/frameworks/ml/nn/runtime/test/generated/models/ |
H A D | space_to_batch_float_3_relaxed.model.cpp | 2 void CreateModel(Model *model) { argument 8 auto input = model->addOperand(&type0); 9 auto block_size = model->addOperand(&type1); 10 auto paddings = model->addOperand(&type2); 11 auto output = model->addOperand(&type3); 14 model->setOperandValue(block_size, block_size_init, sizeof(int32_t) * 2); 16 model->setOperandValue(paddings, paddings_init, sizeof(int32_t) * 4); 17 model->addOperation(ANEURALNETWORKS_SPACE_TO_BATCH_ND, {input, block_size, paddings}, {output}); 19 model->identifyInputsAndOutputs( 23 model [all...] |
H A D | space_to_batch_quant8_1.model.cpp | 2 void CreateModel(Model *model) { argument 8 auto input = model->addOperand(&type0); 9 auto block_size = model->addOperand(&type1); 10 auto paddings = model->addOperand(&type2); 11 auto output = model->addOperand(&type3); 14 model->setOperandValue(block_size, block_size_init, sizeof(int32_t) * 2); 16 model->setOperandValue(paddings, paddings_init, sizeof(int32_t) * 4); 17 model->addOperation(ANEURALNETWORKS_SPACE_TO_BATCH_ND, {input, block_size, paddings}, {output}); 19 model->identifyInputsAndOutputs( 22 assert(model [all...] |
H A D | space_to_batch_quant8_2.model.cpp | 2 void CreateModel(Model *model) { argument 8 auto input = model->addOperand(&type0); 9 auto block_size = model->addOperand(&type1); 10 auto paddings = model->addOperand(&type2); 11 auto output = model->addOperand(&type3); 14 model->setOperandValue(block_size, block_size_init, sizeof(int32_t) * 2); 16 model->setOperandValue(paddings, paddings_init, sizeof(int32_t) * 4); 17 model->addOperation(ANEURALNETWORKS_SPACE_TO_BATCH_ND, {input, block_size, paddings}, {output}); 19 model->identifyInputsAndOutputs( 22 assert(model [all...] |
H A D | space_to_batch_quant8_3.model.cpp | 2 void CreateModel(Model *model) { argument 8 auto input = model->addOperand(&type0); 9 auto block_size = model->addOperand(&type1); 10 auto paddings = model->addOperand(&type2); 11 auto output = model->addOperand(&type3); 14 model->setOperandValue(block_size, block_size_init, sizeof(int32_t) * 2); 16 model->setOperandValue(paddings, paddings_init, sizeof(int32_t) * 4); 17 model->addOperation(ANEURALNETWORKS_SPACE_TO_BATCH_ND, {input, block_size, paddings}, {output}); 19 model->identifyInputsAndOutputs( 22 assert(model [all...] |
H A D | space_to_batch_relaxed.model.cpp | 2 void CreateModel(Model *model) { argument 8 auto input = model->addOperand(&type0); 9 auto block_size = model->addOperand(&type1); 10 auto paddings = model->addOperand(&type2); 11 auto output = model->addOperand(&type3); 14 model->setOperandValue(block_size, block_size_init, sizeof(int32_t) * 2); 16 model->setOperandValue(paddings, paddings_init, sizeof(int32_t) * 4); 17 model->addOperation(ANEURALNETWORKS_SPACE_TO_BATCH_ND, {input, block_size, paddings}, {output}); 19 model->identifyInputsAndOutputs( 23 model [all...] |
H A D | svdf.model.cpp | 2 void CreateModel(Model *model) { argument 11 auto input = model->addOperand(&type0); 12 auto weights_feature = model->addOperand(&type1); 13 auto weights_time = model->addOperand(&type2); 14 auto bias = model->addOperand(&type3); 15 auto state_in = model->addOperand(&type4); 16 auto rank_param = model->addOperand(&type5); 17 auto activation_param = model->addOperand(&type5); 18 auto state_out = model->addOperand(&type4); 19 auto output = model [all...] |
H A D | svdf2.model.cpp | 2 void CreateModel(Model *model) { argument 11 auto input = model->addOperand(&type0); 12 auto weights_feature = model->addOperand(&type1); 13 auto weights_time = model->addOperand(&type2); 14 auto bias = model->addOperand(&type3); 15 auto state_in = model->addOperand(&type4); 16 auto rank_param = model->addOperand(&type5); 17 auto activation_param = model->addOperand(&type5); 18 auto state_out = model->addOperand(&type4); 19 auto output = model [all...] |
H A D | svdf2_relaxed.model.cpp | 2 void CreateModel(Model *model) { argument 11 auto input = model->addOperand(&type0); 12 auto weights_feature = model->addOperand(&type1); 13 auto weights_time = model->addOperand(&type2); 14 auto bias = model->addOperand(&type3); 15 auto state_in = model->addOperand(&type4); 16 auto rank_param = model->addOperand(&type5); 17 auto activation_param = model->addOperand(&type5); 18 auto state_out = model->addOperand(&type4); 19 auto output = model [all...] |
H A D | svdf_relaxed.model.cpp | 2 void CreateModel(Model *model) { argument 11 auto input = model->addOperand(&type0); 12 auto weights_feature = model->addOperand(&type1); 13 auto weights_time = model->addOperand(&type2); 14 auto bias = model->addOperand(&type3); 15 auto state_in = model->addOperand(&type4); 16 auto rank_param = model->addOperand(&type5); 17 auto activation_param = model->addOperand(&type5); 18 auto state_out = model->addOperand(&type4); 19 auto output = model [all...] |
H A D | svdf_state.model.cpp | 2 void CreateModel(Model *model) { argument 11 auto input = model->addOperand(&type0); 12 auto weights_feature = model->addOperand(&type1); 13 auto weights_time = model->addOperand(&type2); 14 auto bias = model->addOperand(&type3); 15 auto state_in = model->addOperand(&type4); 16 auto rank_param = model->addOperand(&type5); 17 auto activation_param = model->addOperand(&type5); 18 auto state_out = model->addOperand(&type4); 19 auto output = model [all...] |
H A D | svdf_state_relaxed.model.cpp | 2 void CreateModel(Model *model) { argument 11 auto input = model->addOperand(&type0); 12 auto weights_feature = model->addOperand(&type1); 13 auto weights_time = model->addOperand(&type2); 14 auto bias = model->addOperand(&type3); 15 auto state_in = model->addOperand(&type4); 16 auto rank_param = model->addOperand(&type5); 17 auto activation_param = model->addOperand(&type5); 18 auto state_out = model->addOperand(&type4); 19 auto output = model [all...] |
/frameworks/ml/nn/runtime/test/specs/V1_0/ |
H A D | concat_float_2.mod.py | 17 # model 18 model = Model() variable 29 model = model.Operation("CONCATENATION", input1, input2, axis0).To(output) variable
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H A D | concat_float_3.mod.py | 17 # model 18 model = Model() variable 29 model = model.Operation("CONCATENATION", input1, input2, axis1).To(output) variable
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H A D | concat_quant8_2.mod.py | 17 # model 18 model = Model() variable 29 model = model.Operation("CONCATENATION", input1, input2, axis0).To(output) variable
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H A D | concat_quant8_3.mod.py | 17 # model 18 model = Model() variable 29 model = model.Operation("CONCATENATION", input1, input2, axis1).To(output) variable
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H A D | conv_1_h3_w2_SAME.mod.py | 0 model = Model() 10 model = model.Conv(i2, i0, i1, i4, i5, i6, i7).To(i3) variable 1 model = Model() variable
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H A D | conv_1_h3_w2_VALID.mod.py | 0 model = Model() 10 model = model.Conv(i2, i0, i1, i4, i5, i6, i7).To(i3) variable 1 model = Model() variable
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H A D | conv_3_h3_w2_SAME.mod.py | 0 model = Model() 10 model = model.Conv(i2, i0, i1, i4, i5, i6, i7).To(i3) variable 1 model = Model() variable
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H A D | conv_3_h3_w2_VALID.mod.py | 0 model = Model() 10 model = model.Conv(i2, i0, i1, i4, i5, i6, i7).To(i3) variable 1 model = Model() variable
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H A D | conv_quant8_2.mod.py | 16 model = Model() variable 28 model = model.Operation("CONV_2D", i1, f1, b1, pad_valid, stride3, variable
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H A D | depthwise_conv.mod.py | 0 model = Model() 11 model = model.DepthWiseConv(i2, i0, i1, i4, i5, i6, i7, i8).To(i3) variable 1 model = Model() variable
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H A D | depthwise_conv2d_float.mod.py | 17 model = Model() variable 27 model = model.Operation("DEPTHWISE_CONV_2D", variable
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H A D | depthwise_conv2d_float_2.mod.py | 17 model = Model() variable 27 model = model.Operation("DEPTHWISE_CONV_2D", variable
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H A D | depthwise_conv2d_float_large.mod.py | 17 model = Model() variable 27 model = model.Operation("DEPTHWISE_CONV_2D", variable
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H A D | depthwise_conv2d_float_large_2.mod.py | 17 model = Model() variable 27 model = model.Operation("DEPTHWISE_CONV_2D", variable
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