/external/annotation-tools/asmx/src/org/objectweb/asm/tree/ |
H A D | MultiANewArrayInsnNode.java | 50 public int dims; field in class:MultiANewArrayInsnNode 56 * @param dims number of dimensions of the array to allocate. 58 public MultiANewArrayInsnNode(final String desc, final int dims) { argument 61 this.dims = dims; 65 mv.visitMultiANewArrayInsn(desc, dims);
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/external/tensorflow/tensorflow/core/kernels/ |
H A D | reverse_sequence_op_gpu.cu.cc | 27 #define DEFINE_GPU_SPEC(T, Tlen, dims) \ 28 template class generator::ReverseGenerator<T, Tlen, dims>; \ 29 template struct functor::ReverseSequence<GPUDevice, T, Tlen, dims>; 31 #define DEFINE_GPU_SPEC_LEN(T, dims) \ 32 DEFINE_GPU_SPEC(T, int32, dims); \ 33 DEFINE_GPU_SPEC(T, int64, dims);
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H A D | scan_ops.h | 34 Eigen::array<bool, 3> dims; local 35 dims[0] = false; 36 dims[1] = reverse; 37 dims[2] = false; 39 To32Bit(in).reverse(dims).scan(1, reducer, exclusive).reverse(dims);
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H A D | conv_grad_input_ops.cc | 228 input_sizes.dims())); 233 ConvBackpropDimensions dims; variable 238 strides_, padding_, data_format_, &dims)); 255 dims.spatial_dims[0].input_size, dims.spatial_dims[0].filter_size, 256 dims.spatial_dims[0].stride, padding_, 257 &dims.spatial_dims[0].output_size, &pad_top, &pad_bottom)); 261 dims.spatial_dims[1].input_size, dims.spatial_dims[1].filter_size, 262 dims 348 ConvBackpropDimensions dims; variable 720 ConvBackpropDimensions dims; local [all...] |
H A D | reduction_ops_common.cc | 36 const int dims = data_reshape_.size(); local 38 for (int i = reduce_first_axis_; i < dims; i += 2) { 41 for (int i = !reduce_first_axis_; i < dims; i += 2) { 48 const int dims = data_reshape_.size(); local 49 const int unreduced_dims = (dims + !reduce_first_axis_) / 2; 50 gtl::InlinedVector<int32, 8> perm(dims); 54 for (int i = unreduced_dims; i < dims; i++) { 66 if (index < -data.dims() || index >= data.dims()) { 68 " for input with ", data.dims(), [all...] |
H A D | conv_grad_filter_ops.cc | 225 filter_sizes.dims())); 230 ConvBackpropDimensions dims; variable 235 out_backprop.shape(), strides_, padding_, data_format_, &dims)); 252 dims.spatial_dims[0].input_size, dims.spatial_dims[0].filter_size, 253 dims.spatial_dims[0].stride, padding_, 254 &dims.spatial_dims[0].output_size, &pad_top, &pad_bottom)); 258 dims.spatial_dims[1].input_size, dims.spatial_dims[1].filter_size, 259 dims 344 ConvBackpropDimensions dims; variable 649 ConvBackpropDimensions dims; local [all...] |
/external/tensorflow/tensorflow/go/ |
H A D | shape.go | 30 dims []int64 35 return Shape{dims: make([]int64, 0)} 45 return Shape{dims: cpy} 51 if s.dims == nil { 54 return len(s.dims) 65 return s.dims[dim] 71 if s.dims == nil { 74 for _, size := range s.dims { 85 if s.dims == nil { 88 cpy := make([]int64, len(s.dims)) [all...] |
/external/javassist/src/main/javassist/bytecode/analysis/ |
H A D | MultiArrayType.java | 28 private int dims; field in class:MultiArrayType 30 public MultiArrayType(MultiType component, int dims) { argument 33 this.dims = dims; 45 String name = arrayName(clazz.getName(), dims); 59 return dims; 63 return dims == 1 ? (Type)component : new MultiArrayType(component, dims - 1); 98 if (typeDims > dims) 101 if (typeDims < dims) { [all...] |
/external/tensorflow/tensorflow/contrib/lite/kernels/internal/ |
H A D | types.h | 31 inline bool NextIndex(const int num_dims, const int* dims, int* current) { argument 33 TFLITE_DCHECK(dims != nullptr); 38 TFLITE_DCHECK_GE(dims[idx], current_val); 39 if (dims[idx] == current_val) { 56 inline size_t ReducedOutputOffset(const int num_dims, const int* dims, argument 60 TFLITE_DCHECK(dims != nullptr); 75 offset = offset * static_cast<size_t>(dims[idx]) + 82 inline int Offset(const Dims<4>& dims, int i0, int i1, int i2, int i3) { argument 83 TFLITE_DCHECK(i0 >= 0 && i0 < dims.sizes[0]); 84 TFLITE_DCHECK(i1 >= 0 && i1 < dims 91 Offset(const Dims<4>& dims, int* index) argument 117 RequiredBufferSizeForDims(const Dims<4>& dims) argument 126 IsPackedWithoutStrides(const Dims<N>& dims) argument [all...] |
/external/tensorflow/tensorflow/core/kernels/neon/ |
H A D | types.h | 31 inline int Offset(const Dims<4>& dims, int i0, int i1, int i2, int i3) { argument 32 DCHECK(i0 >= 0 && i0 < dims.sizes[0]); 33 DCHECK(i1 >= 0 && i1 < dims.sizes[1]); 34 DCHECK(i2 >= 0 && i2 < dims.sizes[2]); 35 DCHECK(i3 >= 0 && i3 < dims.sizes[3]); 36 return i0 * dims.strides[0] + i1 * dims.strides[1] + i2 * dims.strides[2] + 37 i3 * dims.strides[3]; 62 inline int RequiredBufferSizeForDims(const Dims<4>& dims) { argument [all...] |
/external/webrtc/webrtc/modules/audio_coding/codecs/isac/main/source/ |
H A D | fft.h | 40 int WebRtcIsac_Fftns (unsigned int ndim, const int dims[], double Re[], double Im[],
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/external/tensorflow/tensorflow/compiler/tf2xla/kernels/ |
H A D | unpack_op.cc | 50 if (axis < 0) axis += input_shape.dims(); 52 OP_REQUIRES(ctx, 0 <= axis && axis < input_shape.dims(), 54 -input_shape.dims(), ", ", 55 input_shape.dims(), ")")); 58 ctx, input_shape.dims() > 0 && input_shape.dim_size(axis) == num, 67 std::vector<int64> start_indices(input_shape.dims(), 0); 68 std::vector<int64> limit_indices(input_shape.dims()); 69 std::vector<int64> strides(input_shape.dims(), 1); 70 for (int i = 0; i < input_shape.dims(); ++i) {
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H A D | l2loss_op.cc | 41 std::vector<int64> dims(input_shape.dims()); 42 std::iota(dims.begin(), dims.end(), 0); 46 ctx->SetOutput(0, b->Div(b->Reduce(b->Mul(x, x), zero, add, dims), two));
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H A D | diag_op.cc | 94 auto dims = input_shape.dim_sizes(); variable 95 OP_REQUIRES(ctx, !dims.empty(), 96 errors::InvalidArgument("Expected 1 <= dims, got shape ", 118 std::vector<int64> new_dims(dims.size() * 2); 119 std::copy(dims.begin(), dims.end(), new_dims.begin()); 120 std::copy(dims.begin(), dims.end(), new_dims.begin() + dims.size()); 137 auto dims variable 208 auto dims = input_shape.dim_sizes(); variable 238 auto dims = input_shape.dim_sizes(); variable [all...] |
H A D | transpose_op.cc | 46 const int dims = input_shape.dims(); variable 47 OP_REQUIRES(ctx, dims == perm_tensor_shape.num_elements(), 49 input_shape.dims(), 54 OP_REQUIRES_OK(ctx, ctx->ConstantInputReshaped(1, {dims}, &literal)); 56 std::vector<int32> perm(dims); 61 // Check whether permutation is a permutation of integers of [0 .. dims). 62 gtl::InlinedVector<bool, 8> bits(dims); 64 for (int i = 0; i < dims; ++i) { 67 ctx, 0 <= d && d < dims, [all...] |
H A D | shape_util.cc | 26 const int dims = input_shape.dims(); local 29 for (int i = 0; i < dims; ++i) { 40 for (int i = 0; i < dims; ++i) {
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/external/tensorflow/tensorflow/contrib/distributions/python/kernel_tests/ |
H A D | vector_diffeomixture_test.py | 39 dims = 4 46 np.float32([2.]*dims), 50 num_rows=dims, 54 diag=np.linspace(2.5, 3.5, dims, dtype=np.float32), 68 dims = 4 75 np.float32([2.]*dims), 79 num_rows=dims, 83 diag=np.linspace(2.5, 3.5, dims, dtype=np.float32), 97 dims = 4 104 np.float32([2.]*dims), [all...] |
/external/androidplot/AndroidPlot-Core/src/main/java/com/androidplot/ui/ |
H A D | Resizable.java | 33 * the Plot class is responsible for updating the LayoutManager. Note that while dims
35 * this method should take care not to make changes to dims as this will affect parent
37 * @param dims
39 public void layout(final DisplayDimensions dims);
argument
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/external/tensorflow/tensorflow/contrib/lite/toco/graph_transformations/ |
H A D | resolve_pad_attributes.cc | 41 const std::vector<int>& dims = array.shape().dims(); local 42 CHECK_EQ(dims.size(), 2); 46 for (int i = 0; i < dims[0]; ++i) {
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H A D | convert_trivial_transpose_to_reshape.cc | 40 std::vector<int> const& dims = output_array.shape().dims(); member in class:toco::std 42 for (int i = 0; i < dims.size(); i++) { 43 if (dims[i] != 1) { 64 1, static_cast<int>(dims.size())}; 68 shape_buffer.data = dims;
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/external/mesa3d/src/mesa/drivers/dri/i965/ |
H A D | brw_vec4_surface_builder.h | 35 unsigned dims, unsigned size, 41 unsigned dims, unsigned size, 48 unsigned dims, unsigned rsize, unsigned op, 53 const src_reg &addr, unsigned dims, unsigned size); 58 unsigned dims, unsigned size); 64 unsigned dims, unsigned rsize, unsigned op,
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/external/tensorflow/tensorflow/contrib/lite/kernels/ |
H A D | concatenation.cc | 52 if (axis < 0) axis += t0->dims->size; 54 TF_LITE_ENSURE(context, axis < t0->dims->size); 58 TF_LITE_ENSURE(context, t0->dims->size <= 4); 65 int sum_axis = t0->dims->data[axis]; 68 TF_LITE_ENSURE_EQ(context, t->dims->size, t0->dims->size); 74 for (int d = 0; d < t0->dims->size; ++d) { 76 sum_axis += t->dims->data[axis]; 78 TF_LITE_ENSURE_EQ(context, t->dims->data[d], t0->dims [all...] |
H A D | bidirectional_sequence_rnn.cc | 67 const int batch_size = input->dims->data[0]; 68 const int max_time = input->dims->data[1]; 69 const int fw_num_units = fw_input_weights->dims->data[0]; 70 const int bw_num_units = bw_input_weights->dims->data[0]; 71 TF_LITE_ASSERT_EQ(input->dims->data[2], fw_input_weights->dims->data[1]); 72 TF_LITE_ASSERT_EQ(input->dims->data[2], bw_input_weights->dims->data[1]); 73 TF_LITE_ASSERT_EQ(fw_input_weights->dims->data[0], fw_bias->dims [all...] |
/external/tensorflow/tensorflow/core/util/ |
H A D | mkl_util_test.cc | 32 memory::dims a_dims = {N, C, H, W}; 44 memory::dims b_dims = {N, C, H, W}; 64 memory::dims dim1 = {3, 4}; 65 memory::dims strides1 = {1, 3}; 70 EXPECT_EQ(a_md1.data.dims[0], 3); 71 EXPECT_EQ(a_md1.data.dims[1], 4); 76 memory::dims dim2 = {3, 4}; 77 memory::dims strides2 = {4, 1}; 82 EXPECT_EQ(b_md2.data.dims[0], 3); 83 EXPECT_EQ(b_md2.data.dims[ [all...] |
H A D | util.cc | 101 const int dims = shape.dims(); local 102 if (dims == 0) return ""; 103 if (dims == 1) return strings::StrCat("[", flat, "]"); 106 gtl::InlinedVector<int64, 32> strides(dims); 108 for (int i = dims - 2; i >= 0; i--) { 115 for (int i = 0; i < dims; i++) {
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