| /external/tensorflow/tensorflow/python/keras/utils/ |
| D | conv_utils_test.py | 169 ndims = len(input_shape)
170 strides = (1,) * ndims
184 ndims = len(input_shape)
185 kernel_shape = (1,) * ndims
186 strides = (1,) * ndims
203 ndims = len(input_shape)
204 kernel_shape = (1,) * ndims
224 ndims = len(input_shape)
225 kernel_shape = (1,) * ndims
247 ndims = len(input_shape)
[all …]
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| /external/tensorflow/tensorflow/core/kernels/ |
| D | tile_functor_gpu.h | 34 const int32* __restrict__ buf, const int32 ndims, in TileKernel() argument
37 const int32* out_strides = buf + ndims; in TileKernel()
38 const int32* in_dim_sizes = buf + ndims * 2; in TileKernel()
42 for (int i = 0; i < ndims; ++i) { in TileKernel()
58 const int32 ndims = in.dims(); in TileSimple() local
59 gtl::InlinedVector<int32, 24> host_buf(ndims * 3); in TileSimple()
62 for (int i = 0; i < ndims; ++i) { in TileSimple()
64 host_buf[ndims + i] = out_strides[i]; in TileSimple()
65 host_buf[ndims * 2 + i] = in.dim_size(i); in TileSimple()
81 reinterpret_cast<const int32*>(dev_buf), ndims, q)); in TileSimple()
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| D | qr_op_impl.h | 141 const int ndims = input.dims(); in ComputeAsync() local
142 const int64 m = input.dim_size(ndims - 2); in ComputeAsync()
143 const int64 n = input.dim_size(ndims - 1); in ComputeAsync()
150 context, ndims >= 2, in ComputeAsync()
151 errors::InvalidArgument("Input must have rank >= 2, got ", ndims), in ComputeAsync()
159 q_shape.set_dim(ndims - 1, full_matrices_ ? m : min_size); in ComputeAsync()
164 r_shape.set_dim(ndims - 2, full_matrices_ ? m : min_size); in ComputeAsync()
179 transposed_shape.set_dim(ndims - 2, input.dim_size(ndims - 1)); in ComputeAsync()
180 transposed_shape.set_dim(ndims - 1, input.dim_size(ndims - 2)); in ComputeAsync()
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| D | transpose_functor_gpu.cu.cc | 37 const int32 ndims, T* __restrict__ dst) { in TransposeKernel() argument
39 const int32* out_strides = buf + ndims; in TransposeKernel()
40 const int32* perm = buf + ndims * 2; in TransposeKernel()
44 for (int32 i = 0; i < ndims; ++i) { in TransposeKernel()
64 const int32 ndims = in.dims(); in TransposeSimple() local
65 gtl::InlinedVector<int32, 24> host_buf(ndims * 3); in TransposeSimple()
69 for (int i = 0; i < ndims; ++i) { in TransposeSimple()
71 host_buf[ndims + i] = out_strides[i]; in TransposeSimple()
72 host_buf[ndims * 2 + i] = perm[i]; in TransposeSimple()
87 reinterpret_cast<const int32*>(dev_buf), ndims, q)); in TransposeSimple()
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| D | determinant_op.cc | 135 const int ndims = input.dims(); in ComputeAsync() local
136 const int64 n = input.dim_size(ndims - 1); in ComputeAsync()
139 context, ndims >= 2, in ComputeAsync()
140 errors::InvalidArgument("Input must have rank >= 2, got ", ndims), in ComputeAsync()
143 context, input.dim_size(ndims - 2) == n, in ComputeAsync()
145 input.dim_size(ndims - 2), " != ", n), in ComputeAsync()
150 for (int dim = 0; dim < ndims - 2; ++dim) { in ComputeAsync()
275 const int ndims = input.dims(); in ComputeAsync() local
276 const int64 n = input.dim_size(ndims - 1); in ComputeAsync()
279 context, ndims >= 2, in ComputeAsync()
[all …]
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| D | matrix_solve_op.cc | 131 const int ndims = input.dims(); in ComputeAsync() local
132 const int64 n = input.dim_size(ndims - 1); in ComputeAsync()
133 const int64 nrhs = rhs.dim_size(ndims - 1); in ComputeAsync()
136 context, ndims >= 2, in ComputeAsync()
137 errors::InvalidArgument("Input must have rank >= 2, got ", ndims), in ComputeAsync()
139 OP_REQUIRES_ASYNC(context, rhs.dims() == ndims, in ComputeAsync()
142 ndims, " != ", rhs.dims()), in ComputeAsync()
145 context, input.dim_size(ndims - 2) == n, in ComputeAsync()
147 input.dim_size(ndims - 2), " != ", n), in ComputeAsync()
149 OP_REQUIRES_ASYNC(context, rhs.dim_size(ndims - 2) == n, in ComputeAsync()
[all …]
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| D | tridiagonal_solve_op_gpu.cu.cc | 264 const int ndims = lhs.dims(); in Compute() local
266 const int64 matrix_size = lhs.dim_size(ndims - 1); in Compute()
268 for (int i = 0; i < ndims - 2; i++) { in Compute()
293 const int ndims = lhs.dims(); in ComputeWithGtsvBatched() local
304 int matrix_size = lhs.dim_size(ndims - 1); in ComputeWithGtsvBatched()
343 const int ndims = lhs.dims(); in TransposeLhsForGtsvBatched() local
347 std::vector<int> perm(ndims); in TransposeLhsForGtsvBatched()
348 perm[0] = ndims - 2; in TransposeLhsForGtsvBatched()
349 for (int i = 0; i < ndims - 2; ++i) { in TransposeLhsForGtsvBatched()
352 perm[ndims - 1] = ndims - 1; in TransposeLhsForGtsvBatched()
[all …]
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| D | reduction_ops_common.h | 98 int ndims() const { return data_reshape_.size(); }
156 CHECK_GE(helper.ndims(), 0);
159 bool is_trivial = helper.ndims() == 0 ||
160 (helper.ndims() == 1 && !helper.reduce_first_axis());
204 } else if ((helper.ndims() == 1) && helper.reduce_first_axis()) {
208 } else if ((helper.ndims() == 2) && helper.reduce_first_axis()) {
212 } else if ((helper.ndims() == 2) && !helper.reduce_first_axis()) {
216 } else if ((helper.ndims() == 3) && helper.reduce_first_axis()) {
221 } else if ((helper.ndims() == 3) && !helper.reduce_first_axis()) {
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| /external/tensorflow/tensorflow/compiler/tf2xla/kernels/ |
| D | matrix_inverse_op.cc | 32 int64 ndims = input_shape.dims(); in Compile() local
34 ctx, ndims >= 2, in Compile()
35 errors::InvalidArgument("Input must have rank >= 2, got ", ndims)); in Compile()
37 ctx, input_shape.dim_size(ndims - 2) == input_shape.dim_size(ndims - 1), in Compile()
39 input_shape.dim_size(ndims - 2), in Compile()
40 " != ", input_shape.dim_size(ndims - 1))); in Compile()
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| /external/tensorflow/tensorflow/stream_executor/ |
| D | dnn.cc | 264 BatchDescriptor::BatchDescriptor(int ndims) in BatchDescriptor() argument
268 tensor_.mutable_dimensions()->Resize(ndims + 2, 0); in BatchDescriptor()
275 std::vector<int64> bdyx_dims(ndims() + 2); in full_dims()
295 phys_strides[ndims() + 1] = 1; in full_strides()
296 for (int i = ndims(); i >= 0; i--) { in full_strides()
311 for (int i = 0; i < ndims(); i++) { in ToString()
329 for (int i = 0; i < ndims(); i++) { in ToShortString()
360 for (int i = 0; i < ndims(); i++) { in NodesPerFeatureMap()
409 FilterDescriptor::FilterDescriptor(int ndims) { in FilterDescriptor() argument
410 tensor_.mutable_dimensions()->Resize(ndims + 2, 0); in FilterDescriptor()
[all …]
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| /external/tensorflow/tensorflow/compiler/xla/service/ |
| D | triangular_solve_expander.cc | 43 int ndims = shape.rank(); in DiagonalBlocks() local
52 std::vector<int64> permutation(ndims); in DiagonalBlocks()
68 MakeEdgePaddingConfig({{0, 0}, {ndims - 2, 0}}); in DiagonalBlocks()
73 std::vector<int64> slice_sizes(ndims); in DiagonalBlocks()
75 for (int i = 0; i < ndims - 2; ++i) { in DiagonalBlocks()
80 slice_sizes[ndims - 2] = slice_sizes[ndims - 1] = block_size; in DiagonalBlocks()
81 dim_numbers.add_offset_dims(ndims - 1); in DiagonalBlocks()
82 dim_numbers.add_offset_dims(ndims); in DiagonalBlocks()
83 dim_numbers.add_start_index_map(ndims - 2); in DiagonalBlocks()
84 dim_numbers.add_start_index_map(ndims - 1); in DiagonalBlocks()
[all …]
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| /external/tensorflow/tensorflow/python/ops/distributions/ |
| D | distribution.py | 1212 if self.batch_shape.ndims is not None
1215 if self.event_shape.ndims is not None
1247 ndims = x.get_shape().ndims # != sample_ndims
1248 if ndims is None:
1250 ndims = array_ops.rank(x)
1252 math_ops.equal(ndims, 0),
1255 elif ndims == 0:
1263 elif ndims != 1:
1274 ndims = x.get_shape().ndims
1275 sample_ndims = sample_shape.ndims
[all …]
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| D | util.py | 626 ndims = x.get_shape().ndims
627 if ndims is not None and shift_value_static is not None:
628 if ndims < 2:
631 abs(shift_value_static) % ndims)
634 perm = np.roll(np.arange(ndims), shift_value_static)
650 ndims = array_ops.rank(x)
652 math_ops.less(shift, 0), math_ops.mod(-shift, ndims),
653 ndims - math_ops.mod(shift, ndims))
655 last = math_ops.range(shift, ndims)
898 ndims = prefer_static_rank(x)
[all …]
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| /external/tensorflow/tensorflow/python/ops/ragged/ |
| D | ragged_batch_gather_with_default_op.py | 84 if (default_value.shape.ndims is not 0
85 and default_value.shape.ndims is not 1):
88 if indices.shape.ndims is None:
90 if params.shape.ndims is None:
93 num_batch_dimensions = indices.shape.ndims - 1
123 params_shape[num_batch_dimensions + 1:params.shape.ndims]
147 num_batch_dimensions = indices.shape.ndims - 1
152 if params.shape.ndims == indices.shape.ndims:
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| D | ragged_concat_ops.py | 156 ndims = None
158 if ndims is None:
159 ndims = rt.shape.ndims
161 rt.shape.assert_has_rank(ndims)
163 out_ndims = ndims if (ndims is None or not stack_values) else ndims + 1
166 if stack_values and ndims == 1 and axis == 0:
176 if ndims is not None and (axis == out_ndims - 1 or axis == ndims - 1):
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| D | ragged_tensor_shape.py | 117 if dimension_size.shape.ndims is None:
121 if partitioned_dim_sizes[0].shape.ndims == 1:
123 if partitioned_dim_sizes[-1].shape.ndims == 0:
130 p.dtype for p in partitioned_dim_sizes if p.shape.ndims == 1)
173 if dim_size.shape.ndims == 1:
175 elif dim_size.shape.ndims != 0:
216 self._partitioned_dim_sizes[axis].shape.ndims == 1)
323 if lengths.shape.ndims is None:
325 elif lengths.shape.ndims > 1:
328 lengths_is_scalar = (lengths.shape.ndims == 0)
[all …]
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| D | ragged_array_ops.py | 101 if mask.shape.ndims is None:
103 elif mask.shape.ndims == 0:
124 if mask.shape.ndims > 2:
147 elif ragged_tensor.is_ragged(data) and mask.shape.ndims == 1:
171 mask, ragged_rank=min(data.ragged_rank, mask.shape.ndims - 1),
180 if mask.shape.ndims >= 2:
192 if mask.shape.ndims > 2:
195 for dim in range(mask.shape.ndims - 3, -1, -1):
447 ndims = None if input.shape.ndims is None else input.shape.ndims + 1
448 axis = ragged_util.get_positive_axis(axis, ndims)
[all …]
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| D | ragged_string_ops.py | 65 rank = input.shape.ndims
123 if input_tensor.shape.ndims is None:
126 if input_tensor.flat_values.shape.ndims > 1:
148 if input_tensor.shape.ndims == 2:
153 elif input_tensor.shape.ndims > 2:
162 elif input_tensor.shape.ndims == 0:
401 input_ndims = input.shape.ndims
500 rank = input.shape.ndims
748 if data.shape.ndims is None:
750 elif data.shape.ndims == 0:
[all …]
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| /external/tensorflow/tensorflow/python/tpu/ |
| D | tpu_sharding.py | 172 ndims = shape.ndims
173 if ndims is None:
175 if ndims <= self._shard_dimension:
208 ndims = shape.ndims
209 if ndims is None:
211 if ndims <= self._shard_dimension:
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| /external/tensorflow/tensorflow/core/framework/ |
| D | ops_util.h | 91 const int ndims = shape.dims(); in ComputeStride() local
92 gtl::InlinedVector<T, 8> strides(ndims); in ComputeStride()
94 for (int i = ndims - 1; i >= 0; --i) { in ComputeStride()
104 const int ndims = shape.rank(); in ComputeEigenStrides() local
105 gtl::InlinedVector<T, 8> strides(ndims); in ComputeEigenStrides()
107 for (int i = ndims - 1; i >= 0; --i) { in ComputeEigenStrides()
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| /external/tensorflow/tensorflow/go/ |
| D | operation.go | 100 ndims := C.TF_GraphGetTensorNumDims(p.Op.g.c, port, status.c)
107 if ndims < 0 {
110 if ndims == 0 {
113 dims := make([]C.int64_t, ndims)
114 C.TF_GraphGetTensorShape(p.Op.g.c, port, &dims[0], ndims, status.c)
119 ret := Shape{dims: make([]int64, ndims)}
120 for i := 0; i < int(ndims); i++ {
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| /external/tensorflow/tensorflow/python/ops/ |
| D | array_ops.py | 804 if optimize and input_shape.ndims is not None:
805 return constant(input_shape.ndims, dtypes.int32, name=name)
1498 if value_shape.ndims is not None:
1499 if axis < -value_shape.ndims or axis >= value_shape.ndims:
1501 (axis, -value_shape.ndims, value_shape.ndims))
1661 ndims_mask = shape_mask.ndims
2188 ndims = a_shape.ndims
2189 if ndims is not None:
2190 if ndims < 2:
2194 perm = list(range(ndims - 2)) + [ndims - 1] + [ndims - 2]
[all …]
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| D | image_ops_impl.py | 212 if image.get_shape().ndims is None:
277 if image.get_shape().ndims is None:
425 if shape.ndims == 3 or shape.ndims is None:
434 elif shape.ndims == 4:
540 if shape.ndims == 3 or shape.ndims is None:
542 elif shape.ndims == 4:
580 if shape.ndims == 3 or shape.ndims is None:
582 elif shape.ndims == 4:
701 if shape.ndims == 3 or shape.ndims is None:
703 elif shape.ndims == 4:
[all …]
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| /external/tensorflow/tensorflow/python/framework/ |
| D | common_shapes.py | 84 if shape_x.ndims is None or shape_y.ndims is None:
102 if shape_x.ndims is None or shape_y.ndims is None:
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| /external/tensorflow/tensorflow/python/ops/linalg/ |
| D | linear_operator_util.py | 242 if sh.ndims is not None and sh.ndims < 2:
389 if a.shape.ndims is None or b.shape.ndims is None:
393 if a.shape.ndims >= b.shape.ndims:
401 b_extra_ndims = b.shape.ndims - a.shape.ndims
449 (np.arange(b_extra_ndims, b.shape.ndims),
|