| /frameworks/ml/nn/runtime/test/generated/models/ |
| local_response_norm_float_2.model.cpp | 12 auto output = model->addOperand(&type0); local
22 model->addOperation(ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION, {input, radius, bias, alpha, beta}, {output});
26 {output});
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| local_response_norm_float_2_relaxed.model.cpp | 12 auto output = model->addOperand(&type0); local
22 model->addOperation(ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION, {input, radius, bias, alpha, beta}, {output});
26 {output});
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| local_response_norm_float_3.model.cpp | 12 auto output = model->addOperand(&type0); local
22 model->addOperation(ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION, {input, radius, bias, alpha, beta}, {output});
26 {output});
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| local_response_norm_float_3_relaxed.model.cpp | 12 auto output = model->addOperand(&type0); local
22 model->addOperation(ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION, {input, radius, bias, alpha, beta}, {output});
26 {output});
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| local_response_norm_float_4.model.cpp | 12 auto output = model->addOperand(&type0); local
22 model->addOperation(ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION, {input, radius, bias, alpha, beta}, {output});
26 {output});
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| local_response_norm_float_4_relaxed.model.cpp | 12 auto output = model->addOperand(&type0); local
22 model->addOperation(ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION, {input, radius, bias, alpha, beta}, {output});
26 {output});
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| max_pool_float_2.model.cpp | 12 auto output = model->addOperand(&type2); local
22 model->addOperation(ANEURALNETWORKS_MAX_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output});
26 {output});
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| max_pool_float_2_relaxed.model.cpp | 12 auto output = model->addOperand(&type2); local
22 model->addOperation(ANEURALNETWORKS_MAX_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output});
26 {output});
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| max_pool_float_3.model.cpp | 12 auto output = model->addOperand(&type2); local
22 model->addOperation(ANEURALNETWORKS_MAX_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, relu6_activation}, {output});
26 {output});
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| max_pool_float_3_relaxed.model.cpp | 12 auto output = model->addOperand(&type2); local
22 model->addOperation(ANEURALNETWORKS_MAX_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, relu6_activation}, {output});
26 {output});
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| max_pool_quant8_2.model.cpp | 12 auto output = model->addOperand(&type2); local
22 model->addOperation(ANEURALNETWORKS_MAX_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output});
26 {output});
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| max_pool_quant8_3.model.cpp | 12 auto output = model->addOperand(&type2); local
22 model->addOperation(ANEURALNETWORKS_MAX_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, relu1_activation}, {output});
26 {output});
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| rnn.model.cpp | 17 auto output = model->addOperand(&type4); local
21 model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output});
25 {hidden_state_out, output});
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| rnn_relaxed.model.cpp | 17 auto output = model->addOperand(&type4); local
21 model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output});
25 {hidden_state_out, output});
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| rnn_state.model.cpp | 17 auto output = model->addOperand(&type4); local
21 model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output});
25 {hidden_state_out, output});
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| rnn_state_relaxed.model.cpp | 17 auto output = model->addOperand(&type4); local
21 model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output});
25 {hidden_state_out, output});
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| svdf.model.cpp | 19 auto output = model->addOperand(&type6); local
25 model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output});
29 {state_out, output});
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| svdf2.model.cpp | 19 auto output = model->addOperand(&type6); local
25 model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output});
29 {state_out, output});
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| svdf2_relaxed.model.cpp | 19 auto output = model->addOperand(&type6); local
25 model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output});
29 {state_out, output});
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| svdf_relaxed.model.cpp | 19 auto output = model->addOperand(&type6); local
25 model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output});
29 {state_out, output});
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| svdf_state.model.cpp | 19 auto output = model->addOperand(&type6); local
25 model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output});
29 {state_out, output});
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| svdf_state_relaxed.model.cpp | 19 auto output = model->addOperand(&type6); local
25 model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output});
29 {state_out, output});
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| /frameworks/ml/nn/runtime/test/specs/V1_0/ |
| concat_float_2.mod.py | 28 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (output_row, col)) # output variable
29 model = model.Operation("CONCATENATION", input1, input2, axis0).To(output)
38 output0 = {output: output_values}
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| concat_float_3.mod.py | 28 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (row, output_col)) # output variable
29 model = model.Operation("CONCATENATION", input1, input2, axis1).To(output)
44 output0 = {output: output_values}
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| concat_quant8_2.mod.py | 28 output = Output("output", "TENSOR_QUANT8_ASYMM", "{%d, %d}, 0.5f, 0" % (output_row, col)) variable
29 model = model.Operation("CONCATENATION", input1, input2, axis0).To(output)
38 output0 = {output: output_values}
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