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Searched refs:convolution (Results 1 – 25 of 74) sorted by relevance

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/external/eigen/bench/tensors/
Dtensor_benchmarks_cpu.cc146 BM_FuncWithKernelDimsCPU(convolution, 7, 1, 4);
147 BM_FuncWithKernelDimsCPU(convolution, 7, 1, 8);
148 BM_FuncWithKernelDimsCPU(convolution, 7, 1, 12);
150 BM_FuncWithKernelDimsCPU(convolution, 1, 7, 4);
151 BM_FuncWithKernelDimsCPU(convolution, 1, 7, 8);
152 BM_FuncWithKernelDimsCPU(convolution, 1, 7, 12);
154 BM_FuncWithKernelDimsCPU(convolution, 7, 4, 4);
155 BM_FuncWithKernelDimsCPU(convolution, 7, 4, 8);
156 BM_FuncWithKernelDimsCPU(convolution, 7, 4, 12);
158 BM_FuncWithKernelDimsCPU(convolution, 4, 7, 4);
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Dtensor_benchmarks_gpu.cu70 BM_FuncWithKernelDimsGPU(convolution, 7, 1);
71 BM_FuncWithKernelDimsGPU(convolution, 1, 7);
72 BM_FuncWithKernelDimsGPU(convolution, 7, 4);
73 BM_FuncWithKernelDimsGPU(convolution, 4, 7);
74 BM_FuncWithKernelDimsGPU(convolution, 7, 64);
75 BM_FuncWithKernelDimsGPU(convolution, 64, 7);
/external/tensorflow/tensorflow/compiler/xla/service/
Dtranspose_folding.cc54 const HloInstruction& convolution, in CanFoldOperandsIntoConvolution() argument
57 if (HloOpcode::kConvolution != convolution.opcode()) { in CanFoldOperandsIntoConvolution()
62 for (int64 i = 0; i < convolution.operand_count(); ++i) { in CanFoldOperandsIntoConvolution()
63 auto& operand = *convolution.operand(i); in CanFoldOperandsIntoConvolution()
69 return transposable_conv_operands(convolution, operand_set); in CanFoldOperandsIntoConvolution()
101 auto& convolution = *pair.first; in FoldTransposeIntoConvolution() local
109 convolution.convolution_dimension_numbers(); in FoldTransposeIntoConvolution()
116 HloInstruction& transpose = *convolution.mutable_operand(kLhsIdx); in FoldTransposeIntoConvolution()
133 new_lhs = convolution.mutable_operand(kLhsIdx); in FoldTransposeIntoConvolution()
140 HloInstruction& transpose = *convolution.mutable_operand(kRhsIdx); in FoldTransposeIntoConvolution()
[all …]
Dhlo_cost_analysis.cc381 Status HloCostAnalysis::HandleConvolution(const HloInstruction* convolution) { in HandleConvolution() argument
382 auto rhs_instruction = convolution->operand(1); in HandleConvolution()
383 const auto& dnums = convolution->convolution_dimension_numbers(); in HandleConvolution()
385 convolution->shape().dimensions(dnums.output_feature_dimension()); in HandleConvolution()
393 const int64 output_elements = ShapeUtil::ElementsIn(convolution->shape()); in HandleConvolution()
Dhlo_verifier.cc75 Status ShapeVerifier::HandleConvolution(HloInstruction* convolution) { in HandleConvolution() argument
79 convolution->operand(0)->shape(), convolution->operand(1)->shape(), in HandleConvolution()
80 convolution->window(), convolution->convolution_dimension_numbers())); in HandleConvolution()
81 return CheckShape(convolution, expected); in HandleConvolution()
Ddfs_hlo_visitor_with_default.h85 Status HandleConvolution(HloInstructionPtr convolution) override { in HandleConvolution() argument
86 return DefaultAction(convolution); in HandleConvolution()
Dalgebraic_simplifier.cc141 Status HandleConvolution(HloInstruction* convolution) override;
1852 HloInstruction* convolution) { in HandleConvolution() argument
1853 auto lhs = convolution->mutable_operand(0); in HandleConvolution()
1854 auto rhs = convolution->mutable_operand(1); in HandleConvolution()
1858 convolution, in HandleConvolution()
1860 convolution->shape(), in HandleConvolution()
1862 ShapeUtil::MakeShape(convolution->shape().element_type(), {}), in HandleConvolution()
1867 const auto& window = convolution->window(); in HandleConvolution()
1885 convolution->convolution_dimension_numbers(); in HandleConvolution()
1888 const Shape& convolution_shape = convolution->shape(); in HandleConvolution()
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/external/tensorflow/tensorflow/compiler/xla/service/cpu/
Dir_emission_utils.cc28 const HloInstruction& convolution) { in PotentiallyImplementedAsEigenConvolution() argument
36 const Shape& input_shape = convolution.operand(0)->shape(); in PotentiallyImplementedAsEigenConvolution()
37 const Shape& kernel_shape = convolution.operand(0)->shape(); in PotentiallyImplementedAsEigenConvolution()
47 if (window_util::HasWindowReversal(convolution.window())) { in PotentiallyImplementedAsEigenConvolution()
52 convolution.convolution_dimension_numbers(); in PotentiallyImplementedAsEigenConvolution()
72 const Shape& output_shape = convolution.shape(); in PotentiallyImplementedAsEigenConvolution()
Dcpu_layout_assignment.cc104 const HloInstruction* convolution = instruction; in AddBackendConstraints() local
105 const HloInstruction* lhs_instruction = convolution->operand(0); in AddBackendConstraints()
106 const HloInstruction* rhs_instruction = convolution->operand(1); in AddBackendConstraints()
113 Shape output_shape(RowMajorShape(convolution->shape())); in AddBackendConstraints()
119 constraints->SetOperandLayout(input_shape, convolution, 0)); in AddBackendConstraints()
121 constraints->SetOperandLayout(filter_shape, convolution, 1)); in AddBackendConstraints()
123 constraints->SetInstructionLayout(output_shape, convolution)); in AddBackendConstraints()
Dir_emission_utils.h26 const HloInstruction& convolution);
Dir_emitter.cc846 Status IrEmitter::HandleConvolution(HloInstruction* convolution) { in HandleConvolution() argument
847 auto lhs = convolution->operand(0); in HandleConvolution()
848 auto rhs = convolution->operand(1); in HandleConvolution()
849 const auto& window = convolution->window(); in HandleConvolution()
851 /*instruction=*/*convolution, /*operands=*/{lhs, rhs}, in HandleConvolution()
855 convolution->convolution_dimension_numbers(); in HandleConvolution()
857 if (PotentiallyImplementedAsEigenConvolution(*convolution)) { in HandleConvolution()
860 const Shape& convolution_shape = convolution->shape(); in HandleConvolution()
872 TF_RETURN_IF_ERROR(EmitTargetAddressForOp(convolution)); in HandleConvolution()
875 convolution->convolution_dimension_numbers(); in HandleConvolution()
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/external/tensorflow/tensorflow/compiler/xla/tests/
Disolated_convolution.hlo1 HloModule convolution.167:
3 ENTRY %convolution.167 (parameter.0: f32[16,28,28,128], parameter.1: f32[3,3,128,128]) -> f32[16,28…
6 …ROOT %convolution.167 = f32[16,28,28,128]{3,0,2,1} convolution(f32[16,28,28,128]{3,0,2,1} %paramet…
/external/tensorflow/tensorflow/python/kernel_tests/
Datrous_convolution_test.py104 y1 = nn_ops.convolution(
106 y2 = nn_ops.convolution(input=x, filter=filters_upsampled, **kwargs)
116 y = nn_ops.convolution(
123 y = nn_ops.convolution(
221 result = nn_ops.convolution(
223 result = nn_ops.convolution(
231 y1 = nn_ops.convolution(
236 y1 = nn_ops.convolution(
257 output = nn_ops.convolution(
/external/tensorflow/tensorflow/core/api_def/base_api/
Dapi_def_Conv2DBackpropInput.pbtxt21 Gradients w.r.t. the output of the convolution.
28 w.r.t. the input of the convolution.
35 of the convolution. Must be in the same order as the dimension specified with
65 summary: "Computes the gradients of convolution with respect to the input."
Dapi_def_Conv2DBackpropFilter.pbtxt21 Gradients w.r.t. the output of the convolution.
29 the `filter` input of the convolution.
36 of the convolution. Must be in the same order as the dimension specified with
66 summary: "Computes the gradients of convolution with respect to the filter."
Dapi_def_DepthwiseConv2dNativeBackpropFilter.pbtxt25 Gradients w.r.t. the output of the convolution.
33 the `filter` input of the convolution.
40 of the convolution.
69 summary: "Computes the gradients of depthwise convolution with respect to the filter."
Dapi_def_DepthwiseConv2dNativeBackpropInput.pbtxt24 Gradients w.r.t. the output of the convolution.
33 convolution.
40 of the convolution.
69 summary: "Computes the gradients of depthwise convolution with respect to the input."
Dapi_def_SpaceToBatch.pbtxt95 Among others, this operation is useful for reducing atrous convolution into
96 regular convolution.
Dapi_def_FusedResizeAndPadConv2D.pbtxt50 summary: "Performs a resize and padding as a preprocess during a convolution."
53 the packing stage of a convolution, so this op allows for an optimized
Dapi_def_SpaceToBatchND.pbtxt125 Among others, this operation is useful for reducing atrous convolution into
126 regular convolution.
Dapi_def_Conv3DBackpropInput.pbtxt36 summary: "Computes the gradients of 3-D convolution with respect to the input."
/external/python/cpython3/Modules/_decimal/libmpdec/literature/
Dbignum.txt6 Bignum arithmetic in libmpdec uses the scheme for fast convolution
13 The transform in a finite field can be used for convolution in the same
75 convolute.c -> do the actual fast convolution, using one of
/external/tensorflow/tensorflow/docs_src/api_guides/python/
Dnn.md33 The convolution ops sweep a 2-D filter over a batch of images, applying the
41 Note that although these ops are called "convolution", they are strictly
54 convolution ops depend on the padding scheme chosen: `'SAME'` or `'VALID'`.
115 * @{tf.nn.convolution}
162 is the max-sum counterpart of standard sum-product convolution:
177 is the min-sum counterpart of standard sum-product convolution:
191 convolution. Please refer to the `Convolution` section for details.
311 scheme for convolution operations.
321 output size after a `'VALID'` convolution is \\(n_o\\). In other words, we need to
405 Putting all together, the total padding used by tensorflow's convolution with
/external/tensorflow/tensorflow/compiler/xla/tools/parser/
Dhlo_parser_test.cc353 …ROOT %convolution = f32[1,2,1]{2,0,1} convolution(f32[1,2,1]{2,0,1} %copy, f32[1,1,1]{2,1,0} %filt…
366 …ROOT %convolution = f32[1,2]{0,1} convolution(f32[1,2]{1,0} %input, f32[1,1]{1,0} %filter), dim_la…
379 …ROOT %convolution-base-dilated = f32[128,14,14,512]{0,3,2,1} convolution(f32[128,7,7,512]{0,3,2,1}…
/external/tensorflow/tensorflow/docs_src/tutorials/
Dpdes.md51 """Transform a 2D array into a convolution kernel"""
57 """A simplified 2D convolution operation"""

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