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/external/tensorflow/tensorflow/contrib/rnn/kernels/
Dgru_ops.cc52 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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Dlstm_ops.cc265 const int64 batch_size = x_tensor->dim_size(0); in Compute()
266 const int64 input_size = x_tensor->dim_size(1); in Compute()
267 const int64 cell_size = cs_prev_tensor->dim_size(1); in Compute()
270 OP_REQUIRES(ctx, cs_prev_tensor->dim_size(0) == batch_size, in Compute()
272 cs_prev_tensor->dim_size(0), " vs. ", in Compute()
274 OP_REQUIRES(ctx, cs_prev_tensor->dim_size(1) == cell_size, in Compute()
276 cs_prev_tensor->dim_size(1), " vs. ", in Compute()
279 OP_REQUIRES(ctx, h_prev_tensor->dim_size(0) == batch_size, in Compute()
281 h_prev_tensor->dim_size(0), " vs. ", in Compute()
283 OP_REQUIRES(ctx, h_prev_tensor->dim_size(1) == cell_size, in Compute()
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/external/tensorflow/tensorflow/core/framework/
Dpartial_tensor_shape_test.cc37 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/
Dneon_depthwise_conv_op.cc73 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/
Droll_op.cc39 const int num_dims, const gtl::ArraySlice<int>& dim_size, in DoRoll() argument
42 auto work = [input, output, num_dims, &dim_size, &threshold, &dim_range]( in DoRoll()
52 const int64 stride = dim_range[i] / dim_size[i]; in DoRoll()
53 const int shift = dim_size[i] - threshold[i]; in DoRoll()
54 const int indx = (start / stride) % dim_size[i]; in DoRoll()
57 const int shifted_indx = (indx + shift) % dim_size[i]; in DoRoll()
66 const int indx = (indices[j] + 1) % dim_size[j]; in DoRoll()
102 const int num_dims, const gtl::ArraySlice<int>& dim_size, in DoRollWithMemcpy() argument
107 auto work = [input, output, num_dims, &dim_size, &threshold, &dim_range, isd]( in DoRollWithMemcpy()
113 const int64 isd_stride = isd_range / std::max<int>(dim_size[isd], 1); in DoRollWithMemcpy()
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Dconv_ops.cc67 if (filter.dim_size(0) == 1 && filter.dim_size(1) == 1 && row_stride == 1 && in operator ()()
77 conv_width *= output->dim_size(i); in operator ()()
84 output->shaped<T, 2>({conv_width, filter.dim_size(3)}), in operator ()()
85 input.shaped<T, 2>({conv_width, filter.dim_size(2)}), in operator ()()
86 filter.shaped<T, 2>({filter.dim_size(2), filter.dim_size(3)}), in operator ()()
88 } else if (filter.dim_size(0) == input.dim_size(1) && in operator ()()
89 filter.dim_size(1) == input.dim_size(2) && row_dilation == 1 && in operator ()()
94 filter.dim_size(0) * filter.dim_size(1) * filter.dim_size(2); in operator ()()
100 output->shaped<T, 2>({input.dim_size(0), filter.dim_size(3)}), in operator ()()
101 input.shaped<T, 2>({input.dim_size(0), k}), in operator ()()
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Dimage_resizer_state.h70 batch_size = input.dim_size(0); in ValidateAndCalculateOutputSize()
75 FastBoundsCheck(input.dim_size(1), std::numeric_limits<int32>::max()) && in ValidateAndCalculateOutputSize()
76 FastBoundsCheck(input.dim_size(2), in ValidateAndCalculateOutputSize()
80 in_height = static_cast<int32>(input.dim_size(1)); in ValidateAndCalculateOutputSize()
81 in_width = static_cast<int32>(input.dim_size(2)); in ValidateAndCalculateOutputSize()
82 channels = input.dim_size(3); in ValidateAndCalculateOutputSize()
89 context, input.dim_size(1) > 0 && input.dim_size(2) > 0, in ValidateAndCalculateOutputSize()
113 TensorShape({input.dim_size(0), out_height, in ValidateAndCreateOutput()
114 out_width, input.dim_size(3)}), in ValidateAndCreateOutput()
152 batch_size = input.dim_size(0); in ValidateAndCreateOutput()
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Dlrn_op.cc78 const int batch = static_cast<int>(in.dim_size(0)); in launch()
79 const int rows = static_cast<int>(in.dim_size(1)); in launch()
80 const int cols = static_cast<int>(in.dim_size(2)); in launch()
81 const int depth = static_cast<int>(in.dim_size(3)); in launch()
185 const int batch = static_cast<int>(in.dim_size(0)); in launch()
186 const int rows = static_cast<int>(in.dim_size(1)); in launch()
187 const int cols = static_cast<int>(in.dim_size(2)); in launch()
188 const int depth = static_cast<int>(in.dim_size(3)); in launch()
256 const int batch = static_cast<int>(in.dim_size(0)); in Compute()
257 const int rows = static_cast<int>(in.dim_size(1)); in Compute()
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Dmkl_transpose_op.cc54 mkl_##PREFIX##omatcopy('R', trans, in.dim_size(0), in.dim_size(1), 1, \
55 in.flat<T>().data(), in.dim_size(1), \
56 out->flat<T>().data(), in.dim_size(0)); \
68 mkl_comatcopy('R', trans, in.dim_size(0), in.dim_size(1), alpha, in INSTANTIATE()
70 in.dim_size(1), in INSTANTIATE()
72 in.dim_size(0)); in INSTANTIATE()
79 mkl_zomatcopy('R', trans, in.dim_size(0), in.dim_size(1), alpha, in MKLTranspose2D()
81 in.dim_size(1), in MKLTranspose2D()
83 in.dim_size(0)); in MKLTranspose2D()
Dsparse_add_grad_op.cc54 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()
Dsparse_conditional_accumulator.h102 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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Dbatch_matmul_op_impl.h160 t.flat<Scalar>().data() + slice * t.dim_size(1) * t.dim_size(2),
161 t.dim_size(1), t.dim_size(2));
166 t->flat<Scalar>().data() + slice * t->dim_size(1) * t->dim_size(2),
167 t->dim_size(1), t->dim_size(2));
208 const int64 batch_size = in_x.dim_size(0);
210 in_x.dim_size(1) * in_x.dim_size(2) * out->dim_size(2);
212 std::min(in_x.dim_size(1), in_x.dim_size(2)), out->dim_size(2));
298 const uint64 m = in_x.dim_size(adj_x ? 2 : 1);
299 const uint64 k = in_x.dim_size(adj_x ? 1 : 2);
300 const uint64 n = in_y.dim_size(adj_y ? 1 : 2);
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Dconv_ops_fused.cc658 st.batch_size = input.dim_size(0); in Compute()
659 st.out_height = input.dim_size(1); in Compute()
660 st.out_width = input.dim_size(2); in Compute()
661 st.in_height = input.dim_size(1); in Compute()
662 st.in_width = input.dim_size(2); in Compute()
663 st.channels = input.dim_size(3); in Compute()
668 {input.dim_size(0), st.out_height, st.out_width, input.dim_size(3)}); in Compute()
684 paddings.dim_size(1) == 2, in Compute()
688 (allow_legacy_scalars() && dims == 0 && paddings.dim_size(0) == 1) in Compute()
692 context, fixed_dims == paddings.dim_size(0), in Compute()
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Dlinalg_ops_common.cc66 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()
Dsummary_image_op.cc59 (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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Ddilation_ops.cc75 const int input_rows = input.dim_size(1); in ParseSizes()
76 const int input_cols = input.dim_size(2); in ParseSizes()
77 const int depth = input.dim_size(3); in ParseSizes()
92 const int filter_rows = filter.dim_size(0); in ParseSizes()
93 const int filter_cols = filter.dim_size(1); in ParseSizes()
94 OP_REQUIRES(context, depth == filter.dim_size(2), in ParseSizes()
97 filter.dim_size(2))); in ParseSizes()
136 const int batch = input.dim_size(0); in Compute()
137 const int depth = input.dim_size(3); in Compute()
235 const int batch = input.dim_size(0); in Compute()
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Dconv_grad_ops.cc53 dim->input_size = input_shape.dim_size(spatial_dim); in ConvBackpropExtractAndVerifyDimensionV2()
54 dim->filter_size = filter_shape.dim_size(filter_spatial_dim); in ConvBackpropExtractAndVerifyDimensionV2()
55 dim->output_size = output_shape.dim_size(spatial_dim); in ConvBackpropExtractAndVerifyDimensionV2()
117 dims->batch_size = input_shape.dim_size(batch_dim); in ConvBackpropComputeDimensionsV2()
118 if (dims->batch_size != out_backprop_shape.dim_size(batch_dim)) { in ConvBackpropComputeDimensionsV2()
122 "outbackprop batch: ", out_backprop_shape.dim_size(batch_dim), in ConvBackpropComputeDimensionsV2()
127 dims->in_depth = input_shape.dim_size(feature_dim); in ConvBackpropComputeDimensionsV2()
130 if (dims->in_depth != filter_shape.dim_size(num_dims - 2)) { in ConvBackpropComputeDimensionsV2()
134 dims->out_depth = filter_shape.dim_size(num_dims - 1); in ConvBackpropComputeDimensionsV2()
135 if (dims->out_depth != out_backprop_shape.dim_size(feature_dim)) { in ConvBackpropComputeDimensionsV2()
/external/tensorflow/tensorflow/contrib/coder/kernels/
Drange_coder_ops_util.cc48 (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()
/external/tensorflow/tensorflow/compiler/tf2xla/kernels/
Dshape_util.cc30 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()
Dlrn_ops.cc92 const int64 batch = in_grads_shape.dim_size(0); in Compile()
93 const int64 rows = in_grads_shape.dim_size(1); in Compile()
94 const int64 cols = in_grads_shape.dim_size(2); in Compile()
95 const int64 depth = in_grads_shape.dim_size(3); in Compile()
97 ctx, in_image_shape.dim_size(0) == batch && in Compile()
98 in_image_shape.dim_size(1) == rows && in Compile()
99 in_image_shape.dim_size(2) == cols && in Compile()
100 in_image_shape.dim_size(3) == depth && in Compile()
101 out_image_shape.dim_size(0) == batch && in Compile()
102 out_image_shape.dim_size(1) == rows && in Compile()
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Dslice_op.cc64 size[i] = input_shape.dim_size(i) - begin[i]; in Compile()
71 if (input_shape.dim_size(i) == 0) { in Compile()
78 OP_REQUIRES(ctx, 0 <= b && b <= input_shape.dim_size(i), in Compile()
80 input_shape.dim_size(i), in Compile()
82 OP_REQUIRES(ctx, 0 <= s && b + s <= input_shape.dim_size(i), in Compile()
84 input_shape.dim_size(i) - b, in Compile()
104 OP_REQUIRES(ctx, size[i] <= input_shape.dim_size(i), in Compile()
106 input_shape.dim_size(i), "], but ", in Compile()
/external/tensorflow/tensorflow/contrib/tensorboard/db/
Dsummary_converter.cc184 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/
Dscatter_add_ndim_op.cc38 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()
/external/tensorflow/tensorflow/contrib/factorization/kernels/
Dmasked_matmul_ops.cc90 const int64 a_dim_0 = a.dim_size(adj_a ? 1 : 0); in Compute()
91 const int64 a_dim_1 = a.dim_size(adj_a ? 0 : 1); in Compute()
92 const int64 b_dim_0 = b.dim_size(adj_b ? 1 : 0); in Compute()
93 const int64 b_dim_1 = b.dim_size(adj_b ? 0 : 1); in Compute()
94 const int64 num_nonzero_elements = mask_indices.dim_size(0); in Compute()
100 OP_REQUIRES(context, mask_indices.dim_size(1) == 2, in Compute()
105 ConstEigenMatFloatMap a_mat(a.matrix<float>().data(), a.dim_size(0), in Compute()
106 a.dim_size(1)); in Compute()
107 ConstEigenMatFloatMap b_mat(b.matrix<float>().data(), b.dim_size(0), in Compute()
108 b.dim_size(1)); in Compute()
/external/tensorflow/tensorflow/contrib/boosted_trees/lib/learner/common/stats/
Dnode-stats.h107 const int64 grad_dim = grad_stats.first.t.dim_size(1); in NodeStats()
112 QCHECK(grad_stats.first.t.dim_size(0) == 1) << strings::Printf( in NodeStats()
114 grad_stats.first.t.dim_size(0), grad_dim); in NodeStats()
123 grad_dim, grad_dim, grad_stats.second.t.shape().dim_size(0), in NodeStats()
124 grad_stats.second.t.shape().dim_size(1), in NodeStats()
125 grad_stats.second.t.shape().dim_size(2)); in NodeStats()
156 const int64 grad_dim = grad_stats.first.t.dim_size(1); in NodeStats()
161 QCHECK(grad_stats.first.t.dim_size(0) == 1) << strings::Printf( in NodeStats()
163 grad_stats.first.t.dim_size(0), grad_dim); in NodeStats()
170 grad_dim, grad_stats.second.t.shape().dim_size(0), in NodeStats()
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