| /external/tensorflow/tensorflow/contrib/rnn/kernels/ |
| D | gru_ops.cc | 52 const int64 batch_size = x_tensor->dim_size(0); in Compute()
53 const int64 input_size = x_tensor->dim_size(1); in Compute()
54 const int64 cell_size = h_prev_tensor->dim_size(1); in Compute()
59 OP_REQUIRES(ctx, h_prev_tensor->dim_size(0) == batch_size, in Compute()
61 h_prev_tensor->dim_size(0), " vs. ", in Compute()
63 OP_REQUIRES(ctx, h_prev_tensor->dim_size(1) == cell_size, in Compute()
65 "h_prev.dims(1) != cell_size: ", h_prev_tensor->dim_size(1), in Compute()
69 OP_REQUIRES(ctx, w_ru_tensor->dim_size(0) == input_size + cell_size, in Compute()
72 w_ru_tensor->dim_size(0), " vs. ", input_size + cell_size)); in Compute()
74 OP_REQUIRES(ctx, w_ru_tensor->dim_size(1) == cell_size * 2, in Compute()
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| D | lstm_ops.cc | 268 const int64 batch_size = x_tensor->dim_size(0); in Compute()
269 const int64 input_size = x_tensor->dim_size(1); in Compute()
270 const int64 cell_size = cs_prev_tensor->dim_size(1); in Compute()
273 OP_REQUIRES(ctx, cs_prev_tensor->dim_size(0) == batch_size, in Compute()
275 cs_prev_tensor->dim_size(0), " vs. ", in Compute()
277 OP_REQUIRES(ctx, cs_prev_tensor->dim_size(1) == cell_size, in Compute()
279 cs_prev_tensor->dim_size(1), " vs. ", in Compute()
282 OP_REQUIRES(ctx, h_prev_tensor->dim_size(0) == batch_size, in Compute()
284 h_prev_tensor->dim_size(0), " vs. ", in Compute()
286 OP_REQUIRES(ctx, h_prev_tensor->dim_size(1) == cell_size, in Compute()
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| /external/tensorflow/tensorflow/core/framework/ |
| D | partial_tensor_shape_test.cc | 37 EXPECT_EQ(10, s.dim_size(0)); in TEST()
38 EXPECT_EQ(5, s.dim_size(1)); in TEST()
43 EXPECT_EQ(10, s1.dim_size(0)); in TEST()
44 EXPECT_EQ(5, s1.dim_size(1)); in TEST()
45 EXPECT_EQ(10, s1.dim_size(2)); in TEST()
46 EXPECT_EQ(5, s1.dim_size(3)); in TEST()
53 EXPECT_EQ(10, s2.dim_size(0)); in TEST()
54 EXPECT_EQ(10, s3.dim_size(0)); in TEST()
55 EXPECT_EQ(5, s2.dim_size(1)); in TEST()
56 EXPECT_EQ(5, s3.dim_size(1)); in TEST()
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| /external/tensorflow/tensorflow/core/kernels/neon/ |
| D | neon_depthwise_conv_op.cc | 73 const int32 in_depth = input.dim_size(3); in Compute()
74 OP_REQUIRES(context, in_depth == filter.dim_size(2), in Compute()
77 " vs ", filter.dim_size(2))); in Compute()
78 const int32 batch = input.dim_size(0); in Compute()
79 const int32 input_rows = input.dim_size(1); in Compute()
80 const int32 input_cols = input.dim_size(2); in Compute()
82 const int32 filter_rows = filter.dim_size(0); in Compute()
83 const int32 filter_cols = filter.dim_size(1); in Compute()
84 const int32 depth_multiplier = filter.dim_size(3); in Compute()
155 result.sizes[0] = input.dim_size(3); in ToNeonDims()
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| /external/tensorflow/tensorflow/core/kernels/ |
| D | roll_op.cc | 40 const int num_dims, const gtl::ArraySlice<int>& dim_size, in DoRoll() argument
43 auto work = [input, output, num_dims, &dim_size, &threshold, &dim_range]( in DoRoll()
53 const int64 stride = dim_range[i] / dim_size[i]; in DoRoll()
54 const int shift = dim_size[i] - threshold[i]; in DoRoll()
55 const int indx = (start / stride) % dim_size[i]; in DoRoll()
58 const int shifted_indx = (indx + shift) % dim_size[i]; in DoRoll()
67 const int indx = (indices[j] + 1) % dim_size[j]; in DoRoll()
103 const int num_dims, const gtl::ArraySlice<int>& dim_size, in DoRollWithMemcpy() argument
108 auto work = [input, output, num_dims, &dim_size, &threshold, &dim_range, isd]( in DoRollWithMemcpy()
114 const int64 isd_stride = isd_range / std::max<int>(dim_size[isd], 1); in DoRollWithMemcpy()
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| D | image_resizer_state.h | 95 batch_size = input.dim_size(0); in ValidateAndCalculateOutputSize()
100 FastBoundsCheck(input.dim_size(1), std::numeric_limits<int32>::max()) && in ValidateAndCalculateOutputSize()
101 FastBoundsCheck(input.dim_size(2), in ValidateAndCalculateOutputSize()
105 in_height = static_cast<int32>(input.dim_size(1)); in ValidateAndCalculateOutputSize()
106 in_width = static_cast<int32>(input.dim_size(2)); in ValidateAndCalculateOutputSize()
107 channels = input.dim_size(3); in ValidateAndCalculateOutputSize()
114 context, input.dim_size(1) > 0 && input.dim_size(2) > 0, in ValidateAndCalculateOutputSize()
138 TensorShape({input.dim_size(0), out_height, in ValidateAndCreateOutput()
139 out_width, input.dim_size(3)}), in ValidateAndCreateOutput()
186 batch_size = input.dim_size(0); in ValidateAndCreateOutput()
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| D | sparse_slice_grad_op.cc | 51 input_indices->dim_size(1) == output_indices->dim_size(1), in Compute()
53 "ndims: got: ", input_indices->dim_size(1), " and ", in Compute()
54 output_indices->dim_size(1))); in Compute()
56 ctx, output_indices->dim_size(0) <= input_indices->dim_size(0), in Compute()
59 output_indices->dim_size(0), " and ", in Compute()
60 input_indices->dim_size(0))); in Compute()
62 ctx, backprop_val_grad->NumElements() == output_indices->dim_size(0), in Compute()
66 output_indices->dim_size(0))); in Compute()
72 const int num_dims = input_indices->dim_size(1); in Compute()
78 const int64 input_nnz = input_indices->dim_size(0); in Compute()
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| D | lrn_op.cc | 82 const int batch = static_cast<int>(in.dim_size(0)); in launch()
83 const int rows = static_cast<int>(in.dim_size(1)); in launch()
84 const int cols = static_cast<int>(in.dim_size(2)); in launch()
85 const int depth = static_cast<int>(in.dim_size(3)); in launch()
189 const int batch = static_cast<int>(in.dim_size(0)); in launch()
190 const int rows = static_cast<int>(in.dim_size(1)); in launch()
191 const int cols = static_cast<int>(in.dim_size(2)); in launch()
192 const int depth = static_cast<int>(in.dim_size(3)); in launch()
260 const int batch = static_cast<int>(in.dim_size(0)); in Compute()
261 const int rows = static_cast<int>(in.dim_size(1)); in Compute()
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| D | sparse_add_grad_op.cc | 54 a_indices->dim_size(1) == b_indices->dim_size(1) && in Compute()
55 b_indices->dim_size(1) == sum_indices->dim_size(1), in Compute()
58 a_indices->dim_size(1), b_indices->dim_size(1), in Compute()
59 sum_indices->dim_size(1))); in Compute()
61 ctx, backprop_val_grad->NumElements() == sum_indices->dim_size(0), in Compute()
65 sum_indices->dim_size(0))); in Compute()
67 const int num_dims = a_indices->dim_size(1); in Compute()
68 const int64 a_nnz = a_indices->dim_size(0); in Compute()
69 const int64 b_nnz = b_indices->dim_size(0); in Compute()
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| D | sparse_conditional_accumulator.h | 102 if (shape_.dim_size(i) != -1 && in ValidateShape()
103 shape_.dim_size(i) != tensor_shape_flat(i)) { in ValidateShape()
105 i, " to be ", shape_.dim_size(i), in ValidateShape()
111 if (shape_.dims() > 0 && shape_.dim_size(0) != -1 && in ValidateShape()
113 for (int64 i = 0; i < tensor_idx->dim_size(0); i++) { in ValidateShape()
114 if (tensor_idx->vec<int64>()(i) >= shape_.dim_size(0)) { in ValidateShape()
118 shape_.dim_size(0)); in ValidateShape()
131 if (accum_val_->dim_size(i) != tensor_val->dim_size(i)) { in ValidateShape()
133 i, " to be ", accum_val_->dim_size(i), in ValidateShape()
134 ", got ", tensor_val->dim_size(i)); in ValidateShape()
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| D | searchsorted_op.cc | 90 OP_REQUIRES(ctx, sorted_inputs_t.dim_size(0) == values_t.dim_size(0), in Compute()
104 FastBoundsCheck(sorted_inputs_t.dim_size(1), in Compute()
108 sorted_inputs_t.dim_size(1))); in Compute()
116 ctx, sorted_inputs, values, sorted_inputs_t.dim_size(0), in Compute()
117 sorted_inputs_t.dim_size(1), values_t.dim_size(1), &output)); in Compute()
131 OP_REQUIRES(ctx, sorted_inputs_t.dim_size(0) == values_t.dim_size(0), in Compute()
145 FastBoundsCheck(sorted_inputs_t.dim_size(1), in Compute()
149 sorted_inputs_t.dim_size(1))); in Compute()
157 ctx, sorted_inputs, values, sorted_inputs_t.dim_size(0), in Compute()
158 sorted_inputs_t.dim_size(1), values_t.dim_size(1), &output)); in Compute()
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| D | conv_grad_ops.cc | 63 dim->input_size = input_shape.dim_size(spatial_dim); in ConvBackpropExtractAndVerifyDimension()
64 dim->filter_size = filter_shape.dim_size(filter_spatial_dim); in ConvBackpropExtractAndVerifyDimension()
65 dim->output_size = output_shape.dim_size(spatial_dim); in ConvBackpropExtractAndVerifyDimension()
119 dims->batch_size = input_shape.dim_size(batch_dim); in ConvBackpropComputeDimensionsV2()
120 if (dims->batch_size != out_backprop_shape.dim_size(batch_dim)) { in ConvBackpropComputeDimensionsV2()
124 "outbackprop batch: ", out_backprop_shape.dim_size(batch_dim), in ConvBackpropComputeDimensionsV2()
129 dims->in_depth = input_shape.dim_size(feature_dim); in ConvBackpropComputeDimensionsV2()
133 << filter_shape.dim_size(num_dims - 2); in ConvBackpropComputeDimensionsV2()
134 if (dims->in_depth % filter_shape.dim_size(num_dims - 2)) { in ConvBackpropComputeDimensionsV2()
138 dims->out_depth = filter_shape.dim_size(num_dims - 1); in ConvBackpropComputeDimensionsV2()
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| D | mkl_transpose_op.cc | 63 mkl_##PREFIX##omatcopy('R', trans, in.dim_size(0), in.dim_size(1), 1, \
64 in.flat<T>().data(), in.dim_size(1), \
65 out->flat<T>().data(), in.dim_size(0)); \
79 'R', trans, in.dim_size(0), in.dim_size(1), alpha, in INSTANTIATE()
81 in.dim_size(1), in INSTANTIATE()
84 in.dim_size(0)); in INSTANTIATE()
93 'R', trans, in.dim_size(0), in.dim_size(1), alpha, in MKLTranspose2D()
95 in.dim_size(1), in MKLTranspose2D()
98 in.dim_size(0)); in MKLTranspose2D()
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| D | batch_matmul_op_impl.h | 162 t.flat<Scalar>().data() + slice * t.dim_size(1) * t.dim_size(2),
163 t.dim_size(1), t.dim_size(2));
168 t->flat<Scalar>().data() + slice * t->dim_size(1) * t->dim_size(2),
169 t->dim_size(1), t->dim_size(2));
210 const int64 batch_size = in_x.dim_size(0);
212 in_x.dim_size(1) * in_x.dim_size(2) * out->dim_size(2);
214 std::min(in_x.dim_size(1), in_x.dim_size(2)), out->dim_size(2));
299 const uint64 m = in_x.dim_size(adj_x ? 2 : 1);
300 const uint64 k = in_x.dim_size(adj_x ? 1 : 2);
301 const uint64 n = in_y.dim_size(adj_y ? 1 : 2);
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| D | conv_ops_fused_image_transform.cc | 659 st.batch_size = input.dim_size(0); in Compute()
660 st.out_height = input.dim_size(1); in Compute()
661 st.out_width = input.dim_size(2); in Compute()
662 st.in_height = input.dim_size(1); in Compute()
663 st.in_width = input.dim_size(2); in Compute()
664 st.channels = input.dim_size(3); in Compute()
669 {input.dim_size(0), st.out_height, st.out_width, input.dim_size(3)}); in Compute()
685 paddings.dim_size(1) == 2, in Compute()
689 (allow_legacy_scalars() && dims == 0 && paddings.dim_size(0) == 1) in Compute()
693 context, fixed_dims == paddings.dim_size(0), in Compute()
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| D | linalg_ops_common.cc | 66 input_matrix_shapes[0].dim_size(0) == input_matrix_shapes[1].dim_size(0), in ValidateSolver()
84 input_matrix_shapes[0].dim_size(0) == input_matrix_shapes[1].dim_size(0), in ValidateSquareSolver()
131 batch_shape->AddDim(in.dim_size(dim)); in AnalyzeInputs()
140 context, in.dim_size(dim) == batch_shape->dim_size(dim), in AnalyzeInputs()
148 const int64 num_rows = in.dim_size(row_dimension); in AnalyzeInputs()
149 const int64 num_cols = in.dim_size(col_dimension); in AnalyzeInputs()
229 input_matrix_shapes[i].dim_size(0), input_matrix_shapes[i].dim_size(1)); in ComputeTensorSlice()
236 ? output_matrix_shapes[i].dim_size(0) in ComputeTensorSlice()
239 ? output_matrix_shapes[i].dim_size(1) in ComputeTensorSlice()
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| D | summary_image_op.cc | 59 (tensor.dim_size(3) == 1 || tensor.dim_size(3) == 3 || in Compute()
60 tensor.dim_size(3) == 4), in Compute()
67 tensor.dim_size(0) < (1LL << 31) && in Compute()
68 tensor.dim_size(1) < (1LL << 31) && in Compute()
69 tensor.dim_size(2) < (1LL << 31) && in Compute()
70 (tensor.dim_size(1) * tensor.dim_size(2)) < (1LL << 29), in Compute()
75 const int batch_size = static_cast<int>(tensor.dim_size(0)); in Compute()
76 const int h = static_cast<int>(tensor.dim_size(1)); in Compute()
77 const int w = static_cast<int>(tensor.dim_size(2)); in Compute()
79 const int depth = static_cast<int>(tensor.dim_size(3)); in Compute()
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| D | mkl_conv_ops.h | 200 FastBoundsCheck(filter_shape.dim_size(i), in GetFilterSizeInMklOrder()
208 OP_REQUIRES(context_, input_depth == filter_shape.dim_size(2), in GetFilterSizeInMklOrder()
211 input_depth, " vs ", filter_shape.dim_size(2))); in GetFilterSizeInMklOrder()
215 static_cast<int>(filter_shape.dim_size(TF_2DFILTER_DIM_H)); in GetFilterSizeInMklOrder()
217 static_cast<int>(filter_shape.dim_size(TF_2DFILTER_DIM_W)); in GetFilterSizeInMklOrder()
219 static_cast<int>(filter_shape.dim_size(TF_2DFILTER_DIM_I)); in GetFilterSizeInMklOrder()
221 static_cast<int>(filter_shape.dim_size(TF_2DFILTER_DIM_O)); in GetFilterSizeInMklOrder()
246 OP_REQUIRES(context_, input_depth == filter_shape.dim_size(3), in GetFilterSizeInMklOrder()
249 input_depth, " vs ", filter_shape.dim_size(3))); in GetFilterSizeInMklOrder()
253 static_cast<int>(filter_shape.dim_size(TF_3DFILTER_DIM_P)); in GetFilterSizeInMklOrder()
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| /external/tensorflow/tensorflow/contrib/coder/kernels/ |
| D | range_coder_ops_util.cc | 48 (broadcast_shape.dim_size(j) != storage_shape.dim_size(j)) && in MergeAxes()
49 (storage_shape.dim_size(j) != 1))) { in MergeAxes()
56 const bool is_broadcasting = (storage_shape.dim_size(j) == 1); in MergeAxes()
63 (broadcast_shape.dim_size(j) <= 1) || in MergeAxes()
67 merged_broadcast_shape[i] *= broadcast_shape.dim_size(j); in MergeAxes()
68 merged_storage_shape[i] *= storage_shape.dim_size(j); in MergeAxes()
71 merged_broadcast_shape.push_back(broadcast_shape.dim_size(j)); in MergeAxes()
72 merged_storage_shape.push_back(storage_shape.dim_size(j)); in MergeAxes()
79 storage_stride *= storage_shape.dim_size(i); in MergeAxes()
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| /external/tensorflow/tensorflow/compiler/tf2xla/kernels/ |
| D | shape_util.cc | 30 int64 dim_size = input_shape.dim_size(i); in TensorShapeToConstant() local
31 if (!FastBoundsCheck(dim_size, std::numeric_limits<int32>::max())) { in TensorShapeToConstant()
34 " but dim ", i, " is ", dim_size); in TensorShapeToConstant()
36 vec(i) = static_cast<int32>(dim_size); in TensorShapeToConstant()
41 int64 dim_size = input_shape.dim_size(i); in TensorShapeToConstant() local
42 vec(i) = dim_size; in TensorShapeToConstant()
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| D | lrn_ops.cc | 96 const int64 batch = in_grads_shape.dim_size(0); in Compile()
97 const int64 rows = in_grads_shape.dim_size(1); in Compile()
98 const int64 cols = in_grads_shape.dim_size(2); in Compile()
99 const int64 depth = in_grads_shape.dim_size(3); in Compile()
101 ctx, in_image_shape.dim_size(0) == batch && in Compile()
102 in_image_shape.dim_size(1) == rows && in Compile()
103 in_image_shape.dim_size(2) == cols && in Compile()
104 in_image_shape.dim_size(3) == depth && in Compile()
105 out_image_shape.dim_size(0) == batch && in Compile()
106 out_image_shape.dim_size(1) == rows && in Compile()
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| /external/tensorflow/tensorflow/core/summary/ |
| D | summary_converter.cc | 184 if (bad_color_tensor.dim_size(0) < depth) { in NormalizeAndAddImages()
187 ", bad_color.size = ", bad_color_tensor.dim_size(0)); in NormalizeAndAddImages()
240 (tensor.dim_size(3) == 1 || tensor.dim_size(3) == 3 || in AddTensorAsImageToSummary()
241 tensor.dim_size(3) == 4))) { in AddTensorAsImageToSummary()
246 if (!(tensor.dim_size(0) < (1LL << 31) && tensor.dim_size(1) < (1LL << 31) && in AddTensorAsImageToSummary()
247 tensor.dim_size(2) < (1LL << 31) && in AddTensorAsImageToSummary()
248 (tensor.dim_size(1) * tensor.dim_size(2)) < (1LL << 29))) { in AddTensorAsImageToSummary()
253 const int batch_size = static_cast<int>(tensor.dim_size(0)); in AddTensorAsImageToSummary()
254 const int h = static_cast<int>(tensor.dim_size(1)); in AddTensorAsImageToSummary()
255 const int w = static_cast<int>(tensor.dim_size(2)); in AddTensorAsImageToSummary()
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| /external/tensorflow/tensorflow/contrib/tensor_forest/kernels/ |
| D | scatter_add_ndim_op.cc | 38 if (indices_tensor.shape().dim_size(0) > 0) { in Compute()
44 indices_tensor.shape().dim_size(1) + delta_dims == in Compute()
51 indices_tensor.shape().dim_size(0) == in Compute()
52 deltas_tensor.shape().dim_size(0), in Compute()
70 static_cast<int32>(indices_tensor.shape().dim_size(1)); in Compute()
76 num_data_per_index *= input_tensor.shape().dim_size(num_dims + i); in Compute()
87 const int32 m = last_size / input_tensor.shape().dim_size(j); in Compute()
93 for (int32 i = 0; i < indices_tensor.shape().dim_size(0); i++) { in Compute()
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| /external/tensorflow/tensorflow/contrib/factorization/kernels/ |
| D | wals_solver_ops.cc | 95 context, input_indices.dim_size(1) == 2, in Compute()
101 OP_REQUIRES(context, input_indices.dim_size(0) == input_values.dim_size(0), in Compute()
111 ((input_weights.dim_size(0) > 0 && in Compute()
112 factor_weights.dim_size(0) == factors.dim_size(0) && in Compute()
113 entry_weights.dim_size(0) == 0) || in Compute()
114 (input_weights.dim_size(0) == 0 && factor_weights.dim_size(0) == 0 && in Compute()
115 entry_weights.dim_size(0) == input_indices.dim_size(0))), in Compute()
122 const int64 factor_dim = factors.dim_size(1); in Compute()
123 const int64 factors_size = factors.dim_size(0); in Compute()
124 const int64 num_nonzero_elements = input_indices.dim_size(0); in Compute()
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| /external/tensorflow/tensorflow/core/grappler/utils/ |
| D | symbolic_shapes.cc | 25 dims.reserve(shape.dim_size()); in ShapeDims()
26 for (int i = 0; i < shape.dim_size(); ++i) in ShapeDims()
56 return shape.dim_size(); in Rank()
76 left.dim_size() != right.dim_size()) { in ShapesSymbolicallyEqual()
79 for (int i = 0; i < left.dim_size(); ++i) { in ShapesSymbolicallyEqual()
153 for (int i = 0; i < shape.dim_size(); ++i) { in CompareSymbolicallyShapedTensorSizes()
155 int64 dim_size = dim.size(); in CompareSymbolicallyShapedTensorSizes() local
156 if (dim_size > 0) { in CompareSymbolicallyShapedTensorSizes()
157 *defined_size *= dim_size; in CompareSymbolicallyShapedTensorSizes()
161 ++(*unknown_dims)[dim_size]; in CompareSymbolicallyShapedTensorSizes()
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