/external/tensorflow/tensorflow/core/kernels/ |
D | dilation_ops.cc | 68 int64* out_rows, int64* out_cols) { in ParseSizes() argument 108 padding, out_rows, pad_top)); in ParseSizes() 129 int64 out_rows = 0, out_cols = 0; in Compute() local 131 &rate_rows, &rate_cols, &pad_top, &pad_left, &out_rows, in Compute() 138 const std::vector<int64> out_sizes = {batch, out_rows, out_cols, depth}; in Compute() 228 int64 out_rows = 0, out_cols = 0; in Compute() local 230 &rate_rows, &rate_cols, &pad_top, &pad_left, &out_rows, in Compute() 239 out_rows == out_backprop.dim_size(1) && in Compute() 348 int64 out_rows = 0, out_cols = 0; in Compute() local 350 &rate_rows, &rate_cols, &pad_top, &pad_left, &out_rows, in Compute() [all …]
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D | deep_conv2d.h | 79 int out_rows; member 92 out_rows(0), in Conv2DArgs() 102 int out_rows, int out_cols);
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D | depthwise_conv_op.cc | 198 args.out_rows * args.out_cols * args.out_depth; in operator ()() 213 const int64 b = i / args.out_rows; in operator ()() 217 const int64 out_r = i % args.out_rows; in operator ()() 233 const int64 total_shards = args.batch * args.out_rows; in operator ()() 374 int64 out_rows = 0, out_cols = 0, pad_top = 0, pad_bottom = 0, pad_left = 0, in Compute() local 384 &out_rows, &pad_top, &pad_bottom)); in Compute() 389 ShapeFromFormat(data_format_, batch, out_rows, out_cols, out_depth); in Compute() 420 << "]; Output: [" << batch << ", " << out_rows << ", " << out_cols in Compute() 462 args.out_rows = out_rows; in Compute()
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D | depthwise_conv_grad_op.cc | 120 int64 out_rows = 0, out_cols = 0, pad_top = 0, pad_bottom = 0, pad_left = 0, \ 130 &out_rows, &pad_top, &pad_bottom)); \ 135 context, output_rows == out_rows, \ 138 "actual = ", output_rows, ", computed = ", out_rows)); \ 155 args.out_rows = out_rows; \ 163 << ", output: [" << batch << ", " << out_rows << ", " << out_cols \ 204 const int64 out_rows = args.out_rows; in CopyOutputBackpropRegion() local 210 const int64 out_r_end = std::min(out_rows - 1, (in_r + pad_rows) / stride); in CopyOutputBackpropRegion() 424 args.out_rows * args.out_cols * args.out_depth; in operator ()() 490 std::min(args.out_rows - 1, (in_r + args.pad_rows) / stride); in DepthwiseConvBackpropInputReference() [all …]
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D | conv_ops.cc | 224 int filter_cols, int pad_rows, int pad_cols, int out_rows, in Run() argument 240 int filter_cols, int pad_rows, int pad_cols, int out_rows, in Run() argument 247 in_depth, out_depth, out_rows, out_cols)) { in Run() 260 args.out_rows = out_rows; in Run() 281 int filter_cols, int pad_rows, int pad_cols, int out_rows, in Run() argument 295 int filter_cols, int pad_rows, int pad_cols, int out_rows, in Run() argument 476 int64 out_rows = 0, out_cols = 0; in ComputeConv2DDimension() local 479 &out_rows, &pad_rows_before, &pad_rows_after)); in ComputeConv2DDimension() 496 dimensions->out_rows = out_rows; in ComputeConv2DDimension() 532 params_.data_format, dimensions.batch, dimensions.out_rows, in Compute() [all …]
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D | deep_conv2d.cc | 50 int out_depth, int out_rows, int out_cols) { in GetDeepConvCost() argument 65 const int64 row_tiles = (out_rows + out_tile_rows - 1) / out_tile_rows; in GetDeepConvCost() 75 int out_depth, int out_rows, int out_cols) { in GetDirectConvCost() argument 76 return filter_rows * filter_cols * in_depth * out_depth * out_rows * out_cols; in GetDirectConvCost() 99 int out_rows, int out_cols) { in CanUseDeepConv2D() argument 117 t.output_shape().cols, in_depth, out_depth, out_rows, out_cols); in CanUseDeepConv2D() 119 filter_rows, filter_cols, in_depth, out_depth, out_rows, out_cols); in CanUseDeepConv2D() 799 if (out_r_start < 0 || out_r_start >= args.out_rows || in operator ()() 809 if (out_r >= args.out_rows) continue; in operator ()() 1011 (args.out_rows + out_tile_rows - 1) / out_tile_rows + in operator ()() [all …]
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D | fractional_avg_pool_op.cc | 245 const int64 out_rows = out_backprop.dim_size(1); in Compute() local 277 out_cols * out_rows * out_batch); in Compute() 283 for (int64 r = 0; r < out_rows; ++r) { in Compute() 296 const int64 out_index = (b * out_rows + r) * out_cols + c; in Compute()
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D | quantized_conv_ops.cc | 540 int64 out_rows = 0, out_cols = 0, pad_rows = 0, pad_cols = 0; in Compute() local 543 padding_, &out_rows, &pad_rows)); in Compute() 548 CHECK_GT(out_rows, 0); in Compute() 551 TensorShape out_shape({batch, out_rows, out_cols, out_depth}); in Compute() 564 padding_, output->flat<T3>().data(), out_rows, out_cols, in Compute()
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D | conv_ops_using_gemm.cc | 522 int64 out_rows = 0, out_cols = 0, pad_rows = 0, pad_cols = 0; in Compute() local 525 padding_, &out_rows, &pad_rows)); in Compute() 530 ShapeFromFormat(data_format_, batch, out_rows, out_cols, out_depth); in Compute() 554 output->flat<T>().data(), out_rows, out_cols); in Compute()
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D | conv_grad_filter_ops_benchmark_test.cc | 65 {batch, conv2d_dims.out_rows, conv2d_dims.out_cols, out_depth}) in Conv2DBackpropFilter() 67 {batch, out_depth, conv2d_dims.out_rows, conv2d_dims.out_cols}); in Conv2DBackpropFilter()
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D | depthwise_conv_op_gpu.h | 57 args.in_cols <= 32 && args.in_rows == args.out_rows && 70 args.in_cols <= 32 && args.in_rows == args.out_rows && 103 const int out_height = args.out_rows; 350 const int out_height = args.out_rows; 662 const int num_outputs = args.out_rows * args.out_cols * block_count; 750 args.batch * args.out_rows * args.out_cols * args.out_depth; 823 const int out_height = args.out_rows; 893 const int out_height = args.out_rows; 1046 const int out_height = args.out_rows; 1331 const int out_height = args.out_rows; [all …]
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D | conv_grad_input_ops_benchmark_test.cc | 64 {batch, conv2d_dims.out_rows, conv2d_dims.out_cols, out_depth}) in Conv2DBackpropInput() 66 {batch, out_depth, conv2d_dims.out_rows, conv2d_dims.out_cols}); in Conv2DBackpropInput()
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D | conv_ops.h | 98 int64 out_rows;
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D | depthwise_conv_op.h | 39 int out_rows; member 54 out_rows(0), in DepthwiseArgs()
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D | conv_ops_fused_image_transform.cc | 822 int64 out_rows = 0, out_cols = 0, pad_rows = 0, pad_cols = 0; in Compute() local 825 padding_, &out_rows, &pad_rows)); in Compute() 830 ShapeFromFormat(FORMAT_NHWC, batch, out_rows, out_cols, out_depth); in Compute() 858 output->flat<T>().data(), out_rows, out_cols, st, top_padding, in Compute()
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/external/tensorflow/tensorflow/core/kernels/neon/ |
D | neon_depthwise_conv_op.cc | 90 int64 out_rows = 0, out_cols = 0, pad_rows = 0, pad_cols = 0; in Compute() local 93 padding_, &out_rows, &pad_rows)); in Compute() 97 TensorShape out_shape({batch, out_rows, out_cols, out_depth}); in Compute() 115 << out_rows << ", " << out_cols << ", " << out_depth << "]"; in Compute()
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/external/tensorflow/tensorflow/core/kernels/image/ |
D | extract_volume_patches_op.cc | 112 int64 out_planes = 0, out_rows = 0, out_cols = 0; in Compute() local 119 padding_, &out_rows, &pad_rows)); in Compute() 125 batch, out_planes, out_rows, out_cols, in Compute()
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D | extract_image_patches_op.cc | 89 int64 out_rows = 0, out_cols = 0; in Compute() local 93 padding_, &out_rows, &pad_rows)); in Compute() 98 const std::vector<int64> out_sizes = {batch, out_rows, out_cols, in Compute()
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/external/tensorflow/tensorflow/core/kernels/mkl/ |
D | mkl_conv_ops.h | 396 int64 out_rows = 0, out_cols = 0, out_planes = 0; variable 414 padding_type, &out_rows, &pad_top, &pad_bottom)); 427 padding_, &out_rows, &pad_top, &pad_bottom)); 456 ? ShapeFromFormat(data_format_, out_batch, out_rows, out_cols, 459 {{out_planes, out_rows, out_cols}}, out_depth); 467 mkldnn_sizes[MklDnnDims::Dim_H] = static_cast<int>(out_rows); 475 mkldnn_sizes[MklDnnDims3D::Dim3d_H] = static_cast<int>(out_rows);
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/external/tensorflow/tensorflow/core/api_def/base_api/ |
D | api_def_ExtractVolumePatches.pbtxt | 12 5-D Tensor with shape `[batch, out_planes, out_rows, out_cols, 15 in the "depth" dimension. Note `out_planes`, `out_rows` and `out_cols`
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D | api_def_ExtractImagePatches.pbtxt | 12 4-D Tensor with shape `[batch, out_rows, out_cols, ksize_rows * 15 `out_rows` and `out_cols` are the dimensions of the output patches.
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D | api_def_Conv3DBackpropInput.pbtxt | 19 Backprop signal of shape `[batch, out_depth, out_rows, out_cols,
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D | api_def_Conv3DBackpropFilter.pbtxt | 19 Backprop signal of shape `[batch, out_depth, out_rows, out_cols,
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D | api_def_Conv3DBackpropFilterV2.pbtxt | 21 Backprop signal of shape `[batch, out_depth, out_rows, out_cols,
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/external/tensorflow/tensorflow/core/grappler/costs/ |
D | utils_test.cc | 70 int out_rows = 7; in TEST() local 87 CreateConstOp("output_backprop", {batch, out_rows, out_cols, out_depth}, in TEST()
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