| /external/tensorflow/tensorflow/core/kernels/ |
| image_resizer_state.h | 59 OP_REQUIRES(context, input.dims() == 4,
63 OP_REQUIRES(context, shape_t.dims() == 1,
138 OP_REQUIRES(context, input.dims() == 4,
147 OP_REQUIRES(context, original_image.dims() == 4,
|
| mfcc_op_test.cc | 62 EXPECT_EQ(3, mfcc_tensor.dims());
|
| padding_fifo_queue_op.cc | 49 OP_REQUIRES(context, shape.dims() >= 0,
|
| sparse_softmax_op.cc | 85 gtl::InlinedVector<int64, 4> dims(rank);
86 std::iota(dims.begin(), dims.end(), 0);
88 const ArraySlice<int64> kReorderDims(dims);
|
| quantized_concat_op.cc | 47 Eigen::array<Eigen::DenseIndex, 1> dims; local
48 dims[0] = n;
49 typename TTypes<T, 1>::UnalignedConstTensor input_array(src, dims);
50 typename TTypes<T, 1>::UnalignedTensor output_array(dst, dims);
135 context, in.dims() == input_dims || (input_is_scalar && in_is_scalar),
156 *output_concat_dim += in.dims() > 0 ? in.dim_size(concat_dim) : 1;
184 const int input_dims = values[0].dims();
208 if (output_shape.dims() == 0) {
|
| spectrogram_op_test.cc | 60 EXPECT_EQ(3, spectrogram_tensor.dims());
94 EXPECT_EQ(3, spectrogram_tensor.dims());
|
| summary_audio_op_test.cc | 94 ASSERT_EQ(0, out_tensor->dims());
128 ASSERT_EQ(0, out_tensor->dims());
|
| summary_op_test.cc | 68 ASSERT_EQ(0, out_tensor->dims());
88 ASSERT_EQ(0, out_tensor->dims());
108 ASSERT_EQ(0, out_tensor->dims());
177 ASSERT_EQ(0, out_tensor->dims());
205 ASSERT_EQ(0, out_tensor->dims());
234 ASSERT_EQ(0, out_tensor->dims());
308 ASSERT_EQ(0, out_tensor->dims());
342 ASSERT_EQ(0, out_tensor->dims());
|
| /frameworks/base/cmds/statsd/src/subscriber/ |
| SubscriberReporter.cpp | 120 void getStatsDimensionsValueHelper(const vector<FieldValue>& dims, size_t* index, int depth,
122 size_t count = dims.size();
124 const auto& dim = dims[*index];
155 getStatsDimensionsValueHelper(dims, index, depth + 1, dim.mField.getPrefix(depth + 1),
|
| /packages/apps/LegacyCamera/jni/ |
| feature_mos_jni.cpp | 587 int* dims = new int[2]; local
598 dims[0] = width;
599 dims[1] = height;
610 env->SetIntArrayRegion(bytes, imageSize, 2, (jint*) dims);
612 delete[] dims;
642 unsigned char* dims = new unsigned char[8]; local
644 dims[0] = (unsigned char)(width >> 24);
645 dims[1] = (unsigned char)(width >> 16);
646 dims[2] = (unsigned char)(width >> 8);
647 dims[3] = (unsigned char)width
[all...] |
| /art/tools/veridex/ |
| veridex.h | 49 VeriClass(Primitive::Type k, uint8_t dims, const DexFile::ClassDef* cl)
50 : kind_(k), dimensions_(dims), class_def_(cl) {}
|
| /external/mesa3d/src/mesa/drivers/dri/i915/ |
| intel_tex_copy.c | 82 intelCopyTexSubImage(struct gl_context *ctx, GLuint dims,
101 _mesa_meta_CopyTexSubImage(ctx, dims, texImage,
|
| /external/tensorflow/tensorflow/compiler/tf2xla/kernels/ |
| select_op.cc | 74 std::vector<int64> dim_order(then_shape.dims());
75 dim_order[0] = then_shape.dims() - 1;
|
| /external/tensorflow/tensorflow/contrib/boosted_trees/lib/learner/common/stats/ |
| node-stats.h | 109 QCHECK(grad_stats.first.t.dims() == 2)
111 grad_stats.first.t.dims());
115 QCHECK(grad_stats.second.t.dims() == 3)
117 grad_stats.second.t.dims());
158 QCHECK(grad_stats.first.t.dims() == 2)
160 grad_stats.first.t.dims());
164 QCHECK(grad_stats.second.t.dims() == 2)
166 grad_stats.second.t.dims());
|
| /external/tensorflow/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/ |
| Interpreter.java | 127 * Resizes idx-th input of the native model to the given dims.
131 public void resizeInput(int idx, @NotNull int[] dims) {
135 wrapper.resizeInput(idx, dims);
|
| NativeInterpreterWrapper.java | 84 int[] dims = shapeOf(inputs[i]); local
85 sizes[i] = dims;
86 numsOfBytes[i] = dataType.elemByteSize() * numElements(dims);
115 void resizeInput(int idx, int[] dims) {
116 resizeInput(interpreterHandle, errorHandle, idx, dims);
120 long interpreterHandle, long errorHandle, int inputIdx, int[] dims);
|
| /external/tensorflow/tensorflow/contrib/lite/kernels/ |
| kernel_util.h | 23 inline int NumDimensions(const TfLiteTensor* t) { return t->dims->size; }
25 return t->dims->data[dim];
|
| topk_v2.cc | 44 TF_LITE_ENSURE_MSG(context, input->dims->size >= 1,
47 TF_LITE_ENSURE_MSG(context, k <= input->dims->data[num_dimensions - 1],
53 output_indexes_shape->data[i] = input->dims->data[i];
54 output_values_shape->data[i] = input->dims->data[i];
195 const int32 row_size = input->dims->data[input->dims->size - 1];
197 for (int i = 0; i < input->dims->size - 1; ++i) {
198 num_rows *= input->dims->data[i];
|
| /external/tensorflow/tensorflow/core/framework/ |
| lookup_interface.cc | 52 for (int i = 0; i < key_shape().dims(); ++i) {
53 expected_value_shape.RemoveDim(expected_value_shape.dims() - 1);
|
| numeric_op.h | 85 switch (a.dims()) {
105 "We only handle up to Tensor::dims() up to 8, not ", a.dims()));
|
| /external/tensorflow/tensorflow/core/util/sparse/ |
| dim_comparator.h | 53 CHECK_LE(order.size(), shape.size()) << "Can only sort up to dims";
74 const int64 a_row, const int64 b_row, const int dims) {
75 for (int d = 0; d < dims; ++d) {
|
| group_iterator.h | 80 GroupIterable(Tensor ix, Tensor vals, int dims, const VarDimArray& group_dims)
81 : ix_(ix), vals_(vals), dims_(dims), group_dims_(group_dims) {}
|
| /hardware/qcom/neuralnetworks/hvxservice/1.0/ |
| HexagonUtils.cpp | 109 std::vector<uint32_t> getAlignedDimensions(const std::vector<uint32_t>& dims, uint32_t N) {
111 N, dims.size(),
112 "Error: constant data dimensions " << dims.size() << " exceeds alignment of " << N);
113 std::vector<uint32_t> dimensions(N - dims.size(), 1);
114 dimensions.insert(dimensions.end(), dims.begin(), dims.end());
192 hexagon_nn_output make_hexagon_nn_output(const std::vector<uint32_t>& dims, uint32_t size) {
193 std::vector<uint32_t> alignedDims = getAlignedDimensions(dims, 4);
|
| /external/tensorflow/tensorflow/contrib/layers/python/layers/ |
| utils.py | 243 dims = shape.dims
244 if dims is None:
245 raise ValueError('dims of shape must be known but is None')
246 if len(dims) < min_rank:
248 len(dims)))
249 value = dims[dim].value
|
| /external/tensorflow/tensorflow/contrib/lite/java/src/main/native/ |
| tensor_jni.cc | 212 int num_dims = tensor->dims->size;
234 int num_dims = tensor->dims->size;
236 jint* dims = env->GetIntArrayElements(result, nullptr); local
238 dims[i] = static_cast<jint>(tensor->dims->data[i]);
240 env->ReleaseIntArrayElements(result, dims, 0);
|
