Lines Matching refs:outs
35 bool add(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, HexagonModel* model) { in add() argument
37 HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for float32::add"); in add()
46 return model->addFusedFloatOperation(OP_Add_f, NN_PAD_NA, {}, act, {in1, in2}, outs); in add()
49 bool average_pool_2d(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, in average_pool_2d() argument
53 HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for float32::average_pool_2d"); in average_pool_2d()
97 outs); in average_pool_2d()
100 bool concatenation(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, in concatenation() argument
103 HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for float32::concatenation"); in concatenation()
119 return model->addBasicOperation(OP_Concat_f, NN_PAD_NA, inputs, outs); in concatenation()
122 bool conv_2d(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, in conv_2d() argument
126 HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for float32::conv_2d"); in conv_2d()
166 outs); in conv_2d()
169 bool depthwise_conv_2d(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, in depthwise_conv_2d() argument
173 HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for float32::depthwise_conv_2d"); in depthwise_conv_2d()
218 {input, filter, stride}, outs); in depthwise_conv_2d()
221 bool fully_connected(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, in fully_connected() argument
224 HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for float32::fully_connected"); in fully_connected()
234 return model->addFusedFloatOperation(OP_MatMul_f, NN_PAD_NA, bias, act, {input, weights}, outs); in fully_connected()
237 bool l2_pool_2d(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, in l2_pool_2d() argument
241 HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for float32::l2_pool_2d"); in l2_pool_2d()
285 outs); in l2_pool_2d()
289 const std::vector<uint32_t>& outs, HexagonModel* model) { in local_response_normalization() argument
292 HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), in local_response_normalization()
306 return model->addBasicOperation(OP_LRN_f, NN_PAD_NA, {input, window, bias, alpha, beta}, outs); in local_response_normalization()
309 bool logistic(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, in logistic() argument
312 HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for float32::logistic"); in logistic()
318 return model->addBasicOperation(OP_Sigmoid_f, NN_PAD_NA, {input}, outs); in logistic()
321 bool max_pool_2d(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, in max_pool_2d() argument
325 HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for float32::max_pool_2d"); in max_pool_2d()
369 outs); in max_pool_2d()
372 bool mul(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, HexagonModel* model) { in mul() argument
374 HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for float32::mul"); in mul()
383 return model->addFusedFloatOperation(OP_Mul_f, NN_PAD_NA, {}, act, {in1, in2}, outs); in mul()
386 bool relu(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, in relu() argument
389 HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for float32::relu"); in relu()
395 return model->addBasicOperation(OP_Relu_f, NN_PAD_NA, {input}, outs); in relu()
398 bool relu1(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, in relu1() argument
401 HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for float32::relu1"); in relu1()
409 return model->addBasicOperation(OP_Clamp_f, NN_PAD_NA, {input, min, max}, outs); in relu1()
412 bool relu6(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, in relu6() argument
415 HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for float32::relu6"); in relu6()
422 return model->addBasicOperation(OP_ReluX_f, NN_PAD_NA, {input, max}, outs); in relu6()
425 bool reshape(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, in reshape() argument
428 HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for float32::reshape"); in reshape()
435 return model->addBasicOperation(OP_Reshape, NN_PAD_NA, {input, newdims}, outs); in reshape()
438 bool resize_bilinear(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, in resize_bilinear() argument
441 HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for float32::resize_bilinear"); in resize_bilinear()
452 return model->addBasicOperation(OP_ResizeBilinear_f, NN_PAD_NA, {input, newdim}, outs); in resize_bilinear()
455 bool softmax(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, in softmax() argument
458 HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for float32::softmax"); in softmax()
465 return model->addBasicOperation(OP_Softmax_f, NN_PAD_NA, {input, beta}, outs); in softmax()
468 bool tanh(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, in tanh() argument
471 HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for float32::tanh"); in tanh()
477 return model->addBasicOperation(OP_Tanh_f, NN_PAD_NA, {input}, outs); in tanh()
484 bool add(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, HexagonModel* model) { in add() argument
486 HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for quant8_asym::add"); in add()
501 {in1, in2, in1_min, in1_max, in2_min, in2_max}, outs); in add()
504 bool average_pool_2d(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, in average_pool_2d() argument
508 HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for quant8_asym::average_pool_2d"); in average_pool_2d()
554 {input, in_min, in_max, window, stride}, outs); in average_pool_2d()
557 bool concatenation(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, in concatenation() argument
560 HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for quant8_asym::concatenation"); in concatenation()
578 return model->addBasicOperation(OP_QuantizedConcat_8, NN_PAD_NA, inputs, outs); in concatenation()
581 bool conv_2d(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, in conv_2d() argument
585 HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for quant8_asym::conv_2d"); in conv_2d()
633 {input, filter, input_min, input_max, filter_min, filter_max, stride}, outs); in conv_2d()
636 bool depthwise_conv_2d(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, in depthwise_conv_2d() argument
640 HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for quant8_asym::depthwise_conv_2d"); in depthwise_conv_2d()
691 {input, filter, input_min, input_max, filter_min, filter_max, stride}, outs); in depthwise_conv_2d()
694 bool dequantize(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, in dequantize() argument
697 HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for quant8_asym::dequantize"); in dequantize()
706 return model->addBasicOperation(OP_Dequantize, NN_PAD_NA, {input, input_min, input_max}, outs); in dequantize()
709 bool fully_connected(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, in fully_connected() argument
712 HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for quant8::fully_connected"); in fully_connected()
731 {input, weights, input_min, input_max, weights_min, weights_max}, outs); in fully_connected()
734 bool logistic(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, in logistic() argument
737 HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for quant8_asym::logistic"); in logistic()
749 outs); in logistic()
752 bool max_pool_2d(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, in max_pool_2d() argument
756 HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for quant8_asym::max_pool_2d"); in max_pool_2d()
802 OP_QuantizedMaxPool_8, pad, act, {input, input_min, input_max, window, stride}, outs); in max_pool_2d()
805 bool mul(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, HexagonModel* model) { in mul() argument
807 HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for quant8_asym::mul"); in mul()
822 {in1, in2, in1_min, in1_max, in2_min, in2_max}, outs); in mul()
825 bool relu(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, in relu() argument
828 HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for quant8_asym::relu"); in relu()
838 outs); in relu()
841 bool relu1(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, in relu1() argument
844 HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for quant8_asym::relu1"); in relu1()
856 {input, input_min, input_max, min, max}, outs); in relu1()
859 bool relu6(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, in relu6() argument
862 HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for quant8_asym::relu6"); in relu6()
873 {input, input_min, input_max, max}, outs); in relu6()
876 bool reshape(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, in reshape() argument
879 HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for quant8_asym::reshape"); in reshape()
890 {input, newdims, input_min, input_max}, outs); in reshape()
893 bool softmax(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, in softmax() argument
896 HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for quant8_asym::softmax"); in softmax()
907 {input, input_min, input_max, beta}, outs); in softmax()