| /frameworks/ml/nn/runtime/test/generated/models/ |
| D | random_multinomial_float16.model.cpp | 10 auto sample_count = model->addOperand(&type1); in CreateModel() local
15 model->setOperandValue(sample_count, sample_count_init, sizeof(int32_t) * 1); in CreateModel()
18 model->addOperation(ANEURALNETWORKS_RANDOM_MULTINOMIAL, {input0, sample_count, seeds}, {output}); in CreateModel()
38 auto sample_count = model->addOperand(&type1); in CreateModel_dynamic_output_shape() local
43 model->setOperandValue(sample_count, sample_count_init, sizeof(int32_t) * 1); in CreateModel_dynamic_output_shape()
46 model->addOperation(ANEURALNETWORKS_RANDOM_MULTINOMIAL, {input0, sample_count, seeds}, {output}); in CreateModel_dynamic_output_shape()
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| D | random_multinomial.model.cpp | 10 auto sample_count = model->addOperand(&type1); in CreateModel() local
15 model->setOperandValue(sample_count, sample_count_init, sizeof(int32_t) * 1); in CreateModel()
18 model->addOperation(ANEURALNETWORKS_RANDOM_MULTINOMIAL, {input0, sample_count, seeds}, {output}); in CreateModel()
38 auto sample_count = model->addOperand(&type1); in CreateModel_dynamic_output_shape() local
43 model->setOperandValue(sample_count, sample_count_init, sizeof(int32_t) * 1); in CreateModel_dynamic_output_shape()
46 model->addOperation(ANEURALNETWORKS_RANDOM_MULTINOMIAL, {input0, sample_count, seeds}, {output}); in CreateModel_dynamic_output_shape()
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| /frameworks/base/tests/JankBench/scripts/ |
| D | itr_collect.py | 91 sample_count = 0
95 sample_count = len(res.durations)
112 per_test_sample_count[test].append(int(sample_count))
116 print "\t%s:\t%0.2f (%0.2f avg. sample count)" % (test, geo_run, sample_count)
118 print "\tOverall:\t%0.2f (%0.2f avg. sample count)" % (geo_run, sample_count)
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| D | collect.py | 129 sample_count = 0
136 sample_count += len(res.durations)
162 sample_count /= len(scoremap[run_id][test])
165 per_test_sample_count[test].append(int(sample_count))
169 print "\t%s:\t%0.2f (%0.2f avg. sample count)" % (test, geo_run, sample_count)
171 print "\tOverall:\t%0.2f (%0.2f avg. sample count)" % (geo_run, sample_count)
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| /frameworks/ml/nn/runtime/test/specs/V1_2/ |
| D | random_multinomial_float16.mod.py | 18 sample_count = Int32Scalar("sample_count", 128) variable
22 model = Model().Operation("RANDOM_MULTINOMIAL", input0, sample_count, seeds).To(output0)
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| D | random_multinomial.mod.py | 18 sample_count = Int32Scalar("sample_count", 128) variable
22 model = Model().Operation("RANDOM_MULTINOMIAL", input0, sample_count, seeds).To(output0)
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| /frameworks/native/libs/vr/libpdx_default_transport/ |
| D | pdx_benchmarks.cpp | 932 uint64_t sample_count = 0; in ClientCommand() local
942 if (sample_count < percent_50 && in ClientCommand()
943 (sample_count + sample_buckets[i]) >= percent_50) { in ClientCommand()
946 if (sample_count < percent_90 && in ClientCommand()
947 (sample_count + sample_buckets[i]) >= percent_90) { in ClientCommand()
950 if (sample_count < percent_95 && in ClientCommand()
951 (sample_count + sample_buckets[i]) >= percent_95) { in ClientCommand()
954 if (sample_count < percent_99 && in ClientCommand()
955 (sample_count + sample_buckets[i]) >= percent_99) { in ClientCommand()
958 sample_count += sample_buckets[i]; in ClientCommand()
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| /frameworks/ml/nn/common/operations/ |
| D | Multinomial.cpp | 66 const uint32_t sample_count = in Prepare() local
70 outputShape->dimensions = {batch_size, sample_count}; in Prepare()
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| D | MultinomialTest.cpp | 67 const int sample_count = batch_size_ * class_size_; in Invoke() local
68 for (int i = 0; i < sample_count; ++i) { in Invoke()
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| /frameworks/ml/nn/runtime/test/ |
| D | TestValidateOperations.cpp | 2047 ANeuralNetworksOperandType sample_count = {.type = ANEURALNETWORKS_INT32, in randomMultinomialOpTest() local
2066 {input, sample_count, seed}, {output}); in randomMultinomialOpTest()
|