| /external/u-boot/arch/arm/dts/ |
| sun8i-h3-nanopi-m1.dts | 46 model = "FriendlyArm NanoPi M1";
|
| sun8i-h3-nanopi-neo.dts | 46 model = "FriendlyARM NanoPi NEO";
|
| /external/glide/library/src/main/java/com/bumptech/glide/load/model/ |
| ModelCache.java | 1 package com.bumptech.glide.load.model;
10 * model, width and height. For a loader that takes a model and returns a url, the cache could be used to safely memoize
13 * @param <A> Some Model type that implements {@link #equals} and {@link #hashCode}.
37 * @param model The model.
43 public B get(A model, int width, int height) {
44 ModelKey<A> key = ModelKey.get(model, width, height);
53 * @param model The model
69 private A model; field in class:ModelCache.ModelKey
[all...] |
| UriLoader.java | 1 package com.bumptech.glide.load.model;
11 * remote {@link android.net.Uri}s to a wrapped {@link com.bumptech.glide.load.model.ModelLoader} that handles
12 * {@link com.bumptech.glide.load.model.GlideUrl}s.
26 public final DataFetcher<T> getResourceFetcher(Uri model, int width, int height) {
27 final String scheme = model.getScheme();
31 if (AssetUriParser.isAssetUri(model)) {
32 String path = AssetUriParser.toAssetPath(model);
35 result = getLocalUriFetcher(context, model);
38 result = urlLoader.getResourceFetcher(new GlideUrl(model.toString()), width, height);
|
| /external/grpc-grpc/tools/internal_ci/linux/ |
| grpc_android.sh | 41 --device model=Nexus6P,version=27,locale=en,orientation=portrait \
42 --device model=Nexus6P,version=26,locale=en,orientation=portrait \
43 --device model=Nexus6P,version=25,locale=en,orientation=portrait \
44 --device model=Nexus6P,version=24,locale=en,orientation=portrait \
45 --device model=Nexus6P,version=23,locale=en,orientation=portrait \
46 --device model=Nexus6,version=22,locale=en,orientation=portrait \
47 --device model=Nexus6,version=21,locale=en,orientation=portrait
|
| /external/tensorflow/tensorflow/lite/toco/graph_transformations/ |
| unroll_batch_matmul.cc | 25 #include "tensorflow/lite/toco/model.h"
35 Model* model, std::vector<std::unique_ptr<Operator>>::iterator* tail_it,
39 const auto& input_array_a = model->GetArray(input_lhs);
40 const auto& input_array_b = model->GetArray(input_rhs);
53 CreateInt32Array(model, batch_name + "/slice_a/slice/begin",
55 CreateInt32Array(model, batch_name + "/slice_a/slice/size", slice_size_a),
57 slice_a_op->outputs = {AvailableArrayName(*model, batch_name + "/slice_a")};
58 auto& slice_a_op_output = model->GetOrCreateArray(slice_a_op->outputs[0]);
60 *tail_it = model->operators.emplace(*tail_it, slice_a_op) + 1
[all...] |
| resolve_squeeze_attributes.cc | 22 #include "tensorflow/lite/toco/model.h"
28 ::tensorflow::Status ResolveSqueezeAttributes::Run(Model* model,
32 auto* squeeze_op = model->operators[op_index].get();
40 if (CountOpsWithInput(*model, squeeze_op->outputs[0]) == 1) {
41 const auto* next_op = GetOpWithInput(*model, squeeze_op->outputs[0]);
48 *modified = RemoveTrivialPassthroughOp(this, model, op_index);
|
| resolve_tensorflow_merge.cc | 21 #include "tensorflow/lite/toco/model.h"
27 ::tensorflow::Status ResolveTensorFlowMerge::Run(Model* model,
31 const auto merge_it = model->operators.begin() + op_index;
51 for (const auto& other_op : model->operators) {
61 model->EraseArray(merge_op->outputs[0]);
62 model->operators.erase(merge_it);
|
| identify_lstm_split_inputs.cc | 23 #include "tensorflow/lite/toco/model.h"
28 ::tensorflow::Status SplitLstmCellInputs::Run(Model* model,
33 auto op_it = model->operators.begin() + op_index;
49 *model, curr_op->inputs[LstmCellOperator::WEIGHTS_INPUT]) ||
51 *model, curr_op->inputs[LstmCellOperator::BIASES_INPUT])) {
56 if (!model->GetArray(curr_op->outputs[LstmCellOperator::ACTIV_OUTPUT])
65 int num_input = model->GetArray(curr_op->inputs[LstmCellOperator::DATA_INPUT])
71 model->GetArray(curr_op->outputs[LstmCellOperator::ACTIV_OUTPUT])
88 model->GetArray(curr_op->inputs[LstmCellOperator::WEIGHTS_INPUT])
[all...] |
| convert_trivial_tile_to_concat.cc | 18 #include "tensorflow/lite/toco/model.h"
24 ::tensorflow::Status ConvertTrivialTileToConcat::Run(Model* model,
28 auto tile_it = model->operators.begin() + op_index;
34 const auto& input_array = model->GetArray(tile_op->inputs[0]);
35 const auto& multiples_array = model->GetArray(tile_op->inputs[1]);
36 const auto& output_array = model->GetArray(tile_op->outputs[0]);
83 if (IsDiscardableArray(*model, tile_op->inputs[1]) &&
84 CountOpsWithInput(*model, tile_op->inputs[1]) == 1) {
85 model->EraseArray(tile_op->inputs[1])
[all...] |
| ensure_bias_vectors.cc | 21 #include "tensorflow/lite/toco/model.h"
29 int GetOutputDepthFromWeights(const Model& model, const Operator& op) {
31 const auto& weights_shape = model.GetArray(weights_name).shape();
43 bool ProcessLinearOperator(Model* model, Operator* op) {
49 if (!model->GetArray(weights_name).has_shape()) {
52 const int depth = GetOutputDepthFromWeights(*model, *op);
53 const string& bias_name = AvailableArrayName(*model, output_name + "_bias");
56 auto& bias_array = model->GetOrCreateArray(bias_name)
[all...] |
| remove_trivial_reshape.cc | 23 #include "tensorflow/lite/toco/model.h"
31 bool IsReshapeTrivial(const Model& model, const Operator& op,
37 const auto& input_array = model.GetArray(op.inputs[0]);
38 const auto& output_array = model.GetArray(op.outputs[0]);
59 if (CountOpsWithInput(model, op.outputs[0]) == 1) {
60 const auto* next_op = GetOpWithInput(model, op.outputs[0]);
62 if (!IsDiscardableArray(model, next_op->outputs[0])) {
63 // If the |next_op| output is used as a model output we need to preserve
84 ::tensorflow::Status RemoveTrivialReshape::Run(Model* model
[all...] |
| identify_dilated_conv.cc | 19 #include "tensorflow/lite/toco/model.h"
57 bool ResolveDilatedConv(Model* model, Operator* conv_base_op, Operator* stb_op,
68 auto* post_conv_op = GetOpWithInput(*model, conv_op->outputs[0]);
83 ? GetOpWithInput(*model, post_conv_op->outputs[0])
84 : GetOpWithInput(*model, conv_op->outputs[0]);
95 ? GetOpWithInput(*model, pad_op->outputs[0])
97 ? GetOpWithInput(*model, post_conv_op->outputs[0])
98 : GetOpWithInput(*model, conv_op->outputs[0]);
103 auto final_op = GetOpWithInput(*model, next_op->outputs[0])
[all...] |
| identify_l2_normalization.cc | 22 #include "tensorflow/lite/toco/model.h"
31 Model* model, const Operator* op) {
32 auto it = model->operators.begin();
33 for (; it != model->operators.end(); ++it) {
42 ::tensorflow::Status IdentifyL2Normalization::Run(Model* model,
46 const auto div_it = model->operators.begin() + op_index;
58 GetOpWithOutput(*model, div_or_mul_op->inputs[0]),
59 GetOpWithOutput(*model, div_or_mul_op->inputs[1])
[all...] |
| dequantize.cc | 22 #include "tensorflow/lite/toco/model.h"
44 Model* model, const string& array_name) {
45 for (auto it = model->operators.begin(); it != model->operators.end(); ++it) {
52 return model->operators.end();
55 void ClearArrayQuantizationParams(const string& array_name, Model* model) {
56 auto* array = &model->GetArray(array_name);
58 for (auto& input_array : *model->flags.mutable_input_arrays())
[all...] |
| fuse_broadcast_into_following_binary.cc | 21 #include "tensorflow/lite/toco/model.h"
33 bool IsBroadcastingOp(const Model& model, Operator* op) {
54 ::tensorflow::Status FuseBroadcastIntoFollowingBinary::Run(Model* model,
58 const auto binary_it = model->operators.begin() + op_index;
74 GetOpWithOutput(*model, binary_op->inputs[0]),
75 GetOpWithOutput(*model, binary_op->inputs[1]),
79 bool is_op_0_broadcast = op[0] && IsBroadcastingOp(*model, op[0]);
80 bool is_op_1_broadcast = op[1] && IsBroadcastingOp(*model, op[1])
[all...] |
| remove_trivial_slice.cc | 23 #include "tensorflow/lite/toco/model.h"
31 bool IsSliceTrivial(const Model& model, const Operator& op,
36 const auto& input_array = model.GetArray(op.inputs[0]);
37 const auto& output_array = model.GetArray(op.outputs[0]);
52 ::tensorflow::Status RemoveTrivialSlice::Run(Model* model, std::size_t op_index,
55 const auto reshape_it = model->operators.begin() + op_index;
61 if (!IsSliceTrivial(*model, *slice_op, this)) {
68 *modified = RemoveTrivialPassthroughOp(this, model, op_index)
[all...] |
| unfuse_activation_functions.cc | 21 #include "tensorflow/lite/toco/model.h"
28 ::tensorflow::Status UnfuseActivationFunctions::Run(Model* model,
32 const auto it = model->operators.begin() + op_index;
61 model->operators.emplace(it + 1, ac_op);
68 AvailableArrayName(*model, op->outputs[0] + "_unfused");
69 CHECK(!model->HasArray(tmp_array_name));
71 const auto& output_array = model->GetArray(op->outputs[0]);
72 auto& tmp_array = model->GetOrCreateArray(tmp_array_name);
|
| unpartition_embedding_lookup.cc | 20 #include "tensorflow/lite/toco/model.h"
25 ::tensorflow::Status UnpartitionEmbeddingLookup::Run(Model* model,
51 auto op_it = model->operators.begin() + op_index;
71 auto* op = GetOpWithOutput(*model, indices_partition_output_name);
96 if (!IsConstantParameterArray(*model, indices_partition_op->inputs[0])) {
100 auto& indices_data_array = model->GetArray(indices_partition_op->inputs[0]);
116 auto* op = GetOpWithOutput(*model, gather_output_name);
131 auto* op = GetOpWithOutput(*model, gather_op->inputs[1]);
164 Operator* div_op = GetOpWithOutput(*model, data_partition_op->inputs[0])
[all...] |
| /external/glide/library/src/main/java/com/bumptech/glide/load/model/file_descriptor/ |
| FileDescriptorStringLoader.java | 1 package com.bumptech.glide.load.model.file_descriptor;
8 import com.bumptech.glide.load.model.GenericLoaderFactory;
9 import com.bumptech.glide.load.model.ModelLoader;
10 import com.bumptech.glide.load.model.ModelLoaderFactory;
11 import com.bumptech.glide.load.model.StringLoader;
21 * The default factory for {@link com.bumptech.glide.load.model.file_descriptor.FileDescriptorStringLoader}s.
|
| /external/glide/library/src/main/java/com/bumptech/glide/load/model/stream/ |
| MediaStoreStreamLoader.java | 1 package com.bumptech.glide.load.model.stream;
8 import com.bumptech.glide.load.model.ModelLoader;
13 * An {@link com.bumptech.glide.load.model.ModelLoader} that can use media store uris to open pre-generated thumbnails
17 * it falls back to the wrapped {@link com.bumptech.glide.load.model.ModelLoader} to load the
30 public DataFetcher<InputStream> getResourceFetcher(Uri model, int width, int height) {
31 return new MediaStoreThumbFetcher(context, model, uriLoader.getResourceFetcher(model, width, height), width,
|
| StreamStringLoader.java | 1 package com.bumptech.glide.load.model.stream;
7 import com.bumptech.glide.load.model.GenericLoaderFactory;
8 import com.bumptech.glide.load.model.ModelLoader;
9 import com.bumptech.glide.load.model.ModelLoaderFactory;
10 import com.bumptech.glide.load.model.StringLoader;
21 * The default factory for {@link com.bumptech.glide.load.model.stream.StreamStringLoader}s.
|
| /external/tensorflow/tensorflow/python/keras/engine/ |
| correctness_test.py | 40 class MultiInputSubclassed(keras.Model):
41 """Subclassed Model that adds its inputs and then adds a bias."""
54 """Functional Model that adds its inputs and then adds a bias."""
60 return keras.Model([input_1, input_2, input_3], output)
68 model = testing_utils.get_model_from_layers([Bias()], input_shape=(1,))
69 model.compile(
73 return model
78 model = self._get_simple_bias_model()
80 history = model.fit(x, y, batch_size=3, epochs=5)
86 model = self._get_simple_bias_model(
[all...] |
| /external/androidplot/AndroidPlot-Core/src/main/java/com/androidplot/ui/ |
| FixedTableModel.java | 55 private FixedTableModel model;
field in class:FixedTableModel.FixedTableModelIterator
60 protected FixedTableModelIterator(FixedTableModel model, RectF tableRect, int numElements) {
61 this.model = model;
67 tableRect.left + model.getCellWidth(),
68 tableRect.top + model.getCellHeight());
78 return lastRect.bottom + model.getCellHeight() > tableRect.height();
82 return lastRect.right + model.getCellWidth() > tableRect.width();
95 switch (model.getOrder()) {
|
| /external/desugar/java/com/google/devtools/common/options/processor/ |
| ProcessorUtils.java | 18 import javax.lang.model.element.AnnotationMirror;
19 import javax.lang.model.element.AnnotationValue;
20 import javax.lang.model.element.Element;
21 import javax.lang.model.element.ExecutableElement;
22 import javax.lang.model.element.TypeElement;
23 import javax.lang.model.type.DeclaredType;
24 import javax.lang.model.type.TypeMirror;
25 import javax.lang.model.util.Elements;
26 import javax.lang.model.util.Types;
28 /** Convenient utilities for dealing with the javax.lang.model types. *
[all...] |
