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Searched refs:convolution (Results 1 – 25 of 97) sorted by relevance

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/external/tensorflow/tensorflow/compiler/xla/service/
Dconvolution_group_converter.cc53 Status HandleConvolution(HloInstruction* convolution) override;
55 Status HandleBatchGroupCount(HloInstruction* convolution);
200 Status ConvolutionVisitor::HandleBatchGroupCount(HloInstruction* convolution) { in HandleBatchGroupCount() argument
201 auto dim_numbers = convolution->convolution_dimension_numbers(); in HandleBatchGroupCount()
202 auto activation = convolution->mutable_operand(0); in HandleBatchGroupCount()
203 auto filter = convolution->mutable_operand(1); in HandleBatchGroupCount()
204 int64 batch_group_count = convolution->batch_group_count(); in HandleBatchGroupCount()
211 << " for convolution " << convolution->ToString() << "\n"; in HandleBatchGroupCount()
290 Window window = convolution->window(); in HandleBatchGroupCount()
300 MakeConvolveHlo(activation, filter, convolution->feature_group_count(), in HandleBatchGroupCount()
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Dtranspose_folding.cc57 const HloInstruction& convolution, in CanFoldOperandsIntoConvolution() argument
60 if (HloOpcode::kConvolution != convolution.opcode()) { in CanFoldOperandsIntoConvolution()
65 for (int64 i = 0; i < convolution.operand_count(); ++i) { in CanFoldOperandsIntoConvolution()
66 auto& operand = *convolution.operand(i); in CanFoldOperandsIntoConvolution()
72 return transposable_conv_operands(convolution, operand_set); in CanFoldOperandsIntoConvolution()
120 auto& convolution = *pair.first; in FoldTransposeIntoConvolution() local
128 convolution.convolution_dimension_numbers(); in FoldTransposeIntoConvolution()
134 HloInstruction& transpose = *convolution.mutable_operand(kLhsIdx); in FoldTransposeIntoConvolution()
151 new_lhs = convolution.mutable_operand(kLhsIdx); in FoldTransposeIntoConvolution()
157 HloInstruction& transpose = *convolution.mutable_operand(kRhsIdx); in FoldTransposeIntoConvolution()
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Ddepthwise_convolution_converter.cc48 Status HandleConvolution(HloInstruction* convolution) override;
50 Status HandleBackwardFilterBatchGroupConvolution(HloInstruction* convolution);
101 HloInstruction* convolution) { in HandleBackwardFilterBatchGroupConvolution() argument
102 auto dim_numbers = convolution->convolution_dimension_numbers(); in HandleBackwardFilterBatchGroupConvolution()
103 auto lhs = convolution->mutable_operand(0); in HandleBackwardFilterBatchGroupConvolution()
104 auto rhs = convolution->mutable_operand(1); in HandleBackwardFilterBatchGroupConvolution()
105 int64 num_groups = convolution->batch_group_count(); in HandleBackwardFilterBatchGroupConvolution()
116 << " for convolution " << convolution->ToString() << "\n"; in HandleBackwardFilterBatchGroupConvolution()
127 if (!is_cost_viable_(convolution)) { in HandleBackwardFilterBatchGroupConvolution()
129 convolution->shape(), dim_numbers.output_batch_dimension(), in HandleBackwardFilterBatchGroupConvolution()
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Dalgebraic_simplifier.cc307 Status HandleConvolution(HloInstruction* convolution) override;
494 StatusOr<bool> FoldConvInputPad(HloInstruction* convolution);
495 StatusOr<bool> FoldConvFilterPad(HloInstruction* convolution);
498 StatusOr<bool> SimplifyConvToDot(HloInstruction* convolution);
4099 HloInstruction* convolution) { in FoldConvInputPad() argument
4100 auto* lhs = convolution->mutable_operand(0); in FoldConvInputPad()
4101 auto* rhs = convolution->mutable_operand(1); in FoldConvInputPad()
4102 const auto& window = convolution->window(); in FoldConvInputPad()
4104 convolution->convolution_dimension_numbers(); in FoldConvInputPad()
4157 auto new_conv = convolution->CloneWithNewOperands( in FoldConvInputPad()
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Dhlo_cost_analysis.cc532 Status HloCostAnalysis::HandleConvolution(const HloInstruction* convolution) { in HandleConvolution() argument
533 auto lhs = convolution->operand(0); in HandleConvolution()
534 auto rhs = convolution->operand(1); in HandleConvolution()
535 Window window = convolution->window(); in HandleConvolution()
536 const auto& result_shape = convolution->shape(); in HandleConvolution()
540 const auto& dnums = convolution->convolution_dimension_numbers(); in HandleConvolution()
649 const int64 fma_count = (input_feature / convolution->feature_group_count()) * in HandleConvolution()
651 (batch / convolution->batch_group_count()) * in HandleConvolution()
/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/cpu/
Dir_emission_utils.cc44 const HloInstruction& convolution, in PotentiallyImplementedAsEigenConvolution() argument
53 const Shape& input_shape = convolution.operand(0)->shape(); in PotentiallyImplementedAsEigenConvolution()
54 const Shape& kernel_shape = convolution.operand(1)->shape(); in PotentiallyImplementedAsEigenConvolution()
55 const Shape& output_shape = convolution.shape(); in PotentiallyImplementedAsEigenConvolution()
79 if (window_util::HasWindowReversal(convolution.window())) { in PotentiallyImplementedAsEigenConvolution()
84 convolution.convolution_dimension_numbers(); in PotentiallyImplementedAsEigenConvolution()
Dir_emission_utils.h27 const HloInstruction& convolution,
/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)
117 y = nn_ops.convolution(
125 y = nn_ops.convolution(
223 result = nn_ops.convolution(
225 result = nn_ops.convolution(
233 y1 = nn_ops.convolution(
238 y1 = nn_ops.convolution(
259 output = nn_ops.convolution(
/external/tensorflow/tensorflow/compiler/xla/service/gpu/
Dbackend_configs.proto20 // Backend config for a convolution that runs through cudnn.
30 // The scaling factor multiplied with the convolution result.
33 // Below are the fields related to cuDNN's fused convolution. Refer to
36 // The requested activation (e.g. relu) after the convolution. It is with type
/external/tensorflow/tensorflow/core/api_def/base_api/
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_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
74 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
75 summary: "Computes the gradients of convolution with respect to the filter."
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
/external/tensorflow/tensorflow/core/protobuf/
Dconv_autotuning.proto1 // This is used for convolution logging. Also see
11 // A convolution. Currently it's only used for logging. In the future, we may
/external/tensorflow/tensorflow/stream_executor/
Ddnn.proto17 // Describes how a convolution input or output layer's data is formatted.
32 // Describes how a convolution filter is laid out in the memory.
65 // referred as convolution. See cuDNN cudnnConvolutionMode_t.
/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/lite/tools/optimize/testdata/
DREADME.md8 A floating point model with single convolution where all weights are
13 A floating point model with a single convolution where weights of the model
/external/tensorflow/tensorflow/lite/g3doc/models/segmentation/
Doverview.md37 …<li>DeepLabv1: We use atrous convolution to explicitly control the resolution at which feature res…
39 …rameters to facilitate the training. In particular, we applying atrous convolution to extract outp…
40 …trarily control the resolution of extracted encoder features by atrous convolution to trade-off pr…
/external/tensorflow/tensorflow/lite/g3doc/guide/
Dops_version.md19 to add dilation parameters to the convolution operation.
25 * Old convolution kernels that don't support dilation are equivalent to
33 For example, the options table of convolution looks like this:
72 The original convolution parameter is as follows:
/external/tensorflow/tensorflow/compiler/mlir/lite/tests/end2end/
Dconv_2d.pbtxt85 name: "conv_net_2d_1/conv_2d_0/convolution"
189 input: "conv_net_2d_1/conv_2d_0/convolution"

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