| /external/tensorflow/tensorflow/lite/toco/graph_transformations/ |
| fuse_activation_functions.cc | 21 #include "tensorflow/lite/toco/model.h"
28 ::tensorflow::Status FuseActivationFunctions::Run(Model* model,
32 const auto ac_it = model->operators.begin() + op_index;
42 Operator* op = GetOpWithOutput(*model, ac_op->inputs[0]);
46 if (CountTrueOutputs(*model, *op) > 1) {
56 int count_ops_consuming_output = CountOpsWithInput(*model, ac_op->inputs[0]);
66 if (!IsDiscardableArray(*model, op->outputs[0])) {
101 model->EraseArray(ac_op->inputs[0]);
103 model->operators.erase(ac_it)
[all...] |
| read_array_minmax_and_narrow_range_from_fake_quant.cc | 22 #include "tensorflow/lite/toco/model.h"
30 bool ApplyAttrsToArray(GraphTransformation* transformation, Model* model,
34 auto& annotated_array = model->GetArray(array_name);
55 Model* model, std::size_t op_index, bool* modified) {
57 const auto fakequant_it = model->operators.begin() + op_index;
76 changed |= ApplyAttrsToArray(this, model, *fq_op, fq_op->inputs[0]);
77 changed |= ApplyAttrsToArray(this, model, *fq_op, fq_op->outputs[0]);
|
| remove_trivial_concatenation_input.cc | 22 #include "tensorflow/lite/toco/model.h"
28 ::tensorflow::Status RemoveTrivialConcatenationInput::Run(Model* model,
39 const auto concat_it = model->operators.begin() + op_index;
47 const auto& input_array = model->GetArray(input);
63 if (IsDiscardableArray(*model, input) &&
64 CountOpsWithInput(*model, input) == 1) {
65 model->EraseArray(input);
|
| remove_trivial_fake_quant.cc | 23 #include "tensorflow/lite/toco/model.h"
31 bool IsFakeQuantTrivial(GraphTransformation* transformation, const Model& model,
42 auto* producing_op = GetOpWithOutput(model, fakequant_op.inputs[0]);
67 ::tensorflow::Status RemoveTrivialFakeQuant::Run(Model* model,
71 const auto op_it = model->operators.begin() + op_index;
78 if (!IsFakeQuantTrivial(this, *model, *fakequant_op)) {
86 *modified = RemoveTrivialPassthroughOp(this, model, op_index);
|
| resolve_batch_normalization.cc | 21 #include "tensorflow/lite/toco/model.h"
28 ::tensorflow::Status ResolveBatchNormalization::Run(Model* model,
32 auto bn_it = model->operators.begin() + op_index;
39 auto& mean_array = model->GetArray(bn_op->inputs[1]);
40 const auto& multiplier_array = model->GetArray(bn_op->inputs[2]);
41 const auto& offset_array = model->GetArray(bn_op->inputs[3]);
43 CHECK(IsConstantParameterArray(*model, bn_op->inputs[1]) &&
44 IsConstantParameterArray(*model, bn_op->inputs[2]) &&
45 IsConstantParameterArray(*model, bn_op->inputs[3])
[all...] |
| resolve_constant_select.cc | 19 #include "tensorflow/lite/toco/model.h"
30 ::tensorflow::Status ResolveConstantSelect::Run(Model* model,
34 auto it = model->operators.begin() + op_index;
43 auto& output_array = model->GetArray(op->outputs[0]);
54 if (!IsConstantParameterArray(*model, op->inputs[0])) {
57 const Array& cond_array = model->GetArray(op->inputs[0]);
79 RemoveTrivialPassthroughOp(this, model, op_index, cond_value ? 1 : 2);
|
| resolve_slice_attributes.cc | 21 #include "tensorflow/lite/toco/model.h"
27 ::tensorflow::Status ResolveSliceAttributes::Run(Model* model,
31 const auto slice_it = model->operators.begin() + op_index;
39 if (!IsConstantParameterArray(*model, op->inputs[1]))
41 if (!IsConstantParameterArray(*model, op->inputs[2]))
44 const auto& begin_array = model->GetArray(op->inputs[1]);
47 const auto& size_array = model->GetArray(op->inputs[2]);
|
| move_binary_operator_before_reshape.cc | 18 #include "tensorflow/lite/toco/model.h"
52 // collapsing of some reshapes. The WaveNet model in particular benefits from
57 ::tensorflow::Status MoveBinaryOperatorBeforeReshape::Run(Model* model,
61 const auto binary_it = model->operators.begin() + op_index;
81 IsConstantParameterArray(*model, binary_op->inputs[0]),
82 IsConstantParameterArray(*model, binary_op->inputs[1]),
99 model->GetArray(binary_op->inputs[variable_input_idx]);
107 model->GetArray(binary_op->inputs[constant_input_idx]).shape(),
108 model->GetArray(binary_op->inputs[variable_input_idx]).shape()))
[all...] |
| /external/u-boot/arch/arm/dts/ |
| am335x-bone.dts | 14 model = "TI AM335x BeagleBone";
|
| armada-388.dtsi | 50 model = "Marvell Armada 388 family SoC";
|
| at91sam9g15ek.dts | 15 model = "Atmel AT91SAM9G15-EK";
|
| at91sam9g20ek.dts | 12 model = "Atmel at91sam9g20ek";
|
| at91sam9x25ek.dts | 14 model = "Atmel AT91SAM9X25-EK";
|
| salvator-x.dtsi | 14 model = "Renesas Salvator-X board";
|
| /external/tensorflow/tensorflow/lite/toco/ |
| toco_tooling.cc | 37 // CHECK-fails if the model contains a kUnsupported operation.
38 void CheckUnsupportedOperations(const Model& model) {
40 for (auto& op : model.operators) {
154 void SetFinalDataTypeOnInputs(const TocoFlags& toco_flags, Model* model) {
169 for (int i = 0; i < model->flags.input_arrays_size(); i++) {
170 string const& array_name = model->flags.input_arrays(i).name();
171 auto* array = &model->GetArray(array_name);
186 // already mixed 8-bit / 16-bit quantized model in TFLITE format an
208 std::unique_ptr<Model> model; local
[all...] |
| /external/tensorflow/tensorflow/lite/tools/optimize/ |
| quantize_model.cc | 28 #include "tensorflow/lite/model.h"
36 ModelT* model, ErrorReporter* error_reporter) {
37 for (size_t subgraph_idx = 0; subgraph_idx < model->subgraphs.size();
39 SubGraphT* subgraph = model->subgraphs.at(subgraph_idx).get();
40 internal::SubgraphQuantizer quantizer(model, subgraph, error_reporter);
46 model->operator_codes[op->opcode_index]->builtin_code;
55 flatbuffers::Offset<Model> output_model_location =
56 Model::Pack(*builder, model);
|
| quantize_weights.cc | 27 #include "tensorflow/lite/model.h"
49 std::vector<ConsumerOpInfo> GetTensorConsumers(const ModelT* model,
53 // instead doing one sweep for the entire model.
149 const ModelT* model, const OperatorT* op, uint64_t weights_min_num_elements,
151 SubGraphT* subgraph = model->subgraphs.at(0).get();
153 model->operator_codes[op->opcode_index]->builtin_code;
182 if (model->buffers[tensor->buffer]->data.data() == nullptr) {
196 int32_t GetOrInsertDequantizeOpCodeIndex(ModelT* model) {
197 for (size_t i = 0; i < model->operator_codes.size(); ++i) {
198 if (model->operator_codes[i]->builtin_code == BuiltinOperator_DEQUANTIZE)
257 std::unique_ptr<ModelT> model; local
[all...] |
| /external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/state_space_models/ |
| test_utils.py | 34 def transition_power_test_template(test_case, model, num_steps):
35 """Tests the transition_to_powers function of a state space model."""
37 model.get_state_transition(), dtype=model.dtype)
44 model_output_tensor = model.transition_to_powers(powers=array_ops.stack(
61 def noise_accumulator_test_template(test_case, model, num_steps):
62 """Tests `model`'s transition_power_noise_accumulator."""
64 model.get_state_transition(), dtype=model.dtype)
66 model.get_noise_transform(), dtype=model.dtype
[all...] |
| /external/tensorflow/tensorflow/python/keras/layers/ |
| cudnn_recurrent_test.py | 88 model = keras.models.Model(inputs, state[0])
89 model.run_eagerly = testing_utils.should_run_eagerly()
92 state = model.predict(inputs)
107 model = keras.models.Sequential()
108 model.add(
111 model.add(layer)
112 model.add(
114 model.compile(loss='categorical_crossentropy',
116 model.fit
[all...] |
| wrappers_test.py | 77 model = keras.models.Sequential()
78 model.add(
81 model.compile(optimizer='rmsprop', loss='mse')
82 model.fit(
89 model.get_config()
91 # check whether the model variables are present in the
94 trackable_util.list_objects(model))
95 for v in model.variables:
99 model = keras.models.Sequential()
100 model.add
[all...] |
| /external/tensorflow/tensorflow/python/keras/saving/ |
| saving_utils_test.py | 61 model = testing_utils.get_small_mlp(10, 3, input_dim)
67 saving_utils.trace_model_call(model)
68 model._set_inputs(inputs)
70 fn = saving_utils.trace_model_call(model)
72 expected_outputs = {model.output_names[0]: model(inputs)}
80 model = testing_utils.get_small_mlp(10, 3, input_dim)
81 model.compile(optimizer='sgd', loss='mse')
82 model.fit(x=np.random.random((8, 5)),
87 fn = saving_utils.trace_model_call(model)
[all...] |
| /external/tensorflow/tensorflow/python/keras/ |
| models_test.py | 15 """Tests for `models.py` (model cloning, mainly)."""
44 class TestModel(keras.Model):
45 """A model subclass."""
112 model = models.Sequential(_get_layers(input_shape, add_input_layer))
115 isinstance(model._layers[0], keras.layers.InputLayer),
117 self.assertEqual(model._is_graph_network, add_input_layer)
119 # With placeholder creation -- clone model should have an InputLayer
120 # if the original model has one.
121 new_model = clone_fn(model)
125 self.assertEqual(new_model._is_graph_network, model._is_graph_network
[all...] |
| /external/dagger2/compiler/src/main/java/dagger/internal/codegen/writer/ |
| TypeNames.java | 20 import javax.lang.model.element.TypeElement;
21 import javax.lang.model.type.ArrayType;
22 import javax.lang.model.type.DeclaredType;
23 import javax.lang.model.type.NoType;
24 import javax.lang.model.type.NullType;
25 import javax.lang.model.type.PrimitiveType;
26 import javax.lang.model.type.TypeMirror;
27 import javax.lang.model.type.TypeVariable;
28 import javax.lang.model.type.WildcardType;
29 import javax.lang.model.util.SimpleTypeVisitor6
[all...] |
| /external/libxkbcommon/xkbcommon/test/ |
| rules-file.c | 33 const char *model; member in struct:test_data
53 data->rules, data->model, data->layout, data->variant, data->options
58 data->model, data->layout, data->variant, data->options);
98 .model = "my_model", .layout = "my_layout", .variant = "my_variant",
110 .model = "", .layout = "", .variant = "", .options = "",
120 .model = "pc104", .layout = "foo", .variant = "", .options = "",
130 .model = "foo", .layout = "ar", .variant = "bar", .options = "",
140 .model = NULL, .layout = "my_layout,second_layout", .variant = "my_variant",
150 .model = "", .layout = "br,al,cn,az", .variant = "",
162 .model = "my_model", .layout = "my_layout", .variant = "my_variant"
[all...] |
| /external/llvm/utils/ |
| schedcover.py | 16 def add(instr, model, resource=None):
20 entry[model] = resource
21 models.add(model)
39 for model in ordered_models:
40 if not model: model = "default"
41 sys.stdout.write(", {}".format(model))
46 for model in ordered_models:
47 if model in mapping:
48 sys.stdout.write(", {}".format(mapping[model]))
[all...] |
