| /external/tensorflow/tensorflow/lite/g3doc/examples/trained/ |
| D | index.md | 1 # Pre-trained models for TensorFlow Lite
3 There are a variety of already trained, open source models you can use
5 Using pre-trained TensorFlow Lite models lets you add machine learning
8 models for use with TensorFlow Lite.
10 You can start browsing TensorFlow Lite models right away based on general use
12 larger set of models on [TensorFlow Hub](https://tfhub.dev/s?deployment-
15 **Important:** TensorFlow Hub lists both regular TensorFlow models and
16 TensorFlow Lite format models. These model formats are not interchangeable.
17 TensorFlow models can be converted into TensorFlow Lite models, but that process
25 to discover models for use with TensorFlow Lite:
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| /external/tensorflow/tensorflow/lite/g3doc/examples/ |
| D | _index.yaml | 3 title: Models
10 Overview of models for TensorFlow Lite
16 TensorFlow Lite uses TensorFlow models converted into a smaller, more efficient machine
17 learning (ML) model format. You can use pre-trained models with TensorFlow Lite, modify
18 existing models, or build your own TensorFlow models and then convert them to
19 TensorFlow Lite format. TensorFlow Lite models can perform almost any task a regular
32 <a href="/lite/models/convert/index"><h3 class="no-link">Have a TensorFlow model?</h3></a>
33 Skip to the <a href="/lite/models/convert/index">Convert</a> section for information about
35 path: /lite/models/convert/index
40 For guidance on getting models for your use case,
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| /external/tensorflow/tensorflow/lite/g3doc/android/ |
| D | index.md | 3 TensorFlow Lite lets you run TensorFlow machine learning (ML) models in your
5 execution environments for running models on Android quickly and efficiently,
41 <a href="../models">
42 <h3 class="no-link hide-from-toc" id="ml-models" data-text="ML models">ML models</h3></a>
43 Learn about choosing and using ML models with TensorFlow Lite, see the
44 <a href="../models">Models</a> docs.
53 ## Machine learning models
55 TensorFlow Lite uses TensorFlow models that are converted into a smaller,
57 models with TensorFlow Lite on Android, or build your own TensorFlow models and
60 **Key Point:** TensorFlow Lite models and TensorFlow models have a *different
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| /external/XNNPACK/bench/ |
| D | qs8-gemm-e2e.cc | 18 #include "models/models.h"
29 models::ExecutionPlanFactory model_factory, in GEMMEnd2EndBenchmark()
83 …static void qs8_gemm_4x8c4__aarch32_neondot_cortex_a55(benchmark::State& state, models::ExecutionP… in qs8_gemm_4x8c4__aarch32_neondot_cortex_a55()
93 …static void qs8_gemm_4x8c4__aarch32_neondot_ld64(benchmark::State& state, models::ExecutionPlanFac… in qs8_gemm_4x8c4__aarch32_neondot_ld64()
110 …static void qs8_gemm_4x8__aarch32_neon_mlal_lane_cortex_a53(benchmark::State& state, models::Execu… in BENCHMARK_QS8_END2END()
120 …static void qs8_gemm_4x8__aarch32_neon_mlal_lane_prfm_cortex_a53(benchmark::State& state, models::… in qs8_gemm_4x8__aarch32_neon_mlal_lane_prfm_cortex_a53()
130 …static void qs8_gemm_4x8__aarch32_neon_mlal_lane_cortex_a7(benchmark::State& state, models::Execut… in qs8_gemm_4x8__aarch32_neon_mlal_lane_cortex_a7()
140 …static void qs8_gemm_4x8__aarch32_neon_mlal_lane_prfm_cortex_a7(benchmark::State& state, models::E… in qs8_gemm_4x8__aarch32_neon_mlal_lane_prfm_cortex_a7()
150 …static void qs8_gemm_4x8__aarch32_neon_mlal_lane_ld64(benchmark::State& state, models::ExecutionPl… in qs8_gemm_4x8__aarch32_neon_mlal_lane_ld64()
160 …static void qs8_gemm_4x8__aarch32_neon_mlal_lane_prfm_ld64(benchmark::State& state, models::Execut… in qs8_gemm_4x8__aarch32_neon_mlal_lane_prfm_ld64()
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| D | qu8-gemm-e2e.cc | 18 #include "models/models.h"
29 models::ExecutionPlanFactory model_factory, in GEMMEnd2EndBenchmark()
82 …static void qu8_gemm_4x8__aarch32_neon_mlal_lane_cortex_a53(benchmark::State& state, models::Execu… in qu8_gemm_4x8__aarch32_neon_mlal_lane_cortex_a53()
92 …static void qu8_gemm_4x8__aarch32_neon_mlal_lane_prfm_cortex_a53(benchmark::State& state, models::… in qu8_gemm_4x8__aarch32_neon_mlal_lane_prfm_cortex_a53()
102 …static void qu8_gemm_4x8__aarch32_neon_mlal_lane_cortex_a7(benchmark::State& state, models::Execut… in qu8_gemm_4x8__aarch32_neon_mlal_lane_cortex_a7()
112 …static void qu8_gemm_4x8__aarch32_neon_mlal_lane_prfm_cortex_a7(benchmark::State& state, models::E… in qu8_gemm_4x8__aarch32_neon_mlal_lane_prfm_cortex_a7()
122 …static void qu8_gemm_4x8__aarch32_neon_mlal_lane_ld64(benchmark::State& state, models::ExecutionPl… in qu8_gemm_4x8__aarch32_neon_mlal_lane_ld64()
132 …static void qu8_gemm_4x8__aarch32_neon_mlal_lane_prfm_ld64(benchmark::State& state, models::Execut… in qu8_gemm_4x8__aarch32_neon_mlal_lane_prfm_ld64()
152 …static void qu8_gemm_4x16c4__aarch64_neondot_cortex_a55(benchmark::State& state, models::Execution… in BENCHMARK_QU8_END2END()
162 …static void qu8_gemm_4x16c4__aarch64_neondot_ld128(benchmark::State& state, models::ExecutionPlanF… in qu8_gemm_4x16c4__aarch64_neondot_ld128()
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| D | qs8-dwconv-e2e.cc | 15 #include "models/models.h"
25 models::ExecutionPlanFactory model_factory, in DWConvEnd2EndBenchmark()
77 …static void qs8_dwconv_up8x9__neon_mul8_ld64(benchmark::State& state, models::ExecutionPlanFactory… in qs8_dwconv_up8x9__neon_mul8_ld64()
83 …static void qs8_dwconv_up16x9__neon_mul8_ld64(benchmark::State& state, models::ExecutionPlanFactor… in qs8_dwconv_up16x9__neon_mul8_ld64()
89 …static void qs8_dwconv_up16x9__neon_mul8_ld128(benchmark::State& state, models::ExecutionPlanFacto… in qs8_dwconv_up16x9__neon_mul8_ld128()
95 …static void qs8_dwconv_up8x9__neon_mla8_ld64(benchmark::State& state, models::ExecutionPlanFactory… in qs8_dwconv_up8x9__neon_mla8_ld64()
101 …static void qs8_dwconv_up16x9__neon_mla8_ld64(benchmark::State& state, models::ExecutionPlanFactor… in qs8_dwconv_up16x9__neon_mla8_ld64()
107 …static void qs8_dwconv_up16x9__neon_mla8_ld128(benchmark::State& state, models::ExecutionPlanFacto… in qs8_dwconv_up16x9__neon_mla8_ld128()
113 …static void qs8_dwconv_up8x9__neon_mul16(benchmark::State& state, models::ExecutionPlanFactory mod… in qs8_dwconv_up8x9__neon_mul16()
119 …static void qs8_dwconv_up16x9__neon_mul16(benchmark::State& state, models::ExecutionPlanFactory mo… in qs8_dwconv_up16x9__neon_mul16()
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| D | qu8-dwconv-e2e.cc | 17 #include "models/models.h"
27 models::ExecutionPlanFactory model_factory, in DWConvEnd2EndBenchmark()
79 …static void qu8_dwconv_up8x9__neon_mul8(benchmark::State& state, models::ExecutionPlanFactory mode… in qu8_dwconv_up8x9__neon_mul8()
85 …static void qu8_dwconv_up16x9__neon_mul8(benchmark::State& state, models::ExecutionPlanFactory mod… in qu8_dwconv_up16x9__neon_mul8()
91 …static void qu8_dwconv_up24x9__neon_mul8(benchmark::State& state, models::ExecutionPlanFactory mod… in qu8_dwconv_up24x9__neon_mul8()
97 …static void qu8_dwconv_up32x9__neon_mul8(benchmark::State& state, models::ExecutionPlanFactory mod… in qu8_dwconv_up32x9__neon_mul8()
103 …static void qu8_dwconv_up8x9__neon_mul16(benchmark::State& state, models::ExecutionPlanFactory mod… in qu8_dwconv_up8x9__neon_mul16()
109 …static void qu8_dwconv_up16x9__neon_mul16(benchmark::State& state, models::ExecutionPlanFactory mo… in qu8_dwconv_up16x9__neon_mul16()
115 …static void qu8_dwconv_up24x9__neon_mul16(benchmark::State& state, models::ExecutionPlanFactory mo… in qu8_dwconv_up24x9__neon_mul16()
121 …static void qu8_dwconv_up32x9__neon_mul16(benchmark::State& state, models::ExecutionPlanFactory mo… in qu8_dwconv_up32x9__neon_mul16()
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| D | f32-dwconv-e2e.cc | 18 #include "models/models.h"
28 models::ExecutionPlanFactory model_factory, in DWConvEnd2EndBenchmark()
83 …static void f32_dwconv_up4x9__aarch64_neonfma(benchmark::State& state, models::ExecutionPlanFactor… in f32_dwconv_up4x9__aarch64_neonfma()
91 …static void f32_dwconv_up4x9__aarch64_neonfma_cortex_a55(benchmark::State& state, models::Executio… in f32_dwconv_up4x9__aarch64_neonfma_cortex_a55()
104 static void f32_dwconv_up4x9__neon(benchmark::State& state, models::ExecutionPlanFactory model) { in f32_dwconv_up4x9__neon()
112 …static void f32_dwconv_up4x9__neon_acc2(benchmark::State& state, models::ExecutionPlanFactory mode… in f32_dwconv_up4x9__neon_acc2()
120 static void f32_dwconv_up8x9__neon(benchmark::State& state, models::ExecutionPlanFactory model) { in f32_dwconv_up8x9__neon()
128 …static void f32_dwconv_up8x9__neon_acc2(benchmark::State& state, models::ExecutionPlanFactory mode… in f32_dwconv_up8x9__neon_acc2()
136 static void f32_dwconv_up16x9__neon(benchmark::State& state, models::ExecutionPlanFactory model) { in f32_dwconv_up16x9__neon()
144 …static void f32_dwconv_up16x9__neon_acc2(benchmark::State& state, models::ExecutionPlanFactory mod… in f32_dwconv_up16x9__neon_acc2()
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| D | f32-gemm-e2e.cc | 18 #include "models/models.h"
29 models::ExecutionPlanFactory model_factory, in GEMMEnd2EndBenchmark()
110 models::ExecutionPlanFactory model_factory, in GEMMEnd2EndBenchmark()
167 …static void f32_gemm_4x2__aarch64_neonfma_cortex_a75(benchmark::State& state, models::ExecutionPla… in f32_gemm_4x2__aarch64_neonfma_cortex_a75()
178 …static void f32_gemm_4x2__aarch64_neonfma_prfm_cortex_a75(benchmark::State& state, models::Executi… in f32_gemm_4x2__aarch64_neonfma_prfm_cortex_a75()
189 …static void f32_gemm_4x2__aarch64_neonfma_ld64(benchmark::State& state, models::ExecutionPlanFacto… in f32_gemm_4x2__aarch64_neonfma_ld64()
200 …static void f32_gemm_4x12__aarch64_neonfma_cortex_a53(benchmark::State& state, models::ExecutionPl… in f32_gemm_4x12__aarch64_neonfma_cortex_a53()
211 …static void f32_gemm_4x8__aarch64_neonfma_cortex_a53(benchmark::State& state, models::ExecutionPla… in f32_gemm_4x8__aarch64_neonfma_cortex_a53()
222 …static void f32_gemm_4x8__aarch64_neonfma_prfm_cortex_a53(benchmark::State& state, models::Executi… in f32_gemm_4x8__aarch64_neonfma_prfm_cortex_a53()
233 …static void f32_gemm_4x8__aarch64_neonfma_cortex_a55(benchmark::State& state, models::ExecutionPla… in f32_gemm_4x8__aarch64_neonfma_cortex_a55()
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| D | end2end.cc | 17 #include "models/models.h"
22 models::ExecutionPlanFactory model_factory) in End2EndBenchmark()
56 End2EndBenchmark(state, models::FP32MobileNetV1); in FP32MobileNetV1()
60 End2EndBenchmark(state, models::FP32MobileNetV2); in FP32MobileNetV2()
64 End2EndBenchmark(state, models::FP32MobileNetV3Large); in FP32MobileNetV3Large()
68 End2EndBenchmark(state, models::FP32MobileNetV3Small); in FP32MobileNetV3Small()
73 return models::FP32SparseMobileNetV1(0.8f, threadpool); in FP32Sparse80MobileNetV1()
79 return models::FP32SparseMobileNetV2(0.8f, threadpool); in FP32Sparse80MobileNetV2()
85 return models::FP32SparseMobileNetV3Large(0.8f, threadpool); in FP32Sparse80MobileNetV3Large()
91 return models::FP32SparseMobileNetV3Small(0.8f, threadpool); in FP32Sparse80MobileNetV3Small()
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| /external/autotest/frontend/afe/ |
| D | rpc_interface_unittest.py | 17 models, rpc_interface, rpc_utils)
30 _hqe_status = models.HostQueueEntry.Status
93 return models.Job.objects.get(id=job_id)
98 label2 = models.Label.objects.create(name='bluetooth', platform=False)
108 host2 = models.Host.objects.create(hostname='test_host2', leased=False)
118 host2 = models.Host.objects.create(hostname='test_host2', leased=False)
124 models.Host,
132 host2 = models.Host.smart_get(host2.id)
141 leased_host = models.Host.objects.create(hostname='leased_host',
167 host3 = models.Host.objects.create(hostname='test_host3', leased=False)
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| D | shard_heartbeat_unittest.py | 14 from autotest_lib.frontend.afe import models
34 assigned = models.Job.assign_to_shard(shard, [])
45 assigned = models.Job.assign_to_shard(shard, [])
54 assigned_jobs = models.Job.assign_to_shard(shard, [known_job.id])
65 assigned = models.Job.assign_to_shard(shard, [])
75 assigned = models.Job.assign_to_shard(shard, [])
81 old = models.Job.SKIP_JOBS_CREATED_BEFORE
83 models.Job.SKIP_JOBS_CREATED_BEFORE = value
86 models.Job.SKIP_JOBS_CREATED_BEFORE = old
92 @param host: A models.Host object.
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| D | models_test.py | 10 from autotest_lib.frontend.afe import models, model_logic
31 everyone_acl = models.AclGroup.objects.get(name='Everyone')
38 models.AclGroup.on_host_membership_change()
55 models.Host.objects.populate_relationships(
56 [host], models.HostAttribute, 'attribute_list')
61 previous_config = models.RESPECT_STATIC_ATTRIBUTES
62 models.RESPECT_STATIC_ATTRIBUTES = False
63 host1 = models.Host.objects.create(hostname='test_host1')
73 models.RESPECT_STATIC_ATTRIBUTES = previous_config
77 previous_config = models.RESPECT_STATIC_ATTRIBUTES
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| D | frontend_test_utils.py | 6 from autotest_lib.frontend.afe import models, model_attributes
14 if models.DroneSet.drone_sets_enabled():
15 models.DroneSet.objects.create(
16 name=models.DroneSet.default_drone_set_name())
18 acl_group = models.AclGroup.objects.create(name='my_acl')
19 acl_group.users.add(models.User.current_user())
21 self.hosts = [models.Host.objects.create(hostname=hostname)
27 models.AclGroup.smart_get('Everyone').hosts = []
29 self.labels = [models.Label.objects.create(name=name) for name in
33 platform = models.Label.objects.create(name='myplatform', platform=True)
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| D | rpc_interface.py | 48 from autotest_lib.frontend.afe import (model_attributes, model_logic, models,
50 from autotest_lib.frontend.tko import models as tko_models
62 from django.db.models import Count
96 label_model = models.Label.smart_get(id)
116 label_model = models.Label.smart_get(id)
127 hosts.append(models.Host.smart_get(h.id))
146 # models.Label.add_object() throws model_logic.ValidationError
151 label = models.Label.add_object(name=name, **kwargs)
171 @raises models.Label.DoesNotExist: If the label with id doesn't exist.
173 label = models.Label.smart_get(id)
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| /external/autotest/frontend/tko/ |
| D | rpc_interface.py | 3 from django.db import models as dbmodels
6 from autotest_lib.frontend.afe import models as afe_models, readonly_connection
7 from autotest_lib.frontend.tko import models, tko_rpc_utils
15 models.TestView.list_objects(filter_data))
19 return models.TestView.query_count(filter_data)
47 query = models.TestView.objects.get_query_set_with_joins(filter_data)
49 query = models.TestView.query_objects(filter_data, initial_query=query,
51 count_alias, count_sql = models.TestView.objects.get_count_sql(query)
55 query = models.TestView.apply_presentation(query, filter_data)
68 query = models.TestView.objects.get_query_set_with_joins(filter_data)
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| /external/tensorflow/tensorflow/lite/delegates/flex/ |
| D | build_def.bzl | 29 models,
36 models: TFLite models to interpret.
47 if type(models) != type([]):
48 models = [models]
50 # List all flex ops from models.
52 ["$(location %s)" % f for f in models],
67 srcs = models,
70 message = "Listing flex ops from %s..." % ",".join(models),
93 models = [],
98 """A rule to generate a flex delegate with only ops to run listed models.
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| /external/aws-sdk-java-v2/codegen/src/test/java/software/amazon/awssdk/codegen/poet/ |
| D | ClientTestModels.java | 32 * A static set of service models that can be used for testing purposes.
41 C2jModels models = C2jModels.builder() in awsJsonServiceModels() local
47 return new IntermediateModelBuilder(models).build(); in awsJsonServiceModels()
54 C2jModels models = C2jModels.builder() in awsQueryCompatibleJsonServiceModels() local
60 return new IntermediateModelBuilder(models).build(); in awsQueryCompatibleJsonServiceModels()
67 C2jModels models = C2jModels.builder() in bearerAuthServiceModels() local
73 return new IntermediateModelBuilder(models).build(); in bearerAuthServiceModels()
80 C2jModels models = C2jModels.builder() in restJsonServiceModels() local
86 return new IntermediateModelBuilder(models).build(); in restJsonServiceModels()
98 C2jModels models = C2jModels in queryServiceModels() local
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| /external/google-cloud-java/java-aiplatform/proto-google-cloud-aiplatform-v1beta1/src/main/java/com/google/cloud/aiplatform/v1beta1/ |
| D | ModelExplanation.java | 80 * For Models that predict only one output, such as regression Models that
82 * predicted output. For Models that predict multiple outputs, such as
83 * multiclass Models that predict multiple classes, each element explains one
93 * NOTE: Currently AutoML tabular classification Models produce only one
113 * For Models that predict only one output, such as regression Models that
115 * predicted output. For Models that predict multiple outputs, such as
116 * multiclass Models that predict multiple classes, each element explains one
126 * NOTE: Currently AutoML tabular classification Models produce only one
147 * For Models that predict only one output, such as regression Models that
149 * predicted output. For Models that predict multiple outputs, such as
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| D | ModelExplanationOrBuilder.java | 32 * For Models that predict only one output, such as regression Models that
34 * predicted output. For Models that predict multiple outputs, such as
35 * multiclass Models that predict multiple classes, each element explains one
45 * NOTE: Currently AutoML tabular classification Models produce only one
62 * For Models that predict only one output, such as regression Models that
64 * predicted output. For Models that predict multiple outputs, such as
65 * multiclass Models that predict multiple classes, each element explains one
75 * NOTE: Currently AutoML tabular classification Models produce only one
92 * For Models that predict only one output, such as regression Models that
94 * predicted output. For Models that predict multiple outputs, such as
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| /external/google-cloud-java/java-aiplatform/proto-google-cloud-aiplatform-v1/src/main/java/com/google/cloud/aiplatform/v1/ |
| D | ModelExplanation.java | 80 * For Models that predict only one output, such as regression Models that
82 * predicted output. For Models that predict multiple outputs, such as
83 * multiclass Models that predict multiple classes, each element explains one
93 * NOTE: Currently AutoML tabular classification Models produce only one
113 * For Models that predict only one output, such as regression Models that
115 * predicted output. For Models that predict multiple outputs, such as
116 * multiclass Models that predict multiple classes, each element explains one
126 * NOTE: Currently AutoML tabular classification Models produce only one
147 * For Models that predict only one output, such as regression Models that
149 * predicted output. For Models that predict multiple outputs, such as
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| D | ModelExplanationOrBuilder.java | 32 * For Models that predict only one output, such as regression Models that
34 * predicted output. For Models that predict multiple outputs, such as
35 * multiclass Models that predict multiple classes, each element explains one
45 * NOTE: Currently AutoML tabular classification Models produce only one
62 * For Models that predict only one output, such as regression Models that
64 * predicted output. For Models that predict multiple outputs, such as
65 * multiclass Models that predict multiple classes, each element explains one
75 * NOTE: Currently AutoML tabular classification Models produce only one
92 * For Models that predict only one output, such as regression Models that
94 * predicted output. For Models that predict multiple outputs, such as
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| /external/tensorflow/tensorflow/lite/g3doc/examples/build/ |
| D | index.md | 1 # Build TensorFlow Lite models
4 your TensorFlow models with the intention of converting to the TensorFlow
5 Lite model format. The machine learning (ML) models you use with TensorFlow
12 [Convert models overview](../convert/)
16 see the [Modify models overview](../modify/model_maker) for guidance.
28 constraints for TensorFlow Lite models and build your model with these
34 growing in compute power and specialized hardware compatibility, the models
36 * **Size of models** - The overall complexity of a model, including data
41 with a machine learning model is limited on a mobile or edge device. Models
47 regular TensorFlow models. As you develop a model for use with TensorFlow
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| /external/flatbuffers/grpc/examples/go/greeter/server/ |
| D | main.go | 10 models "github.com/google/flatbuffers/grpc/examples/go/greeter/models" packageName
19 models.UnimplementedGreeterServer
22 func (s *greeterServer) SayHello(ctx context.Context, request *models.HelloRequest) (*flatbuffers.B…
32 models.HelloReplyStart(b)
33 models.HelloReplyAddMessage(b, idx)
34 b.Finish(models.HelloReplyEnd(b))
38 func (s *greeterServer) SayManyHellos(request *models.HelloRequest, stream models.Greeter_SayManyHe…
50 models.HelloReplyStart(b)
51 models.HelloReplyAddMessage(b, idx)
52 b.Finish(models.HelloReplyEnd(b))
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| /external/sdv/vsomeip/third_party/boost/iterator/doc/ |
| D | permutation_iterator_ref.rst | 48 ``permutation_iterator`` models
51 ``permutation_iterator`` models the same iterator traversal concepts
55 If ``IndexIterator`` models Single Pass Iterator and
56 ``ElementIterator`` models Readable Iterator then
57 ``permutation_iterator`` models Input Iterator.
59 If ``IndexIterator`` models Forward Traversal Iterator and
60 ``ElementIterator`` models Readable Lvalue Iterator then
61 ``permutation_iterator`` models Forward Iterator.
63 If ``IndexIterator`` models Bidirectional Traversal Iterator and
64 ``ElementIterator`` models Readable Lvalue Iterator then
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