/external/tensorflow/tensorflow/core/util/ |
D | use_cudnn.cc | 73 bool IsCudnnSupportedFilterSize(const int32 filter_rows, in IsCudnnSupportedFilterSize() argument 76 return in_depth == out_depth && filter_rows == filter_cols && in IsCudnnSupportedFilterSize() 77 (filter_rows == 1 || filter_rows == 3 || filter_rows == 5 || in IsCudnnSupportedFilterSize() 78 filter_rows == 7); in IsCudnnSupportedFilterSize()
|
D | use_cudnn.h | 35 bool IsCudnnSupportedFilterSize(const int32 filter_rows,
|
/external/tensorflow/tensorflow/core/kernels/ |
D | dilation_ops_gpu.cu.cc | 41 int depth, int filter_rows, int filter_cols, int output_rows, in DilationKernel() argument 55 for (int h = 0; h < filter_rows; ++h) { in DilationKernel() 80 int batch, int input_rows, int input_cols, int depth, int filter_rows, in DilationBackpropInputKernel() argument 100 for (int h = 0; h < filter_rows; ++h) { in DilationBackpropInputKernel() 130 int batch, int input_rows, int input_cols, int depth, int filter_rows, in DilationBackpropFilterKernel() argument 150 for (int h = 0; h < filter_rows; ++h) { in DilationBackpropFilterKernel() 190 const int filter_rows = filter.dimension(0); in operator ()() local 202 batch, input_rows, input_cols, depth, filter_rows, filter_cols, in operator ()() 221 const int filter_rows = filter.dimension(0); in operator ()() local 244 input_cols, depth, filter_rows, filter_cols, output_rows, output_cols, in operator ()() [all …]
|
D | depthwise_conv_grad_op.cc | 87 const int64 filter_rows = filter_shape.dim_size(0); \ 129 input_rows, filter_rows, stride_, padding_, \ 149 args.filter_rows = filter_rows; \ 160 << "]; Filter: [" << filter_rows << ", " << filter_cols << ", " \ 200 const int64 filter_rows = args.filter_rows; in CopyOutputBackpropRegion() local 209 static_cast<int64>(0), (in_r - filter_rows + pad_rows + stride) / stride); in CopyOutputBackpropRegion() 216 const int64 filter_spatial_size = args.filter_rows * args.filter_cols; in CopyOutputBackpropRegion() 217 if ((out_r_end - out_r_start + 1) < args.filter_rows || in CopyOutputBackpropRegion() 299 const int64 filter_spatial_size = args.filter_rows * args.filter_cols; in ComputeBackpropInput() 401 const int64 filter_spatial_size = args.filter_rows * args.filter_cols; in operator ()() [all …]
|
D | deep_conv2d.h | 73 int filter_rows; member 88 filter_rows(0), in Conv2DArgs() 100 bool CanUseDeepConv2D(int stride_rows, int stride_cols, int filter_rows,
|
D | depthwise_conv_op.cc | 95 const int64 filter_spatial_size = args.filter_rows * args.filter_cols; in Run() 176 const int64 filter_spatial_size = args.filter_rows * args.filter_cols; in operator ()() 199 const int64 filter_spatial_size = args.filter_rows * args.filter_cols; in operator ()() 361 const int32 filter_rows = filter.dim_size(0); in Compute() local 383 input_rows, filter_rows, stride_, padding_, in Compute() 411 IsCudnnSupportedFilterSize(/*filter_rows=*/filter_rows, in Compute() 418 << ", " << in_depth << "]; Filter: [" << filter_rows << ", " in Compute() 436 TensorShape{filter_rows, filter_cols, filter_in_depth, out_depth}; in Compute() 456 args.filter_rows = filter_rows; in Compute()
|
D | dilation_ops.cc | 92 const int filter_rows = filter.dim_size(0); in ParseSizes() local 102 filter_rows + (filter_rows - 1) * (*rate_rows - 1); in ParseSizes() 173 const int filter_rows = filter.dimension(0); in operator ()() local 188 for (int h = 0; h < filter_rows; ++h) { in operator ()() 282 const int filter_rows = filter.dimension(0); in operator ()() local 305 for (int h = 0; h < filter_rows; ++h) { in operator ()() 402 const int filter_rows = filter.dimension(0); in operator ()() local 425 for (int h = 0; h < filter_rows; ++h) { in operator ()()
|
D | depthwise_conv_op.h | 31 int filter_rows; member 48 filter_rows(0), in DepthwiseArgs() 145 const int64 filter_spatial_size = args.filter_rows * args.filter_cols; 218 for (int64 f_r = 0; f_r < args.filter_rows; ++f_r) { 272 for (int64 f_r = 0; f_r < args.filter_rows; ++f_r) { 305 for (int64 f_r = 0; f_r < args.filter_rows; ++f_r) {
|
D | depthwise_conv_op_gpu.h | 59 args.pad_rows < args.filter_rows && args.pad_cols >= 0 && 61 args.filter_rows * args.filter_cols <= 72 args.pad_rows < args.filter_rows && args.pad_cols >= 0 && 74 args.filter_rows * args.filter_cols <= args.in_cols * block_height; 95 kKnownFilterHeight < 0 ? args.filter_rows : kKnownFilterHeight; 206 kKnownFilterHeight < 0 ? args.filter_rows : kKnownFilterHeight; 342 kKnownFilterHeight < 0 ? args.filter_rows : kKnownFilterHeight; 497 kKnownFilterHeight < 0 ? args.filter_rows : kKnownFilterHeight; 657 const int tile_height = block_height * 2 + args.filter_rows - 1; 659 const int filter_pixels = args.filter_rows * args.filter_cols; [all …]
|
D | conv_ops.cc | 223 int input_cols, int in_depth, int filter_rows, in Run() argument 239 int input_cols, int in_depth, int filter_rows, in Run() argument 246 !CanUseDeepConv2D(stride_rows, stride_cols, filter_rows, filter_cols, in Run() 256 args.filter_rows = filter_rows; in Run() 280 int input_cols, int in_depth, int filter_rows, in Run() argument 294 int input_cols, int in_depth, int filter_rows, in Run() argument 308 desc.R = filter_rows; in Run() 442 const int filter_rows = static_cast<int>(filter.dim_size(0)); in ComputeConv2DDimension() local 478 input_rows, filter_rows, dilation_rows, stride_rows, params.padding, in ComputeConv2DDimension() 488 dimensions->filter_rows = filter_rows; in ComputeConv2DDimension() [all …]
|
D | eigen_benchmark.h | 126 Eigen::Index filter_rows = filter_dims[0]; in SpatialConvolutionBackwardKernel() local 145 input, output_backward, filter_rows, filter_cols); in SpatialConvolutionBackwardKernel() 261 Eigen::Index filter_rows = filter_dims[0]; in CuboidConvolutionBackwardKernel() local 281 input, output_backward, filter_planes, filter_rows, filter_cols); in CuboidConvolutionBackwardKernel()
|
D | conv_ops_using_gemm.cc | 498 const int filter_rows = static_cast<int>(filter.dim_size(0)); in Compute() local 524 GetWindowedOutputSize(input_rows, filter_rows, stride_rows, in Compute() 541 << ", filter_rows = " << filter_rows in Compute() 552 in_depth, filter.flat<T>().data(), filter_rows, filter_cols, in Compute()
|
D | deep_conv2d.cc | 74 static int64 GetDirectConvCost(int filter_rows, int filter_cols, int in_depth, in GetDirectConvCost() argument 76 return filter_rows * filter_cols * in_depth * out_depth * out_rows * out_cols; in GetDirectConvCost() 97 bool CanUseDeepConv2D(int stride_rows, int stride_cols, int filter_rows, in CanUseDeepConv2D() argument 102 if (stride_rows > 1 || stride_cols > 1 || filter_rows != 3 || in CanUseDeepConv2D() 119 filter_rows, filter_cols, in_depth, out_depth, out_rows, out_cols); in CanUseDeepConv2D() 297 std::max(int64{0}, args.filter_rows - base_filter_rows); in operator ()() 334 if (f_r >= args.filter_rows) continue; in operator ()() 483 const int64 shard_cost = args.filter_rows * args.filter_cols * in_depth * in operator ()() 957 std::max(int64{0}, args.filter_rows - base_filter_rows); in operator ()()
|
D | conv_ops_3d.cc | 229 const int64 filter_rows = filter.dim_size(1); in launch() local 243 0, (out_rows - 1) * strides[1] + filter_rows - in_rows); in launch() 251 if (!is_grouped_convolution && filter_planes == 1 && filter_rows == 1 && in launch() 279 filter_rows == in_rows && filter_cols == in_cols && in launch() 413 .set_spatial_dim(DimIndex::Y, filter_rows) in launch() 487 {{filter_planes, filter_rows, filter_cols}}, in launch()
|
D | nn_ops_test.cc | 108 int filter_rows, int filter_cols, CONV_OP op, in BM_ConvFloat() argument 129 TF_CHECK_OK(GetWindowedOutputSize(rows, filter_rows, stride, padding, in BM_ConvFloat() 140 static_cast<int64>(filter_rows * filter_cols) * in BM_ConvFloat() 147 static_cast<int64>(filter_rows * filter_cols) * in BM_ConvFloat() 153 SetConstOp("filter", {filter_rows, filter_cols, in_depth, out_depth}, in BM_ConvFloat() 162 std::vector<int32>({filter_rows, filter_cols, in_depth, out_depth}), in BM_ConvFloat() 517 int filter_rows, int filter_cols, in BM_ConvFloatDepthwise() argument 538 TF_CHECK_OK(GetWindowedOutputSize(rows, filter_rows, stride, padding, in BM_ConvFloatDepthwise() 550 static_cast<int64>(filter_rows * filter_cols) * in BM_ConvFloatDepthwise() 561 static_cast<int64>(filter_rows * filter_cols) * in BM_ConvFloatDepthwise() [all …]
|
D | conv_ops.h | 87 int filter_rows;
|
D | quantized_conv_ops.cc | 525 const int64 filter_rows = filter.dim_size(0); in Compute() local 542 GetWindowedOutputSize(input_rows, filter_rows, stride, in Compute() 563 filter_rows, filter_cols, out_depth, offset_filter, stride, in Compute()
|
D | conv_ops_fused_image_transform.cc | 796 const int filter_rows = static_cast<int>(filter.dim_size(0)); in Compute() local 824 GetWindowedOutputSize(padded_rows, filter_rows, stride_rows, in Compute() 845 << ", filter_rows = " << filter_rows in Compute() 856 padded_cols, in_depth, filter.flat<T>().data(), filter_rows, in Compute()
|
D | eigen_spatial_convolutions_test.cc | 1384 int filter_count, int filter_cols, int filter_rows, in PackRhsHelper() argument 1486 filter_rows, filter_cols, // in PackRhsHelper() 1504 numext::ceil((input_rows_eff - filter_rows + 1.f) / row_strides); in PackRhsHelper() 1512 reshape_dims[0] = input_depth * filter_rows * filter_cols; // patch size in PackRhsHelper() 1582 int filter_count, int filter_cols, int filter_rows, in PackLhsHelper() argument 1589 eigen_assert(block_cols <= input_depth * filter_rows * filter_cols); in PackLhsHelper() 1595 Dimensions filter_dims(filter_count, filter_rows, filter_cols, input_depth); in PackLhsHelper() 1651 reshape_dims[1] = input_depth * filter_rows * filter_cols; in PackLhsHelper() 1710 const Index max_col = filter_rows * filter_cols * input_depth; in PackLhsHelper() 1739 "filter: count=", filter_count, " dims=", filter_rows, "x", filter_cols, in PackLhsHelper()
|
/external/tensorflow/tensorflow/lite/kernels/internal/optimized/integer_ops/ |
D | fully_connected.h | 55 const int filter_rows = filter_shape.Dims(filter_dim_count - 2); in FullyConnected() local 57 TFLITE_DCHECK_EQ(filter_shape.FlatSize(), filter_rows * filter_cols); in FullyConnected() 59 TFLITE_DCHECK_EQ(output_rows, filter_rows); in FullyConnected() 65 lhs_params.rows = filter_rows; in FullyConnected() 75 dst_params.rows = filter_rows; in FullyConnected()
|
D | conv.h | 85 const int filter_rows = filter_shape.Dims(0); in ConvPerChannel() local 92 TFLITE_DCHECK_EQ(output_rows, filter_rows); in ConvPerChannel() 98 lhs_params.rows = filter_rows; in ConvPerChannel()
|
/external/tensorflow/tensorflow/core/kernels/mkl/ |
D | mkl_conv_ops.h | 220 int filter_rows = in GetFilterSizeInMklOrder() local 238 mkldnn_sizes[MKL_GROUP_FILTER_DIM_H] = filter_rows; in GetFilterSizeInMklOrder() 246 mkldnn_sizes[MklDnnDims::Dim_H] = filter_rows; in GetFilterSizeInMklOrder() 260 int filter_rows = in GetFilterSizeInMklOrder() local 275 mkldnn_sizes[MklDnnDims3D::Dim3d_H] = filter_rows; in GetFilterSizeInMklOrder() 349 int filter_planes, filter_rows, filter_cols; variable 351 filter_rows = filter_shape.dim_size(TF_2DFILTER_DIM_H); 355 filter_rows = filter_shape.dim_size(TF_3DFILTER_DIM_H); 413 input_rows, filter_rows, dilation_rows, stride_rows, 426 input_rows, filter_rows, dilation_rows, stride_rows,
|
/external/tensorflow/tensorflow/python/kernel_tests/ |
D | conv_ops_test.py | 858 filter_rows=3, 880 filter_rows=3, 1854 filter_rows, argument 1868 filter_shape = [filter_rows, filter_cols, in_depth // num_groups, out_depth] 1871 output_rows = (input_rows - filter_rows + stride_rows) // stride_rows 1878 output_rows = (input_rows + padding[1][0] + padding[1][1] - filter_rows + 1948 filter_rows=3, 1966 filter_rows=2, 1984 filter_rows=3, 2002 filter_rows=2, [all …]
|
/external/tensorflow/tensorflow/core/kernels/neon/ |
D | neon_depthwise_conv_op.cc | 82 const int32 filter_rows = filter.dim_size(0); in Compute() local 92 GetWindowedOutputSize(input_rows, filter_rows, stride, in Compute() 111 << ", " << in_depth << "]; Filter: [" << filter_rows << ", " in Compute()
|
/external/tensorflow/tensorflow/core/grappler/costs/ |
D | utils_test.cc | 68 int filter_rows = 3; in TEST() local 83 CreateConstOp("filter", {filter_rows, filter_cols, in_depth, out_depth}, in TEST() 98 std::vector<int32>({filter_rows, filter_cols, in_depth, out_depth}), in TEST()
|