:
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return VSI_NN_LSTMUNIT_ACT_HARD_SIGMOID;
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default: {
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VSILOGW("Not supported activition type for LSTM = %d", static_cast<int32_t>(act));
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return VSI_NN_ACT_NONE;
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}
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}
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}
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UnidirectionalSequenceLstm::UnidirectionalSequenceLstm(
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Graph* graph, float cell_clip, float proj_clip, ActivationType act_type,
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float forget_bias, bool time_major, ActivationType recurrent_act_type,
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bool return_sequences)
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: Operation(graph, VSI_NN_OP_LSTM_OVXLIB, LSTM_INPUT_CNT, LSTM_OUTPUT_CNT),
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act_type_(act_type),
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recurrent_act_type_(recurrent_act_type) {
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this->impl()->node()->nn_param.lstm_ovxlib.cell_clip = cell_clip;
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this->impl()->node()->nn_param.lstm_ovxlib.proj_clip = proj_clip;
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this->impl()->node()->nn_param.lstm_ovxlib.time_major = time_major;
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this->impl()->node()->nn_param.lstm_ovxlib.activation =
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downcast_act_type(act_type);
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this->impl()->node()->nn_param.lstm_ovxlib.forget_bias = forget_bias;
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this->impl()->node()->nn_param.lstm_ovxlib.recurrent_activation =
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downcast_act_type(recurrent_act_type);
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this->impl()->node()->nn_param.lstm_ovxlib.return_sequences =
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return_sequences;
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}
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std::shared_ptr<Operation> UnidirectionalSequenceLstm::Clone(std::shared_ptr<Graph>& graph) const {
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auto cloned_op =
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graph->CreateOperation<tim::vx::ops::UnidirectionalSequenceLstm>(
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this->impl()->node()->nn_param.lstm_ovxlib.cell_clip,
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this->impl()->node()->nn_param.lstm_ovxlib.proj_clip,
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act_type_,
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this->impl()->node()->nn_param.lstm_ovxlib.forget_bias,
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this->impl()->node()->nn_param.lstm_ovxlib.time_major,
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recurrent_act_type_,
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this->impl()->node()->nn_param.lstm_ovxlib.return_sequences);
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return cloned_op;
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}
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}
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} // namespace vx
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} // namespace tim
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@ -0,0 +1,209 @@
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/****************************************************************************
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*
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* Copyright (c) 2021 Vivante Corporation
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*
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* Permission is hereby granted, free of charge, to any person obtaining a
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* copy of this software and associated documentation files (the "Software"),
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* to deal in the Software without restriction, including without limitation
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* the rights to use, copy, modify, merge, publish, distribute, sublicense,
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* and/or sell copies of the Software, and to permit persons to whom the
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* Software is furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in
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* all copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
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* DEALINGS IN THE SOFTWARE.
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*
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*****************************************************************************/
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#include "tim/vx/context.h"
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#include "tim/vx/graph.h"
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#include "tim/vx/ops/unidirectional_sequence_lstm.h"
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#include "gtest/gtest.h"
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std::shared_ptr<tim::vx::Tensor> make_empty_tensor(
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std::shared_ptr<tim::vx::Graph> graph, const tim::vx::ShapeType& shape,
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const tim::vx::TensorAttribute& role) //, const float& default_value) {
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{
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tim::vx::TensorSpec spec(tim::vx::DataType::FLOAT32, shape, role);
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auto tensor = graph->CreateTensor(spec);
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uint32_t count = 1;
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for (auto dim : shape) {count *= dim;}
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std::vector<float> default_value(count);
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for(auto& i: default_value) {
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i = 0.f;
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}
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tensor->CopyDataToTensor(default_value.data(), count*sizeof(float));
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return tensor;
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}
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TEST(LSTM_CELL, shape_in_2_cell_4_out_4_float32) {
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// NoCifg_NoPeephole_NoProjection_NoLayerNorm
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auto ctx = tim::vx::Context::Create();
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auto g = ctx->CreateGraph();
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uint32_t n_batch, n_step, n_cell, n_input, n_output;
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n_batch = 1, n_step = 1, n_cell = 4, n_input = 2, n_output = 4;
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tim::vx::ShapeType input_shape, cell_shape, state_shape;
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input_shape = {n_batch, n_step, n_input}; // non-time-major
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tim::vx::TensorSpec input_tensor_spec(tim::vx::DataType::FLOAT32, tim::vx::ShapeType({n_input, n_step, n_batch}), tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec i2i_weight_spec(tim::vx::DataType::FLOAT32, tim::vx::ShapeType({n_input, n_cell}), tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec i2i_bias_spec (tim::vx::DataType::FLOAT32, tim::vx::ShapeType({n_cell}), tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec i2c_weight_spec(tim::vx::DataType::FLOAT32, tim::vx::ShapeType({n_input, n_cell}), tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec i2c_bias_spec (tim::vx::DataType::FLOAT32, tim::vx::ShapeType({n_cell}), tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec i2f_weight_spec(tim::vx::DataType::FLOAT32, tim::vx::ShapeType({n_input, n_cell}), tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec i2f_bias_spec (tim::vx::DataType::FLOAT32, tim::vx::ShapeType({n_cell}), tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec i2o_weight_spec(tim::vx::DataType::FLOAT32, tim::vx::ShapeType({n_input, n_cell}), tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec i2o_bias_spec (tim::vx::DataType::FLOAT32, tim::vx::ShapeType({n_cell}), tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec output_spec (tim::vx::DataType::FLOAT32, tim::vx::ShapeType({n_output, n_step, n_batch}), tim::vx::TensorAttribute::OUTPUT);
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// tim::vx::TensorSpec hstate_spec (tim::vx::DataType::FLOAT32, tim::vx::ShapeType({n_batch, n_output}), tim::vx::TensorAttribute::OUTPUT);
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// tim::vx::TensorSpec cstate_spec (tim::vx::DataType::FLOAT32, tim::vx::ShapeType({n_batch, n_cell}), tim::vx::TensorAttribute::OUTPUT);
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auto output_tensor = g->CreateTensor(output_spec);
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std::vector<float> input2input_weights, input2cell_weights, input2forget_weights, input2output_weights;
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// tim::vx::Tensor input2input_weights_tensor, input2cell_weights_tensor, input2forget_weights_tensor, input2output_weights_tensor;
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input2input_weights = {-0.45018822, -0.02338299, -0.0870589, -0.34550029,
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0.04266912, -0.15680569, -0.34856534, 0.43890524};
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std::vector<float> input_gate_bias = {0.0, 0.0, 0.0, 0.0};
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auto input2input_weights_tensor = g->CreateTensor(i2i_weight_spec, input2input_weights.data());
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auto input_gate_bias_tensor = g->CreateTensor(i2i_bias_spec, input_gate_bias.data());
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input2cell_weights = {-0.50013041, 0.1370284, 0.11810488, 0.2013163,
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-0.20583314, 0.44344562, 0.22077113, -0.29909778};
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std::vector<float> cell_gate_bias = {0.0, 0.0, 0.0, 0.0};
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auto cell_gate_bias_tensor = g->CreateTensor(i2c_bias_spec, cell_gate_bias.data());
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auto input2cell_weights_tensor = g->CreateTensor(i2c_weight_spec, input2cell_weights.data());
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input2forget_weights = {0.09701663, 0.20334584, -0.50592935,
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-0.31343272, -0.40032279, 0.44781327,
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0.01387155, -0.35593212};
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std::vector<float> forget_gate_bias = {1., 1., 1., 1.};
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auto input2forget_weights_tensor = g->CreateTensor(i2f_weight_spec, input2forget_weights.data());
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auto forget_gate_bias_tensor = g->CreateTensor(i2f_bias_spec, forget_gate_bias.data());
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input2output_weights = {-0.25065863, -0.28290087, 0.04613829, 0.40525138,
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0.44272184, 0.03897077, -0.1556896, 0.19487578};
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std::vector<float> output_gate_bias = {0.0, 0.0, 0.0, 0.0};
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auto input2output_weights_tensor = g->CreateTensor(i2o_weight_spec, input2output_weights.data());
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auto output_gate_bias_tensor = g->CreateTensor(i2o_bias_spec, output_gate_bias.data());
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tim::vx::TensorSpec r2i_weight_spec(tim::vx::DataType::FLOAT32, tim::vx::ShapeType({n_output, n_cell}), tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec r2c_weight_spec(tim::vx::DataType::FLOAT32, tim::vx::ShapeType({n_output, n_cell}), tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec r2f_weight_spec(tim::vx::DataType::FLOAT32, tim::vx::ShapeType({n_output, n_cell}), tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec r2o_weight_spec(tim::vx::DataType::FLOAT32, tim::vx::ShapeType({n_output, n_cell}), tim::vx::TensorAttribute::CONSTANT);
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std::vector<float> recurrent_to_input_weights = {
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-0.0063535, -0.2042388, 0.31454784, -0.35746509,
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0.28902304, 0.08183324, -0.16555229, 0.02286911,
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-0.13566875, 0.03034258, 0.48091322, -0.12528998,
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0.24077177, -0.51332325, -0.33502164, 0.10629296};
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std::vector<float> recurrent_to_cell_weights = {
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-0.3407414, 0.24443203, -0.2078532, 0.26320225,
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0.05695659, -0.00123841, -0.4744786, -0.35869038,
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-0.06418842, -0.13502428, -0.501764, 0.22830659,
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-0.46367589, 0.26016325, -0.03894562, -0.16368064};
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std::vector<float> recurrent_to_forget_weights = {
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-0.48684245, -0.06655136, 0.42224967, 0.2112639,
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0.27654213, 0.20864892, -0.07646349, 0.45877004,
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0.00141793, -0.14609534, 0.36447752, 0.09196436,
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0.28053468, 0.01560611, -0.20127171, -0.01140004};
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std::vector<float> recurrent_to_output_weights = {
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0.43385774, -0.17194885, 0.2718237, 0.09215671,
|
||||
0.24107647, -0.39835793, 0.18212086, 0.01301402,
|
||||
0.48572797, -0.50656658, 0.20047462, -0.20607421,
|
||||
-0.51818722, -0.15390486, 0.0468148, 0.39922136};
|
||||
|
||||
auto r2i_weight_tensor = g->CreateTensor(r2i_weight_spec, recurrent_to_input_weights.data());
|
||||
auto r2c_weight_tensor = g->CreateTensor(r2c_weight_spec, recurrent_to_cell_weights.data());
|
||||
auto r2f_weight_tensor = g->CreateTensor(r2f_weight_spec, recurrent_to_forget_weights.data());
|
||||
auto r2o_weight_tensor = g->CreateTensor(r2o_weight_spec, recurrent_to_output_weights.data());
|
||||
|
||||
// std::vector<std::vector<float>> golden_output = {
|
||||
// {{-0.02973187, 0.1229473, 0.20885126, -0.15358765}},
|
||||
// {{-0.03716109, 0.12507336, 0.41193449, -0.20860538}},
|
||||
// {{-0.15053082, 0.09120187, 0.24278517, -0.12222792}}};
|
||||
|
||||
std::vector<float> input = {2,3};
|
||||
auto input_tensor = g->CreateTensor(input_tensor_spec, input.data());
|
||||
|
||||
auto lstm_cell_op = g->CreateOperation<tim::vx::ops::UnidirectionalSequenceLstm>(
|
||||
0.0, 0.0, tim::vx::ops::UnidirectionalSequenceLstm::ActivationType::kTANH, 0.0, false,
|
||||
tim::vx::ops::UnidirectionalSequenceLstm::kNONE, true);
|
||||
|
||||
(*lstm_cell_op)
|
||||
.BindInputs({
|
||||
input_tensor,
|
||||
g->CreateTensorPlaceHolder(), /*h_state*/
|
||||
g->CreateTensorPlaceHolder(), /*c_state*/
|
||||
input2input_weights_tensor,
|
||||
input2forget_weights_tensor,
|
||||
input2cell_weights_tensor,
|
||||
input2output_weights_tensor,
|
||||
|
||||
r2i_weight_tensor,
|
||||
r2c_weight_tensor,
|
||||
r2f_weight_tensor,
|
||||
r2o_weight_tensor,
|
||||
|
||||
g->CreateTensorPlaceHolder(), /*weight_c2i*/
|
||||
g->CreateTensorPlaceHolder(), /*weight_c2f*/
|
||||
g->CreateTensorPlaceHolder(), /*weight_c2o*/
|
||||
|
||||
input_gate_bias_tensor,
|
||||
forget_gate_bias_tensor,
|
||||
cell_gate_bias_tensor,
|
||||
output_gate_bias_tensor,
|
||||
|
||||
// optional for projection
|
||||
/*weight_prj*/
|
||||
/*bias_prj*/
|
||||
|
||||
// Layer norm IFCO
|
||||
// g->CreateTensorPlaceHolder(),
|
||||
// g->CreateTensorPlaceHolder(),
|
||||
// g->CreateTensorPlaceHolder(),
|
||||
// g->CreateTensorPlaceHolder(),
|
||||
|
||||
// AUX weight_i2i|i2f|i2c|i2o
|
||||
// g->CreateTensorPlaceHolder(),
|
||||
// g->CreateTensorPlaceHolder(),
|
||||
// g->CreateTensorPlaceHolder(),
|
||||
// g->CreateTensorPlaceHolder(),
|
||||
})
|
||||
.BindOutputs({
|
||||
output_tensor,
|
||||
make_empty_tensor(
|
||||
g, tim::vx::ShapeType({n_output, n_batch}),
|
||||
tim::vx::TensorAttribute::OUTPUT), // Output_H_STATE
|
||||
make_empty_tensor(
|
||||
g, tim::vx::ShapeType({n_cell, n_batch}),
|
||||
tim::vx::TensorAttribute::OUTPUT), // output_C_State
|
||||
});
|
||||
|
||||
g->Compile();
|
||||
g->Run();
|
||||
|
||||
std::vector<float> golden = {{-0.02973187, 0.1229473, 0.20885126, -0.15358765}};
|
||||
std::vector<float> real(golden.size());
|
||||
output_tensor->CopyDataFromTensor(real.data());
|
||||
|
||||
for(uint32_t i = 0; i < golden.size(); ++i) {
|
||||
EXPECT_NEAR(golden[i], real[i], 0.001f) << "Failed at " << i << "th item";
|
||||
}
|
||||
}
|
||||
Loading…
Reference in New Issue