Operators Catalogue
- Accumulate
- AccumulateHistogram
- Accuracy
- Add
- AddPadding
- Alias
- Allgather
- Allreduce
- And
- Append
- AtomicAppend
- AtomicFetchAdd
- AveragePool
- AveragePoolGradient
- AveragedLoss
- AveragedLossGradient
- BatchBoxCox
- BatchMatMul
- BatchOneHot
- BatchToSpace
- BooleanMask
- BooleanMaskLengths
- BooleanUnmask
- Broadcast
- Cast
- CheckAtomicBool
- CheckCounterDone
- CheckDatasetConsistency
- Checkpoint
- Clip
- ClipGradient
- Col2Im
- CollectTensor
- ComputeOffset
- Concat
- ConcatTensorVector
- ConditionalSetAtomicBool
- ConstantFill
- Conv
- ConvGradient
- ConvTranspose
- ConvTransposeGradient
- Copy
- CopyCPUToGPU
- CopyFromCPUInput
- CopyGPUToCPU
- CopyOnDeviceLike
- CosineEmbeddingCriterion
- CosineEmbeddingCriterionGradient
- CosineSimilarity
- CosineSimilarityGradient
- CountDown
- CountUp
- CreateAtomicBool
- CreateCommonWorld
- CreateCounter
- CreateMutex
- CreateQPSMetric
- CreateTensorVector
- CreateTextFileReader
- CreateTreeCursor
- CrossEntropy
- CrossEntropyGradient
- DBExists
- DepthConcat
- DepthSplit
- Div
- DivGradient
- DotProduct
- DotProductGradient
- DotProductWithPadding
- DotProductWithPaddingGradient
- Dropout
- DropoutGrad
- EQ
- ElementwiseLinear
- ElementwiseLinearGradient
- Elu
- EluGradient
- EnsureCPUOutput
- EnsureDense
- Exp
- ExpandDims
- ExtendTensor
- FC
- FCGradient
- FeedBlob
- Find
- FindDuplicateElements
- Flatten
- FlattenToVec
- FloatToHalf
- Free
- GE
- GT
- Gather
- GatherPadding
- GatherRanges
- GaussianFill
- GetAllBlobNames
- GetGPUMemoryUsage
- GivenTensorBoolFill
- GivenTensorFill
- GivenTensorInt64Fill
- GivenTensorIntFill
- GivenTensorStringFill
- HSoftmax
- HSoftmaxGradient
- HSoftmaxSearch
- HalfToFloat
- HasElements
- HuffmanTreeHierarchy
- Im2Col
- IndexFreeze
- IndexGet
- IndexLoad
- IndexSize
- IndexStore
- InstanceNorm
- InstanceNormGradient
- IntIndexCreate
- IsEmpty
- IsMemberOf
- L1Distance
- L1DistanceGradient
- LE
- LRN
- LRNGradient
- LSTMUnit
- LSTMUnitGradient
- LT
- LabelCrossEntropy
- LabelCrossEntropyGradient
- LastNWindowCollector
- LeakyRelu
- LeakyReluGradient
- LengthsGather
- LengthsMean
- LengthsMeanGradient
- LengthsPartition
- LengthsRangeFill
- LengthsSum
- LengthsSumGradient
- LengthsTile
- LengthsToRanges
- LengthsToSegmentIds
- LengthsToShape
- LengthsToWeights
- LengthsWeightedSum
- LengthsWeightedSumGradient
- LengthsWeightedSumWithMainInputGradient
- Load
- Log
- Logit
- LongIndexCreate
- LpPool
- LpPoolGradient
- MSRAFill
- MakeTwoClass
- MakeTwoClassGradient
- MarginRankingCriterion
- MarginRankingCriterionGradient
- MatMul
- Max
- MaxGradient
- MaxPool
- MaxPoolGradient
- MaxPoolWithIndex
- Mul
- MultiClassAccuracy
- NCHW2NHWC
- NHWC2NCHW
- NanCheck
- Negative
- Normalize
- NormalizeGradient
- Not
- OneHot
- Or
- PRelu
- PReluGradient
- PackRecords
- PackSegments
- PadEmptySamples
- PadImage
- PadImageGradient
- PairWiseLoss
- PairWiseLossGradient
- Partition
- Perplexity
- PiecewiseLinearTransform
- Pow
- QPSMetric
- QPSMetricReport
- RangeFill
- ReadNextBatch
- ReadRandomBatch
- ReceiveTensor
- RecurrentNetwork
- RecurrentNetworkGradient
- Reduce
- ReduceBackMean
- ReduceBackMeanGradient
- ReduceBackSum
- ReduceBackSumGradient
- ReduceFrontMean
- ReduceFrontMeanGradient
- ReduceFrontSum
- ReduceFrontSumGradient
- ReduceFrontWeightedSum
- ReduceFrontWeightedSumGradient
- ReduceTailSum
- Relu
- ReluGradient
- RemoveDataBlocks
- RemovePadding
- ReplaceNaN
- ResetCounter
- ResetCursor
- Reshape
- ResizeLike
- ResizeNearest
- RetrieveCount
- ReversePackedSegs
- RoIPool
- RoIPoolGradient
- RowMul
- Save
- Scale
- ScatterAssign
- ScatterWeightedSum
- SegmentIdsToLengths
- SegmentIdsToRanges
- SegmentOneHot
- SendTensor
- Shape
- Sigmoid
- SigmoidCrossEntropyWithLogits
- SigmoidCrossEntropyWithLogitsGradient
- SigmoidGradient
- Slice
- Softmax
- SoftmaxGradient
- SoftmaxWithLoss
- SoftmaxWithLossGradient
- Softplus
- SoftplusGradient
- Softsign
- SoftsignGradient
- SortAndShuffle
- SortedSegmentMean
- SortedSegmentMeanGradient
- SortedSegmentRangeLogMeanExp
- SortedSegmentRangeLogMeanExpGradient
- SortedSegmentRangeLogSumExp
- SortedSegmentRangeLogSumExpGradient
- SortedSegmentRangeMax
- SortedSegmentRangeMaxGradient
- SortedSegmentRangeMean
- SortedSegmentRangeMeanGradient
- SortedSegmentRangeSum
- SortedSegmentRangeSumGradient
- SortedSegmentSum
- SortedSegmentSumGradient
- SortedSegmentWeightedSum
- SortedSegmentWeightedSumGradient
- SpaceToBatch
- SparseLengthsMean
- SparseLengthsMeanGradient
- SparseLengthsSum
- SparseLengthsSumGradient
- SparseLengthsWeightedSum
- SparseLengthsWeightedSumGradient
- SparseLengthsWeightedSumWithMainInputGradient
- SparseSortedSegmentMean
- SparseSortedSegmentMeanGradient
- SparseSortedSegmentSum
- SparseSortedSegmentSumGradient
- SparseSortedSegmentWeightedSum
- SparseSortedSegmentWeightedSumGradient
- SparseToDense
- SparseToDenseMask
- SparseUnsortedSegmentMean
- SparseUnsortedSegmentMeanGradient
- SparseUnsortedSegmentSum
- SparseUnsortedSegmentSumGradient
- SparseUnsortedSegmentWeightedSum
- SparseUnsortedSegmentWeightedSumGradient
- SpatialBN
- SpatialBNGradient
- Split
- Sqr
- SquareRootDivide
- SquaredL2Distance
- SquaredL2DistanceGradient
- Squeeze
- StatRegistryCreate
- StatRegistryExport
- StatRegistryUpdate
- StopGradient
- StringEndsWith
- StringIndexCreate
- StringJoin
- StringPrefix
- StringStartsWith
- StringSuffix
- Sub
- Sum
- SumElements
- SumElementsGradient
- SumInt
- SumReduceLike
- SumSqrElements
- Summarize
- TT
- Tanh
- TanhGradient
- TensorProtosDBInput
- TensorVectorSize
- TextFileReaderRead
- Tile
- TileGradient
- TimerBegin
- TimerEnd
- TopK
- Transpose
- UnPackRecords
- UniformFill
- UniformIntFill
- Unique
- UniqueUniformFill
- UnpackSegments
- UnsafeCoalesce
- UnsortedSegmentMean
- UnsortedSegmentMeanGradient
- UnsortedSegmentSum
- UnsortedSegmentSumGradient
- UnsortedSegmentWeightedSum
- UnsortedSegmentWeightedSumGradient
- WallClockTime
- WeightedSum
- Where
- XavierFill
- Xor
- rnn_internal_accumulate_gradient_input
- Adagrad
- Adam
- AtomicIter
- CloseBlobsQueue
- CloseRebatchingQueue
- CreateBlobsQueue
- CreateDB
- CreateRebatchingQueue
- DequeueBlobs
- DequeueRebatchingQueue
- EnqueueBlobs
- EnqueueRebatchingQueue
- Ftrl
- ImageInput
- Iter
- LearningRate
- MomentumSGD
- MomentumSGDUpdate
- PackedFC
- Python
- PythonGradient
- RmsProp
- SafeDequeueBlobs
- SafeEnqueueBlobs
- SparseAdagrad
- SparseAdam
- SparseFtrl
- SparseMomentumSGDUpdate
- VideoInput
- WeightedSampleDequeueBlobs
- FCGradient_Decomp
- FCGradient_Prune
- FC_Decomp
- FC_Prune
- FC_Sparse
- FunHash
- FunHashGradient
- SparseFunHash
- SparseFunHashGradient
- SparseMatrixReshape
- TTContraction
- TTContractionGradient
- TTPad
- TTPadGradient
- FC_Dcomp
- MaxPoolWithIndexGradient
- ReluFp16
- ReluFp16Gradient
- Snapshot
- SparseLabelToDense
- StumpFunc
- TTLinearGradient
Accumulate
Accumulate operator accumulates the input tensor to the output tensor. If the output tensor already has the right size, we add to it; otherwise, we first initialize the output tensor to all zeros, and then do accumulation. Any further calls to the operator, given that no one else fiddles with the output in the interim, will do simple accumulations. Accumulation is done using Axpby operation as shown:
1 | Y = 1*X + gamma*Y |
where X is the input tensor, Y is the output tensor and gamma is the multiplier argument.
Interface
Arguments | |
gamma |
(float, default 1.0) Accumulation multiplier |
Inputs | |
input |
The input tensor that has to be accumulated to the output tensor. If the output size is not the same as input size, the output tensor is first reshaped and initialized to zero, and only then, accumulation is done. |
Outputs | |
output |
Accumulated output tensor |
Code
caffe2/operators/accumulate_op.cc
AccumulateHistogram
This operator calculate thes histogram of values in input tensor. There’re 2 outputs, one for histogram of current input tensor, and another for histogram of the all input tensors accumulated through history. The output would contain num_buckets + 2 values. index[1 … num_buckets] for values in [lower_bound, upper_bound) interval. And the rest 2 for values smaller than lower_bound or greater than upper_bound respectively.
Interface
Arguments | |
lower_bound |
the lower bound value |
upper_bound |
the upper bound value |
num_buckets |
number of buckets to use in [lower_bound, upper_bound) |
Inputs | |
X |
Input tensor. |
Outputs | |
CurHist |
Output histogram of the current tensor. |
AccHist |
Accumulated histogram of the history tensor. |
Code
caffe2/operators/utility_ops.cc
Accuracy
Accuracy takes two inputs- predictions and labels, and returns a float accuracy value for the batch. Predictions are expected in the form of 2-D tensor containing a batch of scores for various classes, and labels are expected in the form of 1-D tensor containing true label indices of samples in the batch. If the score for the label index in the predictions is the highest among all classes, it is considered a correct prediction.
Interface
Inputs | |
predictions |
2-D tensor (Tensor |
labels |
1-D tensor (Tensor |
Outputs | |
accuracy |
1-D tensor (Tensor |
Code
caffe2/operators/accuracy_op.cc
Add
Performs element-wise binary addition (with limited broadcast support). If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of size 1 (a scalar value), or having its shape as a contiguous subset of the first tensor’s shape. The starting of the mutually equal shape is specified by the argument “axis”, and if it is not set, suffix matching is assumed. 1-dim expansion doesn’t work yet. For example, the following tensor shapes are supported (with broadcast=1):
1 2 3 4 5 6 | shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 |
Argument broadcast=1
needs to be passed to enable broadcasting.
Interface
Arguments | |
broadcast |
Pass 1 to enable broadcasting |
axis |
If set, defines the broadcast dimensions. See doc for details. |
Inputs | |
A |
First operand, should share the type with the second operand. |
B |
Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size. |
Outputs | |
C |
Result, has same dimensions and type as A |
Code
caffe2/operators/elementwise_op_schema.cc
AddPadding
Given a partitioned tensor T<N, D1…, Dn>, where the partitions are defined as ranges on its outer-most (slowest varying) dimension N, with given range lengths, return a tensor T<N + 2*padding_width, D1 …, Dn> with paddings added to the start and end of each range. Optionally, different paddings can be provided for beginning and end. Paddings provided must be a tensor T<D1…, Dn>. If no padding is provided, add zero padding. If no lengths vector is provided, add padding only once, at the start and end of data.
Interface
Arguments | |
padding_width |
Number of copies of padding to add around each range. |
end_padding_width |
(Optional) Specifies a different end-padding width. |
Inputs | |
data_in |
(T<N, D1…, Dn>) Input data |
lengths |
(i64) Num of elements in each range. sum(lengths) = N. |
start_padding |
T<D1…, Dn> Padding data for range start. |
end_padding |
T<D1…, Dn> (optional) Padding for range end. If not provided, start_padding is used as end_padding as well. |
Outputs | |
data_out |
(T<N + 2*padding_width, D1…, Dn>) Padded data. |
lengths_out |
(i64, optional) Lengths for each padded range. |
Code
caffe2/operators/sequence_ops.cc
Alias
Makes the output and the input share the same underlying storage. WARNING: in general, in caffe2’s operator interface different tensors should have different underlying storage, which is the assumption made by components such as the dependency engine and memory optimization. Thus, in normal situations you should not use the AliasOp, especially in a normal forward-backward pass. The Alias op is provided so one can achieve true asynchrony, such as Hogwild, in a graph. But make sure you understand all the implications similar to multi-thread computation before you use it explicitly.
Interface
Inputs | |
input |
Input tensor whose storage will be shared. |
Outputs | |
output |
Tensor of same shape as input, sharing its storage. |
Code
caffe2/operators/utility_ops.cc
Allgather
Does an allgather operation among the nodes.
Interface
Inputs | |
comm_world |
The common world. |
X |
A tensor to be allgathered. |
Outputs | |
Y |
The allgathered tensor, same on all nodes. |
Code
caffe2/operators/communicator_op.cc
Allreduce
Does an allreduce operation among the nodes. Currently only Sum is supported.
Interface
Inputs | |
comm_world |
The common world. |
X |
A tensor to be allreduced. |
Outputs | |
Y |
The allreduced tensor, same on all nodes. |
Code
caffe2/operators/communicator_op.cc
And
Performs element-wise logical operation and
(with limited broadcast support).
Both input operands should be of type bool
.
If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of size 1 (a scalar value), or having its shape as a contiguous subset of the first tensor’s shape. The starting of the mutually equal shape is specified by the argument “axis”, and if it is not set, suffix matching is assumed. 1-dim expansion doesn’t work yet.
For example, the following tensor shapes are supported (with broadcast=1):
1 2 3 4 5 6 | shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 |
Argument broadcast=1
needs to be passed to enable broadcasting.
Interface
Arguments | |
broadcast |
Pass 1 to enable broadcasting |
axis |
If set, defines the broadcast dimensions. See doc for details. |
Inputs | |
A |
First operand. |
B |
Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size. |
Outputs | |
C |
Result, has same dimensions and A and type bool |
Code
caffe2/operators/elementwise_op_schema.cc
Append
Append input 2 to the end of input 1. Input 1 must be the same as output, that is, it is required to be in-place. Input 1 may have to be re-allocated in order for accommodate to the new size. Currently, an exponential growth ratio is used in order to ensure amortized constant time complexity. All except the outer-most dimension must be the same between input 1 and 2.
Interface
Inputs | |
dataset |
The tensor to be appended to. |
new_data |
Tensor to append to the end of dataset. |
Outputs | |
dataset |
Same as input 0, representing the mutated tensor. |
Code
caffe2/operators/dataset_ops.cc
AtomicAppend
No documentation yet.
Code
caffe2/operators/dataset_ops.cc
AtomicFetchAdd
Given a mutex and two int32 scalar tensors, performs an atomic fetch add by mutating the first argument and adding it to the second input argument. Returns the updated integer and the value prior to the update.
Interface
Inputs | |
mutex_ptr |
Blob containing to a unique_ptr |
mut_value |
Value to be mutated after the sum. |
increment |
Value to add to the first operand. |
Outputs | |
mut_value |
Mutated value after sum. Usually same as input 1. |
fetched_value |
Value of the first operand before sum. |
Code
caffe2/operators/atomic_ops.cc
AveragePool
AveragePool consumes an input blob X and applies average pooling across the the blob according to kernel sizes, stride sizes, and pad lengths defined by the ConvPoolOpBase operator. Average pooling consisting of averaging all values of a subset of the input tensor according to the kernel size and downsampling the data into the output blob Y for further processing.
Interface
Inputs | |
X |
Input data tensor from the previous operator; dimensions depend on whether the NCHW or NHWC operators are being used. For example, in the former, the input has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. The corresponding permutation of dimensions is used in the latter case. |
Outputs | |
Y |
Output data tensor from average pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. |
Code
AveragePoolGradient
No documentation yet.
Code
caffe2/operators/pool_gradient_op.cc
AveragedLoss
AveragedLoss takes in a 1-D tensor as input and returns a single output float value which represents the average of input data (average of the losses).
Interface
Inputs | |
input |
The input data as Tensor |
Outputs | |
output |
The output tensor of size 1 containing the averaged value. |
Code
AveragedLossGradient
No documentation yet.
Code
BatchBoxCox
Input data
is a N * D matrix. Apply box-cox transform for each column.
lambda1
and lambda2
is of size D that defines the hyper-paramteres for the transform of each column x
of the input data
:
1 2 3 | ln(x + lambda2), if lambda1 == 0 ((x + lambda2)^lambda1 - 1)/lambda1, if lambda1 != 0 |
Interface
Inputs | |
data |
input float or double N * D matrix |
lambda1 |
tensor of size D with the same type as data |
lambda2 |
tensor of size D with the same type as data |
Outputs | |
output |
output matrix that applied box-cox transform |
Code
caffe2/operators/batch_box_cox_op.cc
BatchMatMul
Batch Matrix multiplication Yi = Ai * Bi, where A has size (C x M x K), B has size (C x K x N) where C is the batch size and i ranges from 0 to C-1.
Interface
Arguments | |
trans_a |
Pass 1 to transpose A before multiplication |
trans_b |
Pass 1 to transpose B before multiplication |
Inputs | |
A |
3D matrix of size (C x M x K) |
B |
3D matrix of size (C x K x N) |
Outputs | |
Y |
3D matrix of size (C x M x N) |
Code
caffe2/operators/batch_matmul_op.cc
BatchOneHot
Input is a matrix tensor. Its first dimension is the batch size. Expand each column of it using one hot encoding. The lengths
specifies the size of each column after encoding, and the values
is the dictionary value of one-hot encoding for each column. For example If data = [[2, 3], [4, 1], [2, 5]], lengths = [2, 3], and values = [2, 4, 1, 3, 5], then output = [[1, 0, 0, 1, 0], [0, 1, 1, 0, 0], [1, 0, 0, 0, 1]]
Interface
Inputs | |
data |
input tensor matrix |
lengths |
the size is the same as the width of the data |
values |
one hot encoding dictionary values |
Outputs | |
output |
output matrix that expands each input column with one hot encoding |
Code
caffe2/operators/one_hot_ops.cc
BatchToSpace
BatchToSpace for 4-D tensors of type T. Rearranges (permutes) data from batch into blocks of spatial data, followed by cropping. This is the reverse transformation of SpaceToBatch. More specifically, this op outputs a copy of the input tensor where values from the batch dimension are moved in spatial blocks to the height and width dimensions, followed by cropping along the height and width dimensions.
Code
caffe2/operators/space_batch_op.cc
BooleanMask
Given a data tensor and a 1D boolean mask tensor, returns a tensor containing only the elements corresponding to positions where the mask is true.
Interface
Inputs | |
data |
The 1D, original data tensor. |
mask |
A tensor of bools of same shape as data . |
Outputs | |
masked_data |
A tensor of same type as data . |
Code
caffe2/operators/boolean_mask_ops.cc
BooleanMaskLengths
Given a tensor of int32 segment lengths and a mask (boolean) tensor, return the segment lengths of a corresponding segmented tensor after BooleanMask is applied.
Interface
Inputs | |
lengths |
A 1D int32 tensor representing segment lengths. |
mask |
A 1D bool tensor of values to keep. |
Outputs | |
masked_lengths |
Segment lengths of a masked tensor. |
Code
caffe2/operators/boolean_mask_ops.cc
BooleanUnmask
Given a series of mask and values, reconstruct values together according to masks. A comprehensive example: mask1
1 | = True, False, True, False, False |
values1 = 1.0, 3.0 mask2
1 | = False, True, False, False, False |
values2 = 2.0 mask3
1 | = False, False, False, True, True |
values3 = 4.0, 5.0 Reconstruct by: output = net.BooleanUnmask([mask1, values1, mask2, values2, mask3, values3], [“output”]) We get: output = 1.0, 2.0, 3.0, 4.0, 5.0 Note that for all mask positions, there must be at least one True. If for a field there are multiple True’s, we will accept the first value. For example: Example 1: mask1
1 | = True, False |
values1 = 1.0 mask2
1 | = False, False |
values2 = This is not allowed: output = net.BooleanUnmask([mask1, values1, mask2, values2], [“output”]) Example 2: mask1
1 | = True, False |
values1 = 1.0 mask2
1 | = True, True |
values2 = 2.0, 2.0 output = net.BooleanUnmask([mask1, values1, mask2, values2], [“output”]) We get: output = 1.0, 2.0
Interface
Outputs | |
unmasked_data |
The final reconstructed unmasked data |
Code
caffe2/operators/boolean_unmask_ops.cc
Broadcast
Does a broadcast operation from the root node to every other node. The tensor on each node should have been pre-created with the same shape and data type.
Interface
Arguments | |
root |
(int, default 0) the root to run broadcast from. |
Inputs | |
comm_world |
The common world. |
X |
A tensor to be broadcasted. |
Outputs | |
X |
In-place as input 1. |
Code
caffe2/operators/communicator_op.cc
Cast
The operator casts the elements of a given input tensor to a data type specified by the ‘to’ argument and returns an output tensor of the same size in the converted type. The ‘to’ argument must be one of the data types specified in the ‘DataType’ enum field in the TensorProto message. If the ‘to’ argument is not provided or is not one of the enumerated types in DataType, Caffe2 throws an Enforce error. NOTE: Casting to and from strings is not supported yet.
Interface
Arguments | |
to |
The data type to which the elements of the input tensor are cast.Strictly must be one of the types from DataType enum in TensorProto |
Inputs | |
input |
Input tensor to be cast. |
Outputs | |
output |
Output tensor with the same shape as input with type specified by the ‘to’ argument |
Code
CheckAtomicBool
Copy the value of a atomic
Interface
Inputs | |
atomic_bool |
Blob containing a unique_ptr<atomic |
Outputs | |
value |
Copy of the value for the atomic |
Code
caffe2/operators/atomic_ops.cc
CheckCounterDone
If the internal count value <= 0, outputs true, otherwise outputs false,
Interface
Inputs | |
counter |
A blob pointing to an instance of a counter. |
Outputs | |
done |
true if the internal count is zero or negative. |
Code
caffe2/operators/counter_ops.cc
CheckDatasetConsistency
Checks that the given data fields represents a consistent dataset unther the schema specified by the fields
argument. Operator fails if the fields are not consistent. If data is consistent, each field’s data can be safely appended to an existing dataset, keeping it consistent.
Interface
Arguments | |
fields |
List of strings representing the string names in the formatspecified in the doc for CreateTreeCursor. |
Inputs | |
field_0 |
Data for field 0. |
Code
caffe2/operators/dataset_ops.cc
Checkpoint
The Checkpoint operator is similar to the Save operator, but allows one to save to db every few iterations, with a db name that is appended with the iteration count. It takes [1, infinity) number of inputs and has no output. The first input has to be a TensorCPU of type int and has size 1 (i.e. the iteration counter). This is determined whether we need to do checkpointing.
Interface
Arguments | |
absolute_path |
(int, default 0) if set, use the db path directly and do not prepend the current root folder of the workspace. |
db |
(string) a template string that one can combine with the iteration to create the final db name. For example, “/home/lonestarr/checkpoint_%08d.db” |
db_type |
(string) the type of the db. |
every |
(int, default 1) the checkpointing is carried out when (iter mod every) is zero. |
Code
caffe2/operators/load_save_op.cc
Clip
Clip operator limits the given input within an interval. The interval is specified with arguments ‘min’ and ‘max’. They default to numeric_limits::min() and numeric_limits::max() respectively. The clipping operation can be done in in-place fashion too, where the input and output blobs are the same.
Interface
Arguments | |
min |
Minimum value, under which element is replaced by min |
max |
Maximum value, above which element is replaced by max |
Inputs | |
input |
Input tensor (Tensor |
output |
Output tensor (Tensor |
Code
ClipGradient
No documentation yet.
Code
Col2Im
No documentation yet.
Code
CollectTensor
Collect tensor into tensor vector by reservoir sampling, argument num_to_collect indicates the max number of tensors that will be collcted. The first half of the inputs are tensor vectors, which are also the outputs. The second half of the inputs are the tensors to be collected into each vector (in the same order). The input tensors are collected in all-or-none manner. If they are collected, they will be placed at the same index in the output vectors.
Interface
Arguments | |
num_to_collect |
The max number of tensors to collect |
Code
caffe2/operators/dataset_ops.cc
ComputeOffset
Compute the offsets matrix given cursor and data blobs. Need to be ran at beginning or after reseting cursor Input(0) is a blob pointing to a TreeCursor, and [Input(1),… Input(num_fields)] a list of tensors containing the data for each field of the dataset. ComputeOffset is thread safe.
Interface
Inputs | |
cursor |
A blob containing a pointer to the cursor. |
dataset_field_0 |
First dataset field |
Outputs | |
field_0 |
Tensor containing offset info for this chunk. |
Code
caffe2/operators/dataset_ops.cc
Concat
Concatenate a list of tensors into a single tensor
Interface
Arguments | |
axis |
Which axis to concat on |
order |
Either NHWC or HCWH, will concat on C axis |
Outputs | |
concat_result |
Concatenated tensor |
split_info |
The dimensions of the inputs. |
Code
caffe2/operators/concat_split_op.cc
ConcatTensorVector
Concat Tensors in the std::unique_ptr<std::vector
Interface
Inputs | |
vector of Tensor |
std::unique_ptr<std::vector |
Outputs | |
tensor |
tensor after concatenating |
Code
caffe2/operators/dataset_ops.cc
ConditionalSetAtomicBool
1 | Set an atomic<bool> to true if the given condition bool variable is true |
Interface
Inputs | |
atomic_bool |
Blob containing a unique_ptr<atomic |
condition |
Blob containing a bool |
Code
caffe2/operators/atomic_ops.cc
ConstantFill
The operator fills the elements of the output tensor with a constant value specified by the ‘value’ argument. The data type is specified by the ‘dtype’ argument. The ‘dtype’ argument must be one of the data types specified in the ‘DataType’ enum field in the TensorProto message. If the ‘dtype’ argument is not provided, the data type of ‘value’ is used. The output tensor shape is specified by the ‘shape’ argument. If the number of input is 1, the shape will be identical to that of the input at run time with optional additional dimensions appended at the end as specified by ‘extra_shape’ argument. In that case the ‘shape’ argument should not be set. If input_as_shape is set to true, then the input should be a 1D tensor containing the desired output shape (the dimensions specified in extra_shape will also be appended) NOTE: Currently, it supports data type of float, int32, int64, and bool.
Interface
Arguments | |
value |
The value for the elements of the output tensor. |
dtype |
The data type for the elements of the output tensor.Strictly must be one of the types from DataType enum in TensorProto. |
shape |
The shape of the output tensor.Cannot set the shape argument and pass in an input at the same time. |
extra_shape |
The additional dimensions appended at the end of the shape indicatedby the input blob.Cannot set the extra_shape argument when there is no input blob. |
input_as_shape |
1D tensor containing the desired output shape |
Inputs | |
input |
Input tensor (optional) to provide shape information. |
Outputs | |
output |
Output tensor of constant values specified by ‘value’argument and its type is specified by the ‘dtype’ argument |
Code
Conv
The convolution operator consumes an input vector, the filter blob and the bias blob and computes the output. Note that other parameters, such as the stride and kernel size, or the pads’ sizes in each direction are not necessary for input because they are provided by the ConvPoolOpBase operator. Various dimension checks are done implicitly, and the sizes are specified in the Input docs for this operator. As is expected, the filter is convolved with a subset of the image and the bias is added; this is done throughout the image data and the output is computed. As a side note on the implementation layout: conv_op_impl.h is the templated implementation of the conv_op.h file, which is why they are separate files.
Interface
Inputs | |
X |
Input data blob from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the NCHW usage. On the other hand, the NHWC Op has a different set of dimension constraints. |
filter |
The filter blob that will be used in the convolutions; has size (M x C x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel. |
bias |
The 1D bias blob that is added through the convolution; has size (M). |
Outputs | |
Y |
Output data blob that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths. |
Code
ConvGradient
No documentation yet.
Code
caffe2/operators/conv_gradient_op.cc
ConvTranspose
1 2 3 4 5 6 7 8 9 10 11 | The transposed convolution consumes an input vector, the filter blob, and the bias blob, and computes the output. Note that other parameters, such as the stride and kernel size, or the pads' sizes in each direction are not necessary for input because they are provided by the ConvTransposeUnpoolOpBase operator. Various dimension checks are done implicitly, and the sizes are specified in the Input docs for this operator. As is expected, the filter is deconvolved with a subset of the image and the bias is added; this is done throughout the image data and the output is computed. As a side note on the implementation layout: conv_transpose_op_impl.h is the templated implementation of the conv_transpose_op.h file, which is why they are separate files. |
Interface
Inputs | |
X |
Input data blob from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the NCHW usage. On the other hand, the NHWC Op has a different set of dimension constraints. |
filter |
The filter blob that will be used in the transposed convolution; has size (M x C x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel. |
bias |
The 1D bias blob that is added through the convolution;has size (C) |
Outputs | |
Y |
Output data blob that contains the result of the transposed convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths. |
Code
caffe2/operators/conv_transpose_op.cc
ConvTransposeGradient
No documentation yet.
Code
caffe2/operators/conv_transpose_gradient_op.cc
Copy
Copy input tensor into output, potentially across devices.
Interface
Inputs | |
input |
The input tensor. |
Outputs | |
output |
Tensor that will contain a copy of the input. |
Code
caffe2/operators/utility_ops.cc
CopyCPUToGPU
Copy tensor for CPU to GPU context. Must be run under GPU device option.
Interface
Inputs | |
input |
The input tensor. |
Outputs | |
output |
Tensor that will contain a copy of the input. |
Code
caffe2/operators/utility_ops.cc
CopyFromCPUInput
Take a CPU input tensor and copy it to an output in the current Context (GPU or CPU). This may involves cross-device MemCpy.
Interface
Inputs | |
input |
The input CPU tensor. |
Outputs | |
output |
either a TensorCUDA or a TensorCPU |
Code
caffe2/operators/utility_ops.cc
CopyGPUToCPU
Copy tensor for GPU to CPU context. Must be run under GPU device option.
Interface
Inputs | |
input |
The input tensor. |
Outputs | |
output |
Tensor that will contain a copy of the input. |
Code
caffe2/operators/utility_ops.cc
CopyOnDeviceLike
Copy input tensor into output to the specific device.
Interface
Inputs | |
input |
The input tensor. |
dst |
Tensor, on which device the copy will be performed. |
Outputs | |
output |
Tensor that will contain a copy of the input. |
Code
caffe2/operators/utility_ops.cc
CosineEmbeddingCriterion
CosineEmbeddingCriterion takes two inputs: the similarity value and the label, and computes the elementwise criterion output as output = 1 - s,
1 | if y == 1 |
1 | max(0, s - margin), if y == -1 |
Interface
Inputs | |
S |
The cosine similarity as a 1-dim TensorCPU. |
Y |
The label as a 1-dim TensorCPU with int value of 1 or -1. |
Outputs | |
loss |
The output loss with the same dimensionality as S. |
Code
caffe2/operators/cosine_embedding_criterion_op.cc
CosineEmbeddingCriterionGradient
No documentation yet.
Code
caffe2/operators/cosine_embedding_criterion_op.cc
CosineSimilarity
1 2 | Given two input float tensors X, Y, and produces one output float tensor of the cosine similarity between X and Y. |
Interface
Inputs | |
X |
1D input tensor |
Outputs | |
Y |
1D input tensor |
Code
caffe2/operators/distance_op.cc
CosineSimilarityGradient
No documentation yet.
Code
caffe2/operators/distance_op.cc
CountDown
If the internal count value > 0, decreases count value by 1 and outputs false, otherwise outputs true.
Interface
Inputs | |
counter |
A blob pointing to an instance of a counter. |
Outputs | |
done |
false unless the internal count is zero. |
Code
caffe2/operators/counter_ops.cc
CountUp
Increases count value by 1 and outputs the previous value atomically
Interface
Inputs | |
counter |
A blob pointing to an instance of a counter. |
Outputs | |
previous_count |
count value BEFORE this operation |
Code
caffe2/operators/counter_ops.cc
CreateAtomicBool
Create an unique_ptr blob to hold a atomic
Interface
Outputs | |
atomic_bool |
Blob containing a unique_ptr<atomic |
Code
caffe2/operators/atomic_ops.cc
CreateCommonWorld
Creates a common world for communication operators.
Interface
Arguments | |
size |
(int) size of the common world. |
rank |
(int) rank of this node in the common world. |
Inputs | |
kv_handler |
Key/value handler for rendezvous (optional). |
Outputs | |
comm_world |
A common world for collective operations. |
Code
caffe2/operators/communicator_op.cc
CreateCounter
Creates a count-down counter with initial value specified by the ‘init_count’ argument.
Interface
Arguments | |
init_count |
Initial count for the counter, must be >= 0. |
Outputs | |
counter |
A blob pointing to an instance of a new counter. |
Code
caffe2/operators/counter_ops.cc
CreateMutex
Creates an unlocked mutex and returns it in a unique_ptr blob.
Interface
Outputs | |
mutex_ptr |
Blob containing a std::unique_ptr |
Code
caffe2/operators/atomic_ops.cc
CreateQPSMetric
CreateQPSMetric operator create a blob that will store state that is required for computing QPSMetric. The only output of the operator will have blob with QPSMetricState as an output.
Interface
Outputs | |
output |
Blob with QPSMetricState |
Code
caffe2/operators/metrics_ops.cc
CreateTensorVector
Create a std::unique_ptr<std::vector
Code
caffe2/operators/dataset_ops.cc
CreateTextFileReader
Create a text file reader. Fields are delimited by
Interface
Arguments | |
filename |
Path to the file. |
num_pases |
Number of passes over the file. |
field_types |
List with type of each field. Type enum is found at core.DataType. |
Outputs | |
handler |
Pointer to the created TextFileReaderInstance. |
Code
caffe2/operators/text_file_reader.cc
CreateTreeCursor
Creates a cursor to iterate through a list of tensors, where some of those tensors contains the lengths in a nested schema. The schema is determined by the fields
arguments.
For example, to represent the following schema:
1 2 3 4 5 | Struct( a=Int(), b=List(List(Int), c=List( Struct( |
1 | c1=String, |
1 2 3 4 5 | c2=List(Int), ), ), ) |
the field list will be:
1 2 3 4 5 6 7 8 9 10 11 | [ "a", "b:lengths", "b:values:lengths", "b:values:values", "c:lengths", "c:c1", "c:c2:lengths", "c:c2:values", ] |
And for the following instance of the struct:
1 2 3 4 5 6 7 8 9 | Struct( a=3, b=[[4, 5], [6, 7, 8], [], [9]], c=[ Struct(c1='alex', c2=[10, 11]), Struct(c1='bob', c2=[12]), ], ) |
The values of the fields will be:
1 2 3 4 5 6 7 8 9 10 11 | { "a": [3], "b:lengths": [4], "b:values:lengths": [2, 3, 0, 1], "b:values:values": [4, 5, 6, 7, 8, 9], "c:lengths": [2], "c:c1": ["alex", "bob"], "c:c2:lengths": [2, 1], "c:c2:values", [10, 11, 12], } |
In general, every field name in the format “{prefix}:lengths” defines a domain “{prefix}”, and every subsequent field in the format “{prefx}:{field}” will be in that domain, and the length of the domain is provided for each entry of the parent domain. In the example, “b:lengths” defines a domain of length 4, so every field under domain “b” will have 4 entries.
The “lengths” field for a given domain must appear before any reference to that domain.
Returns a pointer to an instance of the Cursor, which keeps the current offset on each of the domains defined by fields
. Cursor also ensures thread-safety such that ReadNextBatch and ResetCursor can be used safely in parallel.
A cursor does not contain data per se, so calls to ReadNextBatch actually need to pass a list of blobs containing the data to read for each one of the fields.
Interface
Arguments | |
fields |
A list of strings each one representing a field of the dataset. |
Outputs | |
cursor |
A blob pointing to an instance of a new TreeCursor. |
Code
caffe2/operators/dataset_ops.cc
CrossEntropy
Operator computes the cross entropy between the input and the label set. In practice, it is most commonly used at the end of models, after the SoftMax operator and before the AveragedLoss operator. Note that CrossEntropy assumes that the soft labels provided is a 2D array of size N x D (batch size x number of classes). Each entry in the 2D label corresponds to the soft label for the input, where each element represents the correct probability of the class being selected. As such, each element must be between 0 and 1, and all elements in an entry must sum to 1. The formula used is:
1 2 | Y[i] = sum_j (label[i][j] * log(X[i][j])) |
where (i, j) is the classifier’s prediction of the jth class (the correct one), and i is the batch size. Each log has a lower limit for numerical stability.
Interface
Inputs | |
X |
Input blob from the previous layer, which is almost always the result of a softmax operation; X is a 2D array of size N x D, where N is the batch size and D is the number of classes |
label |
Blob containing the labels used to compare the input |
Outputs | |
Y |
Output blob after the cross entropy computation |
Code
caffe2/operators/cross_entropy_op.cc
CrossEntropyGradient
No documentation yet.
Code
caffe2/operators/cross_entropy_op.cc
DBExists
Checks if the DB exists.
Interface
Arguments | |
absolute_path |
(int, default 0) if set, use the db path directly and do not prepend the current root folder of the workspace. |
db_name |
(string) the path to the db to load. |
db_type |
(string) the type of the db. |
Outputs | |
exists |
A scalar bool Tensor. |
Code
caffe2/operators/load_save_op.cc
DepthConcat
Backward compatible operator name for Concat.
Code
caffe2/operators/concat_split_op.cc
DepthSplit
Backward compatible operator name for Split.
Code
caffe2/operators/concat_split_op.cc
Div
Performs element-wise binary division (with limited broadcast support). If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of size 1 (a scalar value), or having its shape as a contiguous subset of the first tensor’s shape. The starting of the mutually equal shape is specified by the argument “axis”, and if it is not set, suffix matching is assumed. 1-dim expansion doesn’t work yet. For example, the following tensor shapes are supported (with broadcast=1):
1 2 3 4 5 6 | shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 |
Argument broadcast=1
needs to be passed to enable broadcasting.
Interface
Arguments | |
broadcast |
Pass 1 to enable broadcasting |
axis |
If set, defines the broadcast dimensions. See doc for details. |
Inputs | |
A |
First operand, should share the type with the second operand. |
B |
Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size. |
Outputs | |
C |
Result, has same dimensions and type as A |
Code
caffe2/operators/elementwise_op_schema.cc
DivGradient
No documentation yet.
Code
caffe2/operators/elementwise_op_schema.cc
DotProduct
1 2 | Given two input float tensors X, Y, and produces one output float tensor of the dot product between X and Y. |
Interface
Inputs | |
X |
1D input tensor |
Outputs | |
Y |
1D input tensor |
Code
caffe2/operators/distance_op.cc
DotProductGradient
No documentation yet.
Code
caffe2/operators/distance_op.cc
DotProductWithPadding
1 2 3 4 5 | Given two input float tensors X, Y with different shapes and produces one output float tensor of the dot product between X and Y. We currently support two kinds of strategies to achieve this. Before doing normal dot_product 1) pad the smaller tensor (using pad_value) to the same shape as the other one. 2) replicate the smaller tensor to the same shape as the other one. |
Interface
Arguments | |
pad_value |
the padding value for tensors with smaller dimension |
replicate |
wehther to replicate the smaller tensor or not |
Inputs | |
X |
1D input tensor |
Outputs | |
Y |
1D input tensor |
Code
caffe2/operators/distance_op.cc
DotProductWithPaddingGradient
No documentation yet.
Code
caffe2/operators/distance_op.cc
Dropout
Dropout takes one input data (Tensor
Interface
Arguments | |
ratio |
(float, default 0.5) the ratio of random dropout |
is_test |
(int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X. |
Inputs | |
data |
The input data as Tensor. |
Outputs | |
output |
The output. |
mask |
The output mask. If is_test is nonzero, this output is not filled. |
Code
caffe2/operators/dropout_op.cc
DropoutGrad
No documentation yet.
Code
caffe2/operators/dropout_op.cc
EQ
Performs element-wise equality comparison ==
(with limited broadcast support).
If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of size 1 (a scalar value), or having its shape as a contiguous subset of the first tensor’s shape. The starting of the mutually equal shape is specified by the argument “axis”, and if it is not set, suffix matching is assumed. 1-dim expansion doesn’t work yet.
For example, the following tensor shapes are supported (with broadcast=1):
1 2 3 4 5 6 | shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 |
Argument broadcast=1
needs to be passed to enable broadcasting.
Interface
Arguments | |
broadcast |
Pass 1 to enable broadcasting |
axis |
If set, defines the broadcast dimensions. See doc for details. |
Inputs | |
A |
First operand, should share the type with the second operand. |
B |
Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size. |
Outputs | |
C |
Result, has same dimensions and A and type bool |
Code
caffe2/operators/elementwise_op_schema.cc
ElementwiseLinear
1 2 | Given inputs X of size (N x D), a of size D and b of size D, the op computes Y of size (N X D) where Y_{nd} = X_{nd} * a_d + b_d |
Interface
Arguments | |
axis |
default to 1; describes the axis of the inputs; defaults to one because the 0th axis most likely describes the batch_size |
Inputs | |
X |
2D input tensor of size (N X D) data |
a |
1D scaling factors of size D |
b |
1D biases of size D |
Outputs | |
Y |
2D output tensor |
Code
caffe2/operators/elementwise_linear_op.cc
ElementwiseLinearGradient
No documentation yet.
Code
caffe2/operators/elementwise_linear_op.cc
Elu
Elu takes one input data (Tensor
Interface
Inputs | |
X |
1D input tensor |
Outputs | |
Y |
1D input tensor |
Code
EluGradient
EluGradient takes both Y and dY and uses this to update dX according to the chain rule and derivatives of the rectified linear function.
Code
EnsureCPUOutput
Take an input tensor in the current Context (GPU or CPU) and create an output which is always a TensorCPU. This may involves cross-device MemCpy.
Interface
Inputs | |
input |
The input CUDA or CPU tensor. |
Outputs | |
output |
TensorCPU that is a copy of the input. |
Code
caffe2/operators/utility_ops.cc
EnsureDense
This operator converts dense or sparse gradients to dense ones. Therefore, sparse gradient can be back propagated to Operators that consume dense gradients only (e.g., FCGradient). The operator’s behaviors: - In forward, simply pass in place or copy input to the output.
- In backward, if the gradient passed-in is sparse gradient, change it to
1 | dense gradient in linear time; otherwise, simply pass the dense gradient. |
Interface
Inputs | |
input |
Input tensors. |
Outputs | |
output |
Output tensor. Same dimension as inputs. |
Code
caffe2/operators/utility_ops.cc
Exp
Calculates the exponential of the given input tensor, element-wise. This operation can be done in an in-place fashion too, by providing the same input and output blobs.
Interface
Inputs | |
input |
Input tensor |
Outputs | |
output |
The exponential of the input tensor computed element-wise |
Code
ExpandDims
Insert single-dimensional entries to the shape of a tensor.
Takes one required argument dims
, a list of dimensions that will be inserted.
Dimension indices in dims
are as seen in the output tensor. For example:
1 2 3 | Given a tensor such that tensor.Shape() = [3, 4, 5], then ExpandDims(tensor, dims=[0, 4]).Shape() == [1, 3, 4, 5, 1]) |
If the same blob is provided in input and output, the operation is copy-free.
Interface
Inputs | |
data |
Original tensor |
Outputs | |
expanded |
Reshaped tensor with same data as input. |
Code
caffe2/operators/utility_ops.cc
ExtendTensor
Extend input 0 if necessary based on max element in input 1. Input 0 must be the same as output, that is, it is required to be in-place. Input 0 may have to be re-allocated in order for accommodate to the new size. Currently, an exponential growth ratio is used in order to ensure amortized constant time complexity. All except the outer-most dimension must be the same between input 0 and 1.
Interface
Inputs | |
tensor |
The tensor to be extended. |
new_indices |
The size of tensor will be extended based on max element in new_indices. |
Outputs | |
extended_tensor |
Same as input 0, representing the mutated tensor. |
Code
caffe2/operators/extend_tensor_op.cc
FC
Computes the result of passing an input vector X into a fully connected layer with 2D weight matrix W and 1D bias vector b. The layer computes Y = X * W^T + b, where X has size (M x K), W has size (N x K), b has size (N), and Y has size (M x N), where M is the batch size. Even though b is 1D, it is resized to size (M x N) implicitly and added to each vector in the batch. These dimensions must be matched correctly, or else the operator will throw errors.
Interface
Arguments | |
axis |
(int32_t) default to 1; describes the axis of the inputs; defaults to one because the 0th axis most likely describes the batch_size |
Inputs | |
X |
2D input of size (MxK) data |
W |
2D blob of size (KxN) containing fully connected weight matrix |
b |
1D blob containing bias vector |
Outputs | |
Y |
2D output tensor |
Code
caffe2/operators/fully_connected_op.cc
FCGradient
No documentation yet.
Code
caffe2/operators/fully_connected_op.cc
FeedBlob
FeedBlobs the content of the blobs. The input and output blobs should be one-to-one inplace.
Interface
Arguments | |
value |
(string) if provided then we will use this string as the value for theprovided output tensor |
Code
caffe2/operators/feed_blob_op.cc
Find
Finds elements of second input from first input,
1 2 3 4 | outputting the last (max) index for each query. If query not find, inserts missing_value. See IndexGet() for a version that modifies the index when values are not found. |
Interface
Arguments | |
missing_value |
Placeholder for items that are not found |
Inputs | |
index |
Index (integers) |
query |
Needles / query |
Outputs | |
query_indices |
Indices of the needles in index or ‘missing value’ |
Code
FindDuplicateElements
Shrink the data tensor by removing data blocks with given zero-based indices in the outermost dimension of the tensor. Indices are not assumed in any order or unique but with the range [0, blocks_size). Indices could be empty.
Interface
Inputs | |
data |
a 1-D tensor. |
Outputs | |
indices |
indices of duplicate elements in data, excluding first occurrences. |
Code
caffe2/operators/find_duplicate_elements_op.cc
Flatten
Flattens the input tensor into a 2D matrix, keeping the first dimension unchanged.
Interface
Inputs | |
input |
A tensor of rank >= 2. |
Outputs | |
output |
A tensor of rank 2 with the contents of the input tensor, with first dimension equal first dimension of input, and remaining input dimensions flatenned into the inner dimension of the output. |
Code
caffe2/operators/utility_ops.cc
FlattenToVec
Flattens the input tensor into a 1D vector.
Interface
Inputs | |
input |
A tensor of rank >= 1. |
Outputs | |
output |
A tensor of rank 1 with the contents of the input tensor |
Code
caffe2/operators/utility_ops.cc
FloatToHalf
No documentation yet.
Code
caffe2/operators/half_float_ops.cc
Free
Frees the content of the blobs. The input and output blobs should be one-to-one inplace.
Code
GE
Performs element-wise greater or equal than comparison >=
(with limited broadcast support).
If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of size 1 (a scalar value), or having its shape as a contiguous subset of the first tensor’s shape. The starting of the mutually equal shape is specified by the argument “axis”, and if it is not set, suffix matching is assumed. 1-dim expansion doesn’t work yet.
For example, the following tensor shapes are supported (with broadcast=1):
1 2 3 4 5 6 | shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 |
Argument broadcast=1
needs to be passed to enable broadcasting.
Interface
Arguments | |
broadcast |
Pass 1 to enable broadcasting |
axis |
If set, defines the broadcast dimensions. See doc for details. |
Inputs | |
A |
First operand, should share the type with the second operand. |
B |
Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size. |
Outputs | |
C |
Result, has same dimensions and A and type bool |
Code
caffe2/operators/elementwise_op_schema.cc
GT
Performs element-wise greater than comparison >
(with limited broadcast support).
If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of size 1 (a scalar value), or having its shape as a contiguous subset of the first tensor’s shape. The starting of the mutually equal shape is specified by the argument “axis”, and if it is not set, suffix matching is assumed. 1-dim expansion doesn’t work yet.
For example, the following tensor shapes are supported (with broadcast=1):
1 2 3 4 5 6 | shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 |
Argument broadcast=1
needs to be passed to enable broadcasting.
Interface
Arguments | |
broadcast |
Pass 1 to enable broadcasting |
axis |
If set, defines the broadcast dimensions. See doc for details. |
Inputs | |
A |
First operand, should share the type with the second operand. |
B |
Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size. |
Outputs | |
C |
Result, has same dimensions and A and type bool |
Code
caffe2/operators/elementwise_op_schema.cc
Gather
Given DATA tensor of rank r >= 1, and INDICES tensor of rank q, gather entries of the outer-most dimension of DATA indexed by INDICES, and concatenate them in an output tensor of rank q + (r - 1). Example:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | DATA = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] INDICES = [ [0, 1], [1, 2], ] OUTPUT = [ [ [1.0, 1.2], [2.3, 3.4], ], [ [2.3, 3.4], [4.5, 5.7], ], ] |
Interface
Inputs | |
DATA |
Tensor of rank r >= 1. |
INDICES |
Tensor of int32/int64 indices, of any rank q. |
Outputs | |
OUTPUT |
Tensor of rank q + (r - 1). |
Code
caffe2/operators/utility_ops.cc
GatherPadding
Gather the sum of start and end paddings in a padded input sequence. Used in order to compute the gradients of AddPadding w.r.t the padding tensors.
Interface
Arguments | |
padding_width |
Outer-size of padding present around each range. |
end_padding_width |
(Optional) Specifies a different end-padding width. |
Inputs | |
data_in |
T<N, D1…, Dn> Padded input data |
lengths |
(i64) Num of elements in each range. sum(lengths) = N. If not provided, considers all data as a single segment. |
Outputs | |
padding_sum |
Sum of all start paddings, or of all paddings if end_padding_sum is not provided. |
end_padding_sum |
T<D1…, Dn> Sum of all end paddings, if provided. |
Code
caffe2/operators/sequence_ops.cc
GatherRanges
Given DATA tensor of rank 1, and RANGES tensor of rank 3, gather corresponding ranges into a 1-D tensor OUTPUT. RANGES dimentions description: 1: represents list of examples within a batch 2: represents list features 3: two values which are start and length or a range (to be applied on DATA) Another output LENGTHS represents each example length within OUTPUT Example:
1 2 3 4 5 6 7 8 9 10 11 12 13 | DATA = [1, 2, 3, 4, 5, 6] RANGES = [ [ [0, 1], [2, 2], ], [ [4, 1], [5, 1], ] ] OUTPUT = [1, 3, 4, 5, 6] LENGTHS = [3, 2] |
Interface
Inputs | |
DATA |
Tensor of rank 1. |
RANGES |
Tensor of int32/int64 ranges, of dims (N, M, 2). Where N is number of examples and M is a size of each example. Last dimention represents a range in the format (start, lengths) |
Outputs | |
OUTPUT |
1-D tensor of size sum of range lengths |
LENGTHS |
1-D tensor of size N with lengths over gathered data for each row in a batch. sum(LENGTHS) == OUTPUT.size() |
Code
caffe2/operators/utility_ops.cc
GaussianFill
No documentation yet.
Code
GetAllBlobNames
Return a 1D tensor of strings containing the names of each blob in the active workspace.
Interface
Arguments | |
include_shared |
(bool, default true) Whether to include blobs inherited from parent workspaces. |
Outputs | |
blob_names |
1D tensor of strings containing blob names. |
Code
caffe2/operators/workspace_ops.cc
GetGPUMemoryUsage
Fetches GPU memory stats from CUDAContext. Result is stored
1 2 3 4 5 | in output blob with shape (2, num_gpus). First row contains the total current memory usage, and the second row the maximum usage during this execution. NOTE: --caffe2_gpu_memory_tracking flag must be enabled to use this op. |
Code
caffe2/operators/mem_query_op.cu
GivenTensorBoolFill
No documentation yet.
Code
caffe2/operators/given_tensor_fill_op.cc
GivenTensorFill
No documentation yet.
Code
caffe2/operators/given_tensor_fill_op.cc
GivenTensorInt64Fill
No documentation yet.
Code
caffe2/operators/given_tensor_fill_op.cc
GivenTensorIntFill
No documentation yet.
Code
caffe2/operators/given_tensor_fill_op.cc
GivenTensorStringFill
No documentation yet.
Code
caffe2/operators/given_tensor_fill_op.cc
HSoftmax
Hierarchical softmax is an operator which approximates the softmax operator while giving significant training speed gains and reasonably comparable performance. In this operator, instead of calculating the probabilities of all the classes, we calculate the probability of each step in the path from root to the target word in the hierarchy.
The operator takes a 2-D tensor (Tensor
Interface
Arguments | |
hierarchy |
Serialized HierarchyProto string containing list of vocabulary words and their paths from root of hierarchy to the leaf |
Inputs | |
X |
Input data from previous layer |
W |
2D blob containing ‘stacked’ fully connected weight matrices. Each node in the hierarchy contributes one FC weight matrix if it has children nodes. Dimension is N*D, D is input dimension of data (X), N is sum of all output dimensions, or total number of nodes (excl root) |
b |
1D blob with N parameters |
labels |
int word_id of the target word |
Outputs | |
Y |
1-D of log probability outputs, one per sample |
intermediate_output |
Extra blob to store the intermediate FC and softmax outputs for each node in the hierarchical path of a word. The outputs from samples are stored in consecutive blocks in the forward pass and are used in reverse order in the backward gradientOp pass |
Code
caffe2/operators/h_softmax_op.cc
HSoftmaxGradient
No documentation yet.
Code
caffe2/operators/h_softmax_op.cc
HSoftmaxSearch
1 2 3 | HSoftmaxSearch is an operator to generate the most possible paths given a well-trained model and input vector. Greedy algorithm is used for pruning the search tree. |
Interface
Arguments | |
tree |
Serialized TreeProto string containing a tree including all intermidate nodes and leafs. All nodes must have names for correct outputs |
beam |
beam used for pruning tree. The pruning algorithm is that only children, whose score is smaller than parent’s score puls beam, will be propagated. |
topN |
Number of nodes in outputs |
Inputs | |
X |
Input data from previous layer |
W |
The matrix trained from Softmax Ops |
b |
The bias traiend from Softmax Ops |
Outputs | |
Y_names |
The name of selected nodes and leafs. For nodes, it will be the name defined in the tree. For leafs, it will be the index of the word in the tree. |
Y_scores |
The corresponding scores of Y_names |
Code
caffe2/operators/h_softmax_op.cc
HalfToFloat
No documentation yet.
Code
caffe2/operators/half_float_ops.cc
HasElements
Returns true iff the input tensor has size > 0
Interface
Inputs | |
tensor |
Tensor of any type. |
Outputs | |
has_elements |
Scalar bool tensor. True if input is not empty. |
Code
caffe2/operators/utility_ops.cc
HuffmanTreeHierarchy
1 2 | HuffmanTreeHierarchy is an operator to generate huffman tree hierarchy given the input labels. It returns the tree as seralized HierarchyProto |
Interface
Arguments | |
num_classes |
The number of classes used to build the hierarchy. |
Inputs | |
Labels |
The labels vector |
Outputs | |
Hierarch |
Huffman coding hierarchy of the labels |
Code
caffe2/operators/h_softmax_op.cc
Im2Col
The Im2Col operator from Matlab.
Interface
Inputs | |
X |
4-tensor in NCHW or NHWC. |
Outputs | |
Y |
4-tensor. For NCHW: N x (C x kH x kW) x outH x outW.For NHWC: N x outH x outW x (kH x kW x C |
Code
IndexFreeze
Freezes the given index, disallowing creation of new index entries. Should not be called concurrently with IndexGet.
Interface
Inputs | |
handle |
Pointer to an Index instance. |
Outputs | |
handle |
The input handle. |
Code
IndexGet
Given an index handle and a tensor of keys, return an Int tensor of same shape containing the indices for each of the keys. If the index is frozen, unknown entries are given index 0. Otherwise, new entries are added into the index. If an insert is necessary but max_elements has been reached, fail.
Interface
Inputs | |
handle |
Pointer to an Index instance. |
keys |
Tensor of keys to be looked up. |
Outputs | |
indices |
Indices for each of the keys. |
Code
IndexLoad
Loads the index from the given 1-D tensor. Elements in the tensor will be given consecutive indexes starting at 1. Fails if tensor contains repeated elements.
Interface
Arguments | |
skip_first_entry |
If set, skips the first entry of the tensor. This allows to load tensors that are aligned with an embedding, where the first entry corresponds to the default 0 index entry. |
Inputs | |
handle |
Pointer to an Index instance. |
items |
1-D tensor with elements starting with index 1. |
Outputs | |
handle |
The input handle. |
Code
IndexSize
Returns the number of entries currently present in the index.
Interface
Inputs | |
handle |
Pointer to an Index instance. |
Outputs | |
items |
Scalar int64 tensor with number of entries. |
Code
IndexStore
Stores the keys of this index in a 1-D tensor. Since element 0 is reserved for unknowns, the first element of the output tensor will be element of index 1.
Interface
Inputs | |
handle |
Pointer to an Index instance. |
Outputs | |
items |
1-D tensor with elements starting with index 1. |
Code
InstanceNorm
Carries out instance normalization as described in the paper https://arxiv.org/abs/1607.08022. Depending on the mode it is being run, there are multiple cases for the number of outputs, which we list below: * Output case #1: output * Output case #2: output, saved_mean
1 | - don't use, doesn't make sense but won't crash |
- Output case #3: output, saved_mean, saved_inv_stdev
1 2 | - Makes sense for training only |
For training mode, type 3 is faster in the sense that for the backward pass, it is able to reuse the saved mean and inv_stdev in the gradient computation.
Interface
Arguments | |
epsilon |
The epsilon value to use to avoid division by zero. |
order |
A StorageOrder string. |
Inputs | |
input |
The input 4-dimensional tensor of shape NCHW or NHWC depending on the order parameter. |
scale |
The input 1-dimensional scale tensor of size C. |
bias |
The input 1-dimensional bias tensor of size C. |
Outputs | |
output |
The output 4-dimensional tensor of the same shape as input. |
saved_mean |
Optional saved mean used during training to speed up gradient computation. Should not be used for testing. |
saved_inv_stdev |
Optional saved inverse stdev used during training to speed up gradient computation. Should not be used for testing. |
Code
caffe2/operators/instance_norm_op.cc
InstanceNormGradient
No documentation yet.
Code
caffe2/operators/instance_norm_gradient_op.cc
IntIndexCreate
Creates a dictionary that maps int32 keys to consecutive integers from 1 to max_elements. Zero is reserved for unknown keys.
Interface
Arguments | |
max_elements |
Max number of elements, including the zero entry. |
Outputs | |
handler |
Pointer to an Index instance. |
Code
IsEmpty
Returns true iff the input tensor has size == 0
Interface
Inputs | |
tensor |
Tensor of any type. |
Outputs | |
is_empty |
Scalar bool tensor. True if input is empty. |
Code
caffe2/operators/utility_ops.cc
IsMemberOf
IsMemberOf takes input data (Tensor
Interface
Arguments | |
value |
Declare one value for the membership test. |
dtype |
The data type for the elements of the output tensor.Strictly must be one of the types from DataType enum in TensorProto. |
Inputs | |
X |
Input tensor of any shape |
Outputs | |
Y |
Output tensor (same size as X containing booleans) |
Code
caffe2/operators/elementwise_logical_ops.cc
L1Distance
1 2 | Given two input float tensors X, Y, and produces one output float tensor of the L1 difference between X and Y, computed as L1(x,y) = sum over |x-y| |
Interface
Inputs | |
X |
1D input tensor |
Outputs | |
Y |
1D input tensor |
Code
caffe2/operators/distance_op.cc
L1DistanceGradient
No documentation yet.
Code
caffe2/operators/distance_op.cc
LE
Performs element-wise less or equal than comparison <=
(with limited broadcast support).
If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of size 1 (a scalar value), or having its shape as a contiguous subset of the first tensor’s shape. The starting of the mutually equal shape is specified by the argument “axis”, and if it is not set, suffix matching is assumed. 1-dim expansion doesn’t work yet.
For example, the following tensor shapes are supported (with broadcast=1):
1 2 3 4 5 6 | shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 |
Argument broadcast=1
needs to be passed to enable broadcasting.
Interface
Arguments | |
broadcast |
Pass 1 to enable broadcasting |
axis |
If set, defines the broadcast dimensions. See doc for details. |
Inputs | |
A |
First operand, should share the type with the second operand. |
B |
Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size. |
Outputs | |
C |
Result, has same dimensions and A and type bool |
Code
caffe2/operators/elementwise_op_schema.cc
LRN
No documentation yet.
Code
caffe2/operators/local_response_normalization_op.cc
LRNGradient
No documentation yet.
Code
caffe2/operators/local_response_normalization_op.cc
LSTMUnit
LSTMUnit computes the activations of a standard LSTM (without peephole connections), in a sequence-length aware fashion. Concretely, given the (fused) inputs X (TxNxD), the previous cell state (NxD), and the sequence lengths (N), computes the LSTM activations, avoiding computation if the input is invalid (as in, the value at X{t][n] >= seqLengths[n].
Interface
Arguments | |
forget_bias |
Bias term to add in while calculating forget gate |
Code
caffe2/operators/lstm_unit_op.cc
LSTMUnitGradient
No documentation yet.
Code
caffe2/operators/lstm_unit_op.cc
LT
Performs element-wise less than comparison <
(with limited broadcast support).
If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of size 1 (a scalar value), or having its shape as a contiguous subset of the first tensor’s shape. The starting of the mutually equal shape is specified by the argument “axis”, and if it is not set, suffix matching is assumed. 1-dim expansion doesn’t work yet.
For example, the following tensor shapes are supported (with broadcast=1):
1 2 3 4 5 6 | shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 |
Argument broadcast=1
needs to be passed to enable broadcasting.
Interface
Arguments | |
broadcast |
Pass 1 to enable broadcasting |
axis |
If set, defines the broadcast dimensions. See doc for details. |
Inputs | |
A |
First operand, should share the type with the second operand. |
B |
Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size. |
Outputs | |
C |
Result, has same dimensions and A and type bool |
Code
caffe2/operators/elementwise_op_schema.cc
LabelCrossEntropy
Operator computes the cross entropy between the input and the label set. In practice, it is most commonly used at the end of models, after the SoftMax operator and before the AveragedLoss operator. Note that LabelCrossEntropy assumes that the label provided is either a 1D array of size N (batch size), or a 2D array of size N x 1 (batch size). Each entry in the label vector indicates which is the correct class; as such, each entry must be between 0 and D - 1, inclusive, where D is the total number of classes. The formula used is:
1 2 | Y[i] = -log(X[i][j]) |
where (i, j) is the classifier’s prediction of the jth class (the correct one), and i is the batch size. Each log has a lower limit for numerical stability.
Interface
Inputs | |
X |
Input blob from the previous layer, which is almost always the result of a softmax operation; X is a 2D array of size N x D, where N is the batch size and D is the number of classes |
label |
Blob containing the labels used to compare the input |
Outputs | |
Y |
Output blob after the cross entropy computation |
Code
caffe2/operators/cross_entropy_op.cc
LabelCrossEntropyGradient
No documentation yet.
Code
caffe2/operators/cross_entropy_op.cc
LastNWindowCollector
Collect the last N rows from input data. The purpose is to keep track of data accross batches, so for example suppose the LastNWindowCollector is called successively with the following input data [1,2,3,4] [5,6,7] [8,9,10,11] And the number of items is set to 6, then the output after the 3rd call will contain the following elements: [6,7,8,9,10,11] No guarantee is made on the ordering of elements in input. So a valid value for output could have been [11,10,9,8,7,6] Also, this method works for any order tensor, treating the first dimension as input rows and keeping the last N rows seen as input. So for instance: [[1,2],[2,3],[3,4],[4,5]] [[5,6],[6,7],[7,8]] [[8,9],[9,10],[10,11],[11,12]] A possible output would be [[6,7],[7,8],[8,9],[9,10],[10,11],[11,12]] This is not thread safe.
Interface
Arguments | |
num_to_collect |
The number of random samples to append for each positive samples |
Inputs | |
last-N buffer |
The buffer for last-N record. Should be intialized to empty tensor |
next cursor |
The cursor pointing to the next positiion that should be replaced. Should be initialized to 0. |
DATA |
tensor to collect from |
Outputs | |
last-N buffer |
Data stored in sessions |
next cursor |
Updated input cursor |
Code
caffe2/operators/last_n_window_collector.cc
LeakyRelu
LeakyRelu takes input data (Tensor
Interface
Arguments | |
alpha |
Coefficient of leakage |
Inputs | |
X |
1D input tensor |
Outputs | |
Y |
1D input tensor |
Code
caffe2/operators/leaky_relu_op.cc
LeakyReluGradient
No documentation yet.
Interface
Arguments | |
alpha |
Coefficient of leakage |
Code
caffe2/operators/leaky_relu_op.cc
LengthsGather
Gather items from sparse tensor. Sparse tensor is described by items and lengths. This operator gathers items corresponding to lengths at the given indices. This deliberately doesn’t return lengths of OUTPUTS so that both lists and maps can be supported without special cases. If you need lengths tensor for OUTPUT, use Gather
.
Example: ITEMS = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] LENGTHS = [0, 2, 3, 1, 4] INDICES = [0, 2, 4] OUTPUT = [2, 3, 4, 6, 7, 8, 9]
Interface
Inputs | |
ITEMS |
items tensor |
LENGTHS |
lengths tensor |
INDICES |
indices into LENGTHS where items should be gathered |
Outputs | |
OUTPUT |
1-D tensor containing gathered items |
Code
caffe2/operators/utility_ops.cc
LengthsMean
Applies ‘Mean’ to each segment of the input tensor. Segments are defined by their LENGTHS.
LENGTHS is a vector that maps each of the first dimension slices of the DATA to a particular group (segment). Values belonging to the same segment are aggregated together.
For example LENGTHS = [2, 1] stands for segments DATA[0..1] and DATA[2] The first dimension of the output is equal to the number of input segments, i.e. len(LENGTHS)
. Other dimensions are inherited from the input tensor.
Mean computes the element-wise mean of the input slices. Operation doesn’t change the shape of the individual blocks.
Interface
Inputs | |
DATA |
Input tensor, slices of which are aggregated. |
LENGTHS |
Vector with the same sum of elements as the first dimension of DATA |
Outputs | |
OUTPUT |
Aggregated output tensor. Has the first dimension of len(LENGTHS) |
Code
caffe2/operators/segment_reduction_op.cc
LengthsMeanGradient
No documentation yet.
Code
caffe2/operators/segment_reduction_op.cc
LengthsPartition
LengthsPartition splits the input int tensor into multiple ones according to the second tensor. The first dimension is expected to be the tensor that describes lengths of the elements. Takes the second input and partitions it to shards according to the remainder of values modulo the number of partitions. It requires the second tensor to be a 1D-tensor of the integral type. The first tensor should be 1D-tensor of int32 that would represent the lengths of the elements in the input. The number of partitions is derived as (num_output / num_input). If additional inputs are present they must have the same shape as the first input, optionally with extra trailing dimensions. They will be partitioned accordingly to the first input. Optional arg ‘pack_first_input’ transforms the first tensor values as X_ij / num_partitions. Outputs are ordered as X_0_part_0, X_1_part_0, …, X_N-1_part_0, X_0_part_1, …, X_N-1_part_K-1
Interface
Arguments | |
pack_first_input |
(int, default 0) If set, the operator transforms the first tensor values as floor(X_ij / num_partitions) |
Inputs | |
input |
Input tensor containing data to be partitioned. The number of input tensors might be greater than 1 but must have the same shape as the previous tensors. |
Outputs | |
partitions |
Output Partitions. The number of output tensors has to be a multiple of the number of input tensors. |
Code
caffe2/operators/partition_ops.cc
LengthsRangeFill
Convert a length vector to a range sequene. For example, input=[4,3,1], the output would be [0,1,2,3,0,1,2,0].
Interface
Inputs | |
lengths |
1D tensor of int32 or int64 segment lengths. |
Outputs | |
range_sequence |
1D tensor whose size is the sum of lengths |
Code
LengthsSum
Applies ‘Sum’ to each segment of the input tensor. Segments are defined by their LENGTHS.
LENGTHS is a vector that maps each of the first dimension slices of the DATA to a particular group (segment). Values belonging to the same segment are aggregated together.
For example LENGTHS = [2, 1] stands for segments DATA[0..1] and DATA[2] The first dimension of the output is equal to the number of input segments, i.e. len(LENGTHS)
. Other dimensions are inherited from the input tensor.
Summation is done element-wise across slices of the input tensor and doesn’t change the shape of the individual blocks.
Interface
Inputs | |
DATA |
Input tensor, slices of which are aggregated. |
LENGTHS |
Vector with the same sum of elements as the first dimension of DATA |
Outputs | |
OUTPUT |
Aggregated output tensor. Has the first dimension of len(LENGTHS) |
Code
caffe2/operators/segment_reduction_op.cc
LengthsSumGradient
No documentation yet.
Code
caffe2/operators/segment_reduction_op.cc
LengthsTile
Given DATA tensor of rank r >= 1, and LENGTHS tensor of rank 1, duplicate each entry of the outer-most dimension of DATA according to LENGTHS, and concatenate them in an output tensor of rank r. Example:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | DATA = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], [6.8, 7.9], ] LENGTHS = [0, 1, 3, 2] OUTPUT = [ [2.3, 3.4], [4.5, 5.7], [4.5, 5.7], [4.5, 5.7], [6.8, 7.9], [6.8, 7.9], ] |
Interface
Inputs | |
DATA |
Tensor of rank r >= 1. First dimension must be equal to the size of lengths |
LENGTHS |
Tensor of int32 lengths of rank 1 |
Outputs | |
OUTPUT |
Tensor of rank r |
Code
caffe2/operators/lengths_tile_op.cc
LengthsToRanges
Given a vector of segment lengths, calculates offsets of each segment and packs them next to the lengths. For the input vector of length N the output is a Nx2 matrix with (offset, lengths) packaged for each segment.
For example, [1, 3, 0, 2]
transforms into [[0, 1], [1, 3], [4, 0], [4, 2]]
.
Interface
Inputs | |
lengths |
1D tensor of int32 segment lengths. |
Outputs | |
ranges |
2D tensor of shape len(lengths) X 2 and the same type as lengths |
Code
caffe2/operators/utility_ops.cc
LengthsToSegmentIds
Given a vector of segment lengths, returns a zero-based, consecutive vector of segment_ids. For example, [1, 3, 0, 2] will produce [0, 1, 1, 1, 3, 3]. In general, the inverse operation is SegmentIdsToLengths. Notice though that trailing empty sequence lengths can’t be properly recovered from segment ids.
Interface
Inputs | |
lengths |
1D tensor of int32 or int64 segment lengths. |
Outputs | |
segment_ids |
1D tensor of length sum(lengths) |
Code
caffe2/operators/utility_ops.cc
LengthsToShape
No documentation yet.
Code
caffe2/operators/utility_ops.cc
LengthsToWeights
Similar as LengthsToSegmentIds but output vector of segment weights derived by lengths. i.e 1/pow(length, power)
Interface
Arguments | |
power |
n of 1/pow(length,n) for normalization |
Inputs | |
lengths |
1-D int32_t or int64_t tensor of lengths |
Outputs | |
a vector of weights |
1-D float tensor of weights by length |
Code
caffe2/operators/utility_ops.cc
LengthsWeightedSum
Applies ‘WeightedSum’ to each segment of the input tensor. Segments are defined by their LENGTHS.
LENGTHS is a vector that maps each of the first dimension slices of the DATA to a particular group (segment). Values belonging to the same segment are aggregated together.
For example LENGTHS = [2, 1] stands for segments DATA[0..1] and DATA[2] The first dimension of the output is equal to the number of input segments, i.e. len(LENGTHS)
. Other dimensions are inherited from the input tensor.
Input slices are first scaled by SCALARS and then summed element-wise. It doesn’t change the shape of the individual blocks.
Interface
Arguments | |
grad_on_weights |
Produce also gradient for weights . For now it’s only supported in Lengths -based operators |
Inputs | |
DATA |
Input tensor for the summation |
SCALARS |
Scalar multipliers for the input slices. Must be a vector with the length matching the first dimension of DATA |
LENGTHS |
Vector with the same sum of elements as the first dimension of DATA |
Outputs | |
OUTPUT |
Aggregated output tensor. Has the first dimension of len(LENGTHS) |
Code
caffe2/operators/segment_reduction_op.cc
LengthsWeightedSumGradient
No documentation yet.
Code
caffe2/operators/segment_reduction_op.cc
LengthsWeightedSumWithMainInputGradient
No documentation yet.
Code
caffe2/operators/segment_reduction_op.cc
Load
The Load operator loads a set of serialized blobs from a db. It takes no input and [0, infinity) number of outputs, using the db keys to match the db entries with the outputs. If an input is passed, then it is assumed that that input blob is a DBReader to load from, and we ignore the db and db_type arguments.
Interface
Arguments | |
absolute_path |
(int, default 0) if set, use the db path directly and do not prepend the current root folder of the workspace. |
add_prefix |
(string, default=””) blobs will be prefixed with this when loading.Useful for avoiding collisions with blobs existing in the workspace.The output blob names specified to this op should include this prefix. |
strip_prefix |
(string, default=””) characters in the provided blob names that match strip_prefix will be removed prior to loading. Also, characters that precede strip_prefix will be removed. Useful for removing device scope from blob names. |
db |
(string) the path to the db to load. |
db_type |
(string) the type of the db. |
keep_device |
(int, default 0) if nonzero, the blobs are loaded into the device that is specified in the serialized BlobProto. Otherwise, the device will be set as the one that the Load operator is being run under. |
load_all |
(int, default 0) if nonzero, will load all blobs pointed to by the db to the workspace overwriting/creating blobs as needed. |
allow_incomplete |
(bool, default false) if true, will allow not loading all the output blobs specified in the outputs |
source_blob_names |
(list of strings) if set, used instead of output blob names, to specify which blobs in the db shall be loaded. Must be the same length as number of output blobs. |
Code
caffe2/operators/load_save_op.cc
Log
Calculates the natural log of the given input tensor, element-wise. This operation can be done in an in-place fashion too, by providing the same input and output blobs.
Interface
Inputs | |
input |
Input tensor |
Outputs | |
output |
The natural log of the input tensor computed element-wise |
Code
Logit
Elementwise logit transform: logit(x) = log(x / (1 - x)), where x is the input data clampped in (eps, 1-eps).
Interface
Arguments | |
eps (optional) |
small positive epsilon value, the default is 1e-6. |
Inputs | |
X |
input float tensor |
Y |
output float tensor |
Code
LongIndexCreate
Creates a dictionary that maps int64 keys to consecutive integers from 1 to max_elements. Zero is reserved for unknown keys.
Interface
Arguments | |
max_elements |
Max number of elements, including the zero entry. |
Outputs | |
handler |
Pointer to an Index instance. |
Code
LpPool
LpPool consumes an input blob X and applies L-p pooling across the the blob according to kernel sizes, stride sizes, and pad lengths defined by the ConvPoolOpBase operator. L-p pooling consisting of taking the L-p norm of a subset of the input tensor according to the kernel size and downsampling the data into the output blob Y for further processing.
Interface
Inputs | |
X |
Input data tensor from the previous operator; dimensions depend on whether the NCHW or NHWC operators are being used. For example, in the former, the input has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. The corresponding permutation of dimensions is used in the latter case. |
Outputs | |
Y |
Output data tensor from L-p pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. |
Code
caffe2/operators/lp_pool_op.cc
LpPoolGradient
No documentation yet.
Code
caffe2/operators/lp_pool_op.cc
MSRAFill
No documentation yet.
Code
MakeTwoClass
Given a vector of probabilities, this operator transforms this into a 2-column matrix with complimentary probabilities for binary classification. In explicit terms, given the vector X, the output Y is vstack(1 - X, X).
Interface
Inputs | |
X |
Input vector of probabilities |
Outputs | |
Y |
2-column matrix with complimentary probabilities of X for binary classification |
Code
caffe2/operators/cross_entropy_op.cc
MakeTwoClassGradient
No documentation yet.
Code
caffe2/operators/cross_entropy_op.cc
MarginRankingCriterion
MarginRankingCriterion takes two input data X1 (Tensor
Interface
Inputs | |
X1 |
The left input vector as a 1-dim TensorCPU. |
X2 |
The right input vector as a 1-dim TensorCPU. |
Y |
The label as a 1-dim TensorCPU with int value of 1 or -1. |
Outputs | |
loss |
The output loss with the same dimensionality as X1. |
Code
caffe2/operators/margin_ranking_criterion_op.cc
MarginRankingCriterionGradient
MarginRankingCriterionGradient takes both X1, X2, Y and dY and uses them to update dX1, and dX2 according to the chain rule and derivatives of the loss function.
Code
caffe2/operators/margin_ranking_criterion_op.cc
MatMul
Matrix multiplication Y = A * B, where A has size (M x K), B has size (K x N), and Y will have a size (M x N).
Interface
Arguments | |
trans_a |
Pass 1 to transpose A before multiplication |
trans_b |
Pass 1 to transpose B before multiplication |
Inputs | |
A |
2D matrix of size (M x K) |
B |
2D matrix of size (K x N) |
Outputs | |
Y |
2D matrix of size (M x N) |
Code
Max
Element-wise max of each of the input tensors. The first input tensor can be used in-place as the output tensor, in which case the max will be done in place and results will be accumulated in input0. All inputs and outputs must have the same shape and data type.
Interface
Inputs | |
data_0 |
First of the input tensors. Can be inplace. |
Outputs | |
max |
Output tensor. Same dimension as inputs. |
Code
caffe2/operators/utility_ops.cc
MaxGradient
No documentation yet.
Code
caffe2/operators/utility_ops.cc
MaxPool
MaxPool consumes an input blob X and applies max pooling across the the blob according to kernel sizes, stride sizes, and pad lengths defined by the ConvPoolOpBase operator. Max pooling consisting of taking the maximumvalue of a subset of the input tensor according to the kernel size and downsampling the data into the output blob Y for further processing.
Interface
Inputs | |
X |
Input data tensor from the previous operator; dimensions depend on whether the NCHW or NHWC operators are being used. For example, in the former, the input has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. The corresponding permutation of dimensions is used in the latter case. |
Outputs | |
Y |
Output data tensor from max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. |
Code
MaxPoolGradient
No documentation yet.
Code
caffe2/operators/pool_gradient_op.cc
MaxPoolWithIndex
1 2 3 4 5 | MaxPoolWithIndex consumes an input blob X and applies max pooling across the blob according to kernel sizes, stride sizes and pad lengths defined by the ConvPoolOpBase operator. It also produces an explicit mask that defines the location that all maximum values were found, which is re-used in the gradient pass. This op is deterministic. |
Interface
Inputs | |
X |
Input data tensor from the previous operator; dimensions depend on whether the NCHW or NHWC operators are being used. For example, in the former, the input has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. The corresponding permutation of dimensions is used in the latter case. |
Outputs | |
Y |
Output data tensor from average pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. |
Index |
Mask of location indices of the found maximum values, used in the gradient operator to accumulate dY values to the appropriate locations in Y |
Code
caffe2/operators/max_pool_with_index.cu
Mul
Performs element-wise binary multiplication (with limited broadcast support). If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of size 1 (a scalar value), or having its shape as a contiguous subset of the first tensor’s shape. The starting of the mutually equal shape is specified by the argument “axis”, and if it is not set, suffix matching is assumed. 1-dim expansion doesn’t work yet. For example, the following tensor shapes are supported (with broadcast=1):
1 2 3 4 5 6 | shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 |
Argument broadcast=1
needs to be passed to enable broadcasting.
Interface
Arguments | |
broadcast |
Pass 1 to enable broadcasting |
axis |
If set, defines the broadcast dimensions. See doc for details. |
Inputs | |
A |
First operand, should share the type with the second operand. |
B |
Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size. |
Outputs | |
C |
Result, has same dimensions and type as A |
Code
caffe2/operators/elementwise_op_schema.cc
MultiClassAccuracy
Respectively compute accuracy score for each class given a number of instances and predicted scores of each class for each instance.
Interface
Inputs | |
prediction |
2-D float tensor (N,D,) of predicted scores of each class for each data. N is the number of instances, i.e., batch size. D is number of possible classes/labels. |
labels |
1-D int tensor (N,) of labels for each instance. |
Outputs | |
accuracies |
1-D float tensor (D,) of accuracy for each class. If a class has no instance in the batch, its accuracy score is set to zero. |
amounts |
1-D int tensor (D,) of number of instances for each class in the batch. |
Code
caffe2/operators/multi_class_accuracy_op.cc
NCHW2NHWC
The operator switches the order of data in a tensor from NCHW- sample index N, channels C, height H and width W, to the NHWC order.
Interface
Inputs | |
data |
The input data (Tensor |
Outputs | |
output |
The output tensor (Tensor |
Code
caffe2/operators/order_switch_ops.cc
NHWC2NCHW
The operator switches the order of data in a tensor from NHWC- sample index N, height H, width H and channels C, to the NCHW order.
Interface
Inputs | |
data |
The input data (Tensor |
Outputs | |
output |
The output tensor (Tensor |
Code
caffe2/operators/order_switch_ops.cc
NanCheck
Identity operator, but checks all values for nan or inf
Interface
Inputs | |
tensor |
Tensor to check for nan/inf |
Outputs | |
output |
Tensor to copy input into if no NaNs or inf. Can be in-place |
Code
caffe2/operators/utility_ops.cc
Negative
Computes the element-wise negative of the input.
Interface
Inputs | |
X |
1D input tensor |
Outputs | |
Y |
1D input tensor |
Code
caffe2/operators/negative_op.cc
Normalize
Given a matrix, apply L2-normalization along the last dimension.
Code
caffe2/operators/normalize_op.cc
NormalizeGradient
No documentation yet.
Code
caffe2/operators/normalize_op.cc
Not
Performs element-wise negation.
Interface
Inputs | |
X |
Input tensor of type bool . |
Outputs | |
Y |
Output tensor of type bool . |
Code
caffe2/operators/elementwise_op_schema.cc
OneHot
Given a sequence of indices, one for each example in a batch, returns a matrix where each inner dimension has the size of the index and has 1.0 in the index active in the given example, and 0.0 everywhere else.
Interface
Inputs | |
indices |
The active index for each example in the batch. |
index_size_tensor |
Scalar with the size of the index. |
Outputs | |
one_hots |
Matrix of size len(indices) x index_size |
Code
caffe2/operators/one_hot_ops.cc
Or
Performs element-wise logical operation or
(with limited broadcast support).
Both input operands should be of type bool
.
If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of size 1 (a scalar value), or having its shape as a contiguous subset of the first tensor’s shape. The starting of the mutually equal shape is specified by the argument “axis”, and if it is not set, suffix matching is assumed. 1-dim expansion doesn’t work yet.
For example, the following tensor shapes are supported (with broadcast=1):
1 2 3 4 5 6 | shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 |
Argument broadcast=1
needs to be passed to enable broadcasting.
Interface
Arguments | |
broadcast |
Pass 1 to enable broadcasting |
axis |
If set, defines the broadcast dimensions. See doc for details. |
Inputs | |
A |
First operand. |
B |
Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size. |
Outputs | |
C |
Result, has same dimensions and A and type bool |
Code
caffe2/operators/elementwise_op_schema.cc
PRelu
PRelu takes input data (Tensor
Interface
Inputs | |
X |
1D input tensor |
Slope |
1D slope tensor. If Slope is of size 1, the value is sharedacross different channels |
Outputs | |
Y |
1D input tensor |
Code
PReluGradient
PReluGradient takes both Y and dY and uses this to update dX and dW according to the chain rule and derivatives of the rectified linear function.
Code
PackRecords
Given a dataset under a schema specified by the fields
argument will pack all the input tensors into one, where each tensor element represents a row of data (batch of size 1). This format allows easier use with the rest of Caffe2 operators.
Interface
Arguments | |
fields |
List of strings representing the string names in the formatspecified in the doc for CreateTreeCursor. |
Outputs | |
tensor |
One dimensional tensor having a complex type of SharedTensorVectorPtr. In order to reverse it back to the original input it has to be inserted into UnPackRecordsOp. |
Code
caffe2/operators/dataset_ops.cc
PackSegments
Map N dim tensor to N+1 dim based on length blob. Sequences that are shorter than the longest sequence are padded with zeros.
Interface
Arguments | |
pad_minf |
Padding number in the packed segments. Use true to pad -infinity, otherwise pad zeros |
Inputs | |
lengths |
1-d int/long tensor contains the length in each of the output. |
tensor |
N dim Tensor. |
Outputs | |
packed_tensor |
N + 1 dim Tesorwhere dim(1) is the max length, dim(0) is the batch size. |
Code
caffe2/operators/pack_segments.cc
PadEmptySamples
Pad empty field given lengths and index features, Input(0) is a blob pointing to the lengths of samples in one batch, [Input(1),… Input(num_fields)] a list of tensors containing the data for each field of the features. PadEmptySamples is thread safe.
Interface
Inputs | |
lengths |
A blob containing a pointer to the lengths. |
Outputs | |
out_lengths |
Tensor containing lengths with empty sample padded. |
Code
caffe2/operators/sequence_ops.cc
PadImage
PadImage pads values around the boundary of an image according to the pad values and stride sizes defined by the ConvPoolOpBase operator.
Interface
Inputs | |
X |
Input data tensor from the previous operator; dimensions depend on whether the NCHW or NHWC operators are being used. For example, in the former, the input has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. The corresponding permutation of dimensions is used in the latter case. |
Outputs | |
Y |
Output data tensor from padding the H and W dimensions on the tensor. Dimensions will vary based on various pad and stride sizes. |
Code
PadImageGradient
No documentation yet.
Code
PairWiseLoss
Operator computes the pair wise loss between all pairs within a batch using the logit loss function on the difference in scores between pairs
Interface
Inputs | |
X |
Input blob from the previous layer, which is almost always the result of a softmax operation; X is a 2D array of size N x 1where N is the batch size. For more info: D. Sculley, Large Scale Learning to Rank. https://www.eecs.tufts.edu/~dsculley/papers/large-scale-rank.pdf |
label |
Blob containing the labels used to compare the input |
Outputs | |
Y |
Output blob after the cross entropy computation |
Code
caffe2/operators/rank_loss_op.cc
PairWiseLossGradient
No documentation yet.
Code
caffe2/operators/rank_loss_op.cc
Partition
Splits the input int tensor into multiple ones according to the first tensor. Takes the first input and partitions it to shards according to the remainder of values modulo the number of partitions. It requires that the first tensor is of integral type. The number of partitions is derived as (num_output / num_input). If additional inputs are present they must have the same shape as the first input, optionally with extra trailing dimensions. They will be partitioned accordingly to the first input. Optional arg ‘pack_first_input’ transforms the first tensor values as X_ij / num_partitions. Outputs are ordered as X_0_part_0, X_1_part_0, …, X_N-1_part_0, X_0_part_1, …, X_N-1_part_K-1
Interface
Arguments | |
pack_first_input |
(int, default 0) If set, the operator transforms the first tensor values as floor(X_ij / num_partitions) |
Inputs | |
input |
Input tensor containing data to be partitioned. The number of input tensors might be greater than 1 but must have the same shape as the previous tensors. |
Outputs | |
partitions |
Output Partitions. The number of output tensors has to be a multiple of the number of input tensors. |
Code
caffe2/operators/partition_ops.cc
Perplexity
Perplexity calculates how well a probability distribution predicts a sample. Perplexity takes a 1-D tensor containing a batch of probabilities. Each value in the tensor belongs to a different sample and represents the probability of the model predicting the true label for that sample. The operator returns a single (float) perplexity value for the batch.
Interface
Inputs | |
probabilities |
The input data as Tensor. It contains a batch oftrue label or target probabilities |
Outputs | |
output |
The output- a single (float) perplexity value for the batch |
Code
caffe2/operators/perplexity_op.cc
PiecewiseLinearTransform
PiecewiseLinearTransform takes inputs – predictions, a 2-D or 1-D tensor (Tensor
- The transform parameters (bounds, slopes, intercepts) can be passed either through args or through input blobs.
- If we have multiple groups of piecewise linear functions, each group has the same number of pieces.
- If a prediction is out of the bounds, it is capped to the smallest or largest bound.
Interface
Arguments | |
bounds |
1-D vector of size (prediction_dimensions x (pieces+1)) contain the upper bounds of each piece of linear function. One special case is the first bound is the lower bound of whole piecewise function and we treat it the same as the left most functions. (bounds, slopes, intercepts) can passed through either arg or input blobs. |
slopes |
1-D vector of size (prediction_dimensions x pieces) containing the slopes of linear function |
intercepts |
1-D vector of size (prediction_dimensions x pieces) containing the intercepts of linear function |
binary |
If set true, we assume the input is a Nx1 or Nx2 tensor. If it is Nx1 tensor, it is positive predictions. If the input is Nx2 tensor, its first column is negative predictions and second column is positive and negative + positive = 1. We just need one group of piecewise linear functions for the positive predictions. |
Inputs | |
predictions |
2-D tensor (Tensor |
bounds (optional) |
See bounds in Arg. (bounds, slopes, intercepts) can passed through either arg or input blobs. |
slopes (optional) |
See slopes in Arg. (bounds, slopes, intercepts) can passed through either arg or input blobs. |
intercepts (optional) |
See intercepts in Arg. (bounds, slopes, intercepts) can passed through either arg or input blobs. |
Outputs | |
transforms |
2-D tensor (Tensor |
Code
caffe2/operators/piecewise_linear_transform_op.cc
Pow
Pow takes input data (Tensor
Interface
Arguments | |
exponent |
The exponent of the power function. |
Inputs | |
X |
Input tensor of any shape |
Outputs | |
Y |
Output tensor (same size as X) |
Code
Logs shape and contents of input tensor to stderr or to a file.
Interface
Arguments | |
to_file |
(bool) if 1, saves contents to the root folder of the current workspace, appending the tensor contents to a file named after the blob name. Otherwise, logs to stderr. |
Inputs | |
tensor |
The tensor to print. |
Code
caffe2/operators/utility_ops.cc
QPSMetric
QPSMetric operator syncronously updates metric storedcreate a blob that will store state that is required for computing QPSMetric. The only output of the operator will have blob with QPSMetricState as an output.
Interface
Inputs | |
QPS_METRIC_STATE |
Input Blob QPSMetricState, that needs to be updated |
INPUT_BATCH |
Input Blob containing a tensor with batch of the examples. First dimension of the batch will be used to get the number of examples in the batch. |
Outputs | |
output |
Blob with QPSMetricState |
Code
caffe2/operators/metrics_ops.cc
QPSMetricReport
QPSMetricReport operator that syncronously consumes the QPSMetricState blob and reports the information about QPS.
Interface
Outputs | |
output |
Blob with QPSMetricState |
Code
caffe2/operators/metrics_ops.cc
RangeFill
No documentation yet.
Code
ReadNextBatch
Read the next batch of examples out of the given cursor and data blobs. Input(0) is a blob pointing to a TreeCursor, and [Input(1),… Input(num_fields)] a list of tensors containing the data for each field of the dataset. ReadNextBatch is thread safe.
Interface
Arguments | |
batch_size |
Number of top-level entries to read. |
Inputs | |
cursor |
A blob containing a pointer to the cursor. |
dataset_field_0 |
First dataset field |
Outputs | |
field_0 |
Tensor containing the next batch for field 0. |
Code
caffe2/operators/dataset_ops.cc
ReadRandomBatch
Read the next batch of examples out of the given cursor, idx blob, offset matrix and data blobs. Input(0) is a blob pointing to a TreeCursor, Input(1) is a blob pointing to the shuffled idx Input(2) is a blob pointing to the offset matrix and [Input(3),… Input(num_fields)] a list of tensors containing the data for each field of the dataset. ReadRandomBatch is thread safe.
Interface
Arguments | |
batch_size |
Number of top-level entries to read. |
Inputs | |
cursor |
A blob containing a pointer to the cursor. |
idx |
idx with a shuffled order. |
offsetsmat |
offset matrix containing length offset info. |
dataset_field_0 |
First dataset field |
Outputs | |
field_0 |
Tensor containing the next batch for field 0. |
Code
caffe2/operators/dataset_ops.cc
ReceiveTensor
Receives the tensor from another node.
Interface
Arguments | |
src |
(int) he rank to receive the tensor from. |
tag |
(int) a tag to receive the tensor with. |
raw_buffer |
(bool) if set, only send the content and assume that the receiver has already known the tensor’s shape and information. |
Inputs | |
comm_world |
The common world. |
Y |
In-place output. If raw_buffer is specified, Y should have pre-allocated data and type.. |
src |
An int CPUtensor of size 1 specifying the rank. If given, this overrides the ‘from’ argument of the op. |
tag |
An int CPUtensor of size 1 specifying the tag to send the tensor with. This overrides the ‘tag’ argument of the op. |
Outputs | |
Y |
The received tensor. |
src |
The sender that sent the message as a CPUTensor of size 1 and of type int. |
tag |
The tag that the message is sent with as a CPUTensor of size 1 and of type int. |
Code
caffe2/operators/communicator_op.cc
RecurrentNetwork
Run the input network in a recurrent fashion. This can be used to implement fairly general recurrent neural networks (RNNs). The operator proceeds as follows.
- First, initialized the states from the input recurrent states - For each timestep T, apply the links (that map offsets from input/output
1 | tensors into the inputs/outputs for the `step` network) |
- Finally, alias the recurrent states to the specified output blobs. This is a fairly special-case meta-operator, and so the implementation is somewhat complex. It trades of generality (and frankly usability) against performance and control (compared to e.g. TF dynamic_rnn, Theano scan, etc). See the usage examples for a flavor of how to use it.
Code
caffe2/operators/recurrent_network_op.cc
RecurrentNetworkGradient
No documentation yet.
Code
caffe2/operators/recurrent_network_op.cc
Reduce
Does a reduce operation from every node to the root node. Currently only Sum is supported.
Interface
Arguments | |
root |
(int, default 0) the root to run reduce into. |
Inputs | |
comm_world |
The common world. |
X |
A tensor to be reduced. |
Outputs | |
Y |
The reduced result on root, not set for other nodes. |
Code
caffe2/operators/communicator_op.cc
ReduceBackMean
Reduces the input tensor along the last dimension of the input tensor by applying ‘Mean’. This op acts in a similar way to SortedSegmentMean and UnsortedSegmentMean but as if all input slices belong to a single segment. Mean computes the element-wise mean of the input slices. Operation doesn’t change the shape of the individual blocks.
Interface
Inputs | |
DATA |
Input tensor to be reduced on the first dimension |
Outputs | |
OUTPUT |
Aggregated tensor |
Code
caffe2/operators/segment_reduction_op.cc
ReduceBackMeanGradient
No documentation yet.
Code
caffe2/operators/segment_reduction_op.cc
ReduceBackSum
Reduces the input tensor along the last dimension of the input tensor by applying ‘Sum’. This op acts in a similar way to SortedSegmentSum and UnsortedSegmentSum but as if all input slices belong to a single segment. Summation is done element-wise across slices of the input tensor and doesn’t change the shape of the individual blocks.
Interface
Inputs | |
DATA |
Input tensor to be reduced on the first dimension |
Outputs | |
OUTPUT |
Aggregated tensor |
Code
caffe2/operators/segment_reduction_op.cc
ReduceBackSumGradient
No documentation yet.
Code
caffe2/operators/segment_reduction_op.cc
ReduceFrontMean
Reduces the input tensor along the first dimension of the input tensor by applying ‘Mean’. This op acts in a similar way to SortedSegmentMean and UnsortedSegmentMean but as if all input slices belong to a single segment. Mean computes the element-wise mean of the input slices. Operation doesn’t change the shape of the individual blocks.
Interface
Inputs | |
DATA |
Input tensor to be reduced on the first dimension |
Outputs | |
OUTPUT |
Aggregated tensor |
Code
caffe2/operators/segment_reduction_op.cc
ReduceFrontMeanGradient
No documentation yet.
Code
caffe2/operators/segment_reduction_op.cc
ReduceFrontSum
Reduces the input tensor along the first dimension of the input tensor by applying ‘Sum’. This op acts in a similar way to SortedSegmentSum and UnsortedSegmentSum but as if all input slices belong to a single segment. Summation is done element-wise across slices of the input tensor and doesn’t change the shape of the individual blocks.
Interface
Inputs | |
DATA |
Input tensor to be reduced on the first dimension |
Outputs | |
OUTPUT |
Aggregated tensor |
Code
caffe2/operators/segment_reduction_op.cc
ReduceFrontSumGradient
No documentation yet.
Code
caffe2/operators/segment_reduction_op.cc
ReduceFrontWeightedSum
Reduces the input tensor along the first dimension of the input tensor by applying ‘WeightedSum’. This op acts in a similar way to SortedSegmentWeightedSum and UnsortedSegmentWeightedSum but as if all input slices belong to a single segment. Input slices are first scaled by SCALARS and then summed element-wise. It doesn’t change the shape of the individual blocks.
Interface
Arguments | |
grad_on_weights |
Produce also gradient for weights . For now it’s only supported in Lengths -based operators |
Inputs | |
DATA |
Input tensor for the summation |
SCALARS |
Scalar multipliers for the input slices. Must be a vector with the length matching the first dimension of DATA |
Outputs | |
OUTPUT |
Aggregated tensor |
Code
caffe2/operators/segment_reduction_op.cc
ReduceFrontWeightedSumGradient
No documentation yet.
Code
caffe2/operators/segment_reduction_op.cc
ReduceTailSum
Reduce the tailing dimensions
Interface
Inputs | |
mat |
The matrix |
Outputs | |
output |
Output |
Code
Relu
Relu takes one input data (Tensor
Interface
Inputs | |
X |
1D input tensor |
Outputs | |
Y |
1D input tensor |
Code
ReluGradient
ReluGradient takes both Y and dY and uses this to update dX according to the chain rule and derivatives of the rectified linear function.
Code
RemoveDataBlocks
Shrink the data tensor by removing data blocks with given zero-based indices in the outermost dimension of the tensor. Indices are not assumed in any order or unique but with the range [0, blocks_size). Indices could be empty.
Interface
Inputs | |
data |
a N-D data tensor, N >= 1 |
indices |
zero-based indices of blocks to be removed |
Outputs | |
shrunk data |
data after removing data blocks indexed by ‘indices’ |
Code
caffe2/operators/remove_data_blocks_op.cc
RemovePadding
Remove padding around the edges of each segment of the input data. This is the reverse opration of AddPadding, and uses the same arguments and conventions for input and output data format.
Interface
Arguments | |
padding_width |
Outer-size of padding to remove around each range. |
end_padding_width |
(Optional) Specifies a different end-padding width. |
Inputs | |
data_in |
T<N, D1…, Dn> Input data |
lengths |
(i64) Num of elements in each range. sum(lengths) = N. If not provided, considers all data as a single segment. |
Outputs | |
data_out |
(T<N - 2*padding_width, D1…, Dn>) Unpadded data. |
lengths_out |
(i64, optional) Lengths for each unpadded range. |
Code
caffe2/operators/sequence_ops.cc
ReplaceNaN
Replace the NaN (not a number) element in the input tensor with argument value
Interface
Arguments | |
value (optional) |
the value to replace NaN, the default is 0 |
Inputs | |
input |
Input tensor |
output |
Output tensor |
Code
caffe2/operators/replace_nan_op.cc
ResetCounter
Resets a count-down counter with initial value specified by the ‘init_count’ argument.
Interface
Arguments | |
init_count |
Resets counter to this value, must be >= 0. |
Inputs | |
counter |
A blob pointing to an instance of a new counter. |
Outputs | |
previous_value |
(optional) Previous value of the counter. |
Code
caffe2/operators/counter_ops.cc
ResetCursor
Resets the offsets for the given TreeCursor. This operation is thread safe.
Interface
Inputs | |
cursor |
A blob containing a pointer to the cursor. |
Code
caffe2/operators/dataset_ops.cc
Reshape
Reshape the input tensor similar to numpy.reshape.
It takes a tensor as input and an optional tensor specifying the new shape.
When the second input is absent, an extra argument shape
must be specified.
It outputs the reshaped tensor as well as the original shape.
At most one dimension of the new shape can be -1. In this case, the value is inferred from the size of the tensor and the remaining dimensions. A dimension could also be 0, in which case the actual dimension value is going to be copied from the input tensor.
Interface
Arguments | |
shape |
New shape |
Inputs | |
data |
An input tensor. |
new_shape |
New shape. |
Outputs | |
reshaped |
Reshaped data. |
old_shape |
Original shape. |
Code
caffe2/operators/reshape_op.cc
ResizeLike
Produces tensor containing data of first input and shape of second input.
Interface
Inputs | |
data |
Tensor whose data will be copied into the output. |
shape_tensor |
Tensor whose shape will be applied to output. |
Outputs | |
output |
Tensor with data of input 0 and shape of input 1. |
Code
caffe2/operators/utility_ops.cc
ResizeNearest
1 2 3 4 5 | Resizes the spatial dimensions of the input using nearest neighbor interpolation. The `width_scale` and `height_scale` arguments control the size of the output, which is given by: output_width = floor(input_width * width_scale) output_height = floor(output_height * height_scale) |
Interface
Arguments | |
width_scale |
Scale along width dimension |
height_scale |
Scale along height dimension |
Inputs | |
X |
1D input tensor |
Outputs | |
Y |
1D input tensor |
Code
RetrieveCount
Retrieve the current value from the counter.
Interface
Inputs | |
counter |
A blob pointing to an instance of a counter. |
Outputs | |
count |
current count value. |
Code
caffe2/operators/counter_ops.cc
ReversePackedSegs
Reverse segments in a 3-D tensor (lengths, segments, embeddings,), leaving paddings unchanged. This operator is used to reverse input of a recurrent neural network to make it a BRNN.
Interface
Inputs | |
data |
a 3-D (lengths, segments, embeddings,) tensor. |
lengths |
length of each segment. |
Outputs | |
reversed data |
a (lengths, segments, embeddings,) tensor with each segment reversedand paddings unchanged. |
Code
caffe2/operators/reverse_packed_segs_op.cc
RoIPool
Carries out ROI Pooling for Faster-RCNN. Depending on the mode, there are multiple output cases:
1 2 | Output case #1: Y, argmaxes (train mode) Output case #2: Y (test mode) |
Interface
Arguments | |
is_test |
If set, run in test mode and skip computation of argmaxes (used for gradient computation). Only one output tensor is produced. (Default: false). |
order |
A StorageOrder string (Default: “NCHW”). |
pooled_h |
The pooled output height (Default: 1). |
pooled_w |
The pooled output width (Default: 1). |
spatial_scale |
Multiplicative spatial scale factor to translate ROI coords from their input scale to the scale used when pooling (Default: 1.0). |
Inputs | |
X |
The input 4-D tensor of data. Only NCHW order is currently supported. |
rois |
RoIs (Regions of Interest) to pool over. Should be a 2-D tensor of shape (num_rois, 5) given as [[batch_id, x1, y1, x2, y2], …]. |
Outputs | |
Y |
RoI pooled output 4-D tensor of shape (num_rois, channels, pooled_h, pooled_w). |
argmaxes |
Argmaxes corresponding to indices in X used for gradient computation. Only output if arg “is_test” is false. |
Code
caffe2/operators/roi_pool_op.cc
RoIPoolGradient
No documentation yet.
Code
caffe2/operators/roi_pool_op.cc
RowMul
Given a matrix A and column vector w, the output is the multiplication of row i of A and element i of w, e.g. C[i][j] = A[i][j] * w[i]. This operator should be deprecated when the gradient operator of Mul with broadcast is implemented.
Interface
Inputs | |
mat |
The matrix |
w |
The column vector |
Outputs | |
output |
Output |
Code
Save
The Save operator saves a set of blobs to a db. It takes [1, infinity) number of inputs and has no output. The contents of the inputs are written into the db specified by the arguments.
Interface
Arguments | |
absolute_path |
(int, default 0) if set, use the db path directly and do not prepend the current root folder of the workspace. |
strip_prefix |
(string, default=””) characters in the provided blob names that match strip_prefix will be removed prior to saving. Also, characters that precede strip_prefix will be removed. Useful for removing device scope from blob names. |
blob_name_overrides |
(list of strings) if set, used instead of original blob names. Must be the same length as number of blobs. |
db |
(string) the path to the db to load. |
db_type |
(string) the type of the db. |
Code
caffe2/operators/load_save_op.cc
Scale
Scale takes one input data (Tensor
Interface
Arguments | |
scale |
(float, default 1.0) the scale to apply. |
Code
ScatterAssign
Update slices of the tensor in-place by overriding current value. Note: The op pretty much ignores the exact shapes of the input arguments and cares only about sizes. It’s done for performance consideration to avoid unnecessary reshapes. Only first dimension of X_0 is important, let’s call it N. If M is the total size of X_0 and K is the size of INDICES then X_i is assumed to be of shape K x (M / N) regardless of the real shape. Note: Each update in INDICES is applied independently which means that if duplicated elements are present in INDICES arbitrary one will win. Currently only works on CPU because of access to INDICES.
Interface
Inputs | |
DATA |
Tensor to be updated. |
INDICES |
1-D list of indices on the first dimensionof X_0 that need to be updated |
SLICES |
Update slices, with shape len(INDICES) + shape(X_0)[1:] |
Outputs | |
DATA |
Has to be exactly the same tensor as the input 0 |
Code
caffe2/operators/utility_ops.cc
ScatterWeightedSum
Similar to WeightedSum, computes the weighted sum of several tensors, with the difference that inputs are sliced tensors. The first tensor has to be in-place and only slices of it on the first dimension as indexed by INDICES will be updated. Note: The op pretty much ignores the exact shapes of the input arguments and cares only about sizes. It’s done for performance consideration to avoid unnecessary reshapes. Only first dimension of X_0 is important, let’s call it N. If M is the total size of X_0 and K is the size of INDICES then X_i is assumed to be of shape K x (M / N) regardless of the real shape. Note: Each update in INDICES is applied independently which means that if duplicated elements are present in INDICES the corresponding slice of X_0 will be scaled multiple times. Manual collapsing of INDICES is required beforehand if necessary. Note: Updates are applied sequentially by inputs which might have undesired consequences if the input tensor is accessed concurrently by different op (e.g. when doing Hogwild). Other threads might see intermediate results even on individual slice level, e.g. X_0 scaled by weight_0 but without any updates applied. Currently only works on CPU because of access to INDICES.
Interface
Inputs | |
X_0 |
Tensor to be updated. |
Weight_0 |
Scalar weight for X_0, applied only to slices affected. |
INDICES |
1-D list of indices on the first dimension of X_0 that need to be updated |
X_1 |
Update slices, with shape len(INDICES) + shape(X_0)[1:] |
Weight_1 |
Scalar weight for X_1 update |
Outputs | |
X_0 |
Has to be exactly the same tensor as the input 0 |
Code
caffe2/operators/utility_ops.cc
SegmentIdsToLengths
Transfers a vector of segment ids to a vector of segment lengths. This operation supports non-consecutive segment ids. Segments not appearing in the input vector will have length 0. If the second input is provided, the number of segments = the size of its first dimension. Otherwise, the number of segments = the last index in the first input vector + 1. In general, for consecutive, zero-based segment IDs, this is the inverse operation of LengthsToSegmentIds, except that a vector of segment IDs cannot represent empty segments at the end (if the second input is absent).
Interface
Inputs | |
segment_ids |
1-D int32_t or int64_t tensor of segment ids |
data (optional) |
if provided, number of segments = the size of its first dimension |
Outputs | |
lengths |
1-D int64_t tensor of segment lengths |
Code
caffe2/operators/utility_ops.cc
SegmentIdsToRanges
Transfers a vector of segment ids to a vector of segment ranges. This operation supports non-consecutive segment ids. Segments not appearing in the input vector will have length 0. If the second input is provided, the number of segments = the size of its first dimension. Otherwise, the number of segments = the last index in the first input vector + 1.
Interface
Inputs | |
segment_ids |
1-D int32_t or int64_t tensor of segment ids |
data (optional) |
if provided, number of segments = the size of its first dimension |
Outputs | |
lengths |
1-D int64_t tensor of segment lengths |
Code
caffe2/operators/utility_ops.cc
SegmentOneHot
Given a sequence of indices, segmented by the lengths tensor, returns a matrix that has the elements in each sequence set to 1.0, and 0.0 everywhere else.
Interface
Inputs | |
lengths |
Size of each segment. |
indices |
Active indices, of size sum(lengths) |
index_size_tensor |
Size of the index |
Outputs | |
one_hots |
Matrix of size len(lengths) x index_size |
Code
caffe2/operators/one_hot_ops.cc
SendTensor
Sends the tensor to another node.
Interface
Arguments | |
dst |
The rank to send the tensor to. |
tag |
(int) a tag to send the tensor with. |
raw_buffer |
(bool) if set, only send the content and assume that the receiver has already known the tensor’s shape and information. |
Inputs | |
comm_world |
The common world. |
X |
A tensor to be allgathered. |
dst |
An int CPUtensor of size 1 specifying the rank. If given, this overrides the ‘to’ argument of the op. |
tag |
An int CPUtensor of size 1 specifying the tag to send the tensor with. This overrides the ‘tag’ argument of the op. |
Code
caffe2/operators/communicator_op.cc
Shape
Produce a 1D int64 tensor with the shape of the input tensor.
Code
caffe2/operators/utility_ops.cc
Sigmoid
Sigmoid takes one input data (Tensor
Interface
Inputs | |
X |
1D input tensor |
Outputs | |
Y |
1D output tensor |
Code
caffe2/operators/sigmoid_op.cc
SigmoidCrossEntropyWithLogits
Given two matrices logits and targets, of same shape, (batch_size, num_classes), computes the sigmoid cross entropy between the two. Returns a tensor of shape (batch_size,) of losses for each example.
Interface
Inputs | |
logits |
matrix of logits for each example and class. |
targets |
matrix of targets, same shape as logits. |
Outputs | |
xentropy |
Vector with the total xentropy for each example. |
Code
caffe2/operators/cross_entropy_op.cc
SigmoidCrossEntropyWithLogitsGradient
No documentation yet.
Code
caffe2/operators/cross_entropy_op.cc
SigmoidGradient
SigmoidGradient takes both Y and dY and uses this to update dX according to the chain rule and derivatives of the sigmoid function.
Code
caffe2/operators/sigmoid_op.cc
Slice
Produces a slice of the input tensor. Currently, only slicing in a single dimension is supported.
Slices are passed as 2 1D vectors with starting and end indices for each dimension of the input data
tensor. End indices are non-inclusive. If a negative value is passed for any of the start or end indices, it represent number of elements before the end of that dimension.
Example:
1 2 3 4 5 6 7 8 9 10 11 | data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] starts = [0, 1] ends = [-1, 3] result = [ [2, 3], [6, 7], ] |
Interface
Inputs | |
data |
Tensor of data to extract slices from. |
starts |
1D tensor: start-indices for each dimension of data. |
ends |
1D tensor: end-indices for each dimension of data. |
Outputs | |
output |
Sliced data tensor. |
Code
caffe2/operators/utility_ops.cc
Softmax
The operator computes the softmax normalized values for each layer in the batch of the given input. The input is a 2-D tensor (Tensor
Interface
Inputs | |
input |
The input data as 2-D Tensor |
Outputs | |
output |
The softmax normalized output values with the same shape as input tensor. |
Code
caffe2/operators/softmax_op.cc
SoftmaxGradient
No documentation yet.
Code
caffe2/operators/softmax_op.cc
SoftmaxWithLoss
Combined Softmax and Cross-Entropy loss operator.
The operator computes the softmax normalized values for each layer in the batch of the given input, after which cross-entropy loss is computed. This operator is numerically more stable than separate Softmax and CrossEntropy ops.
The inputs are a 2-D tensor (Tensor
1 | Currently does not handle spatial=1 case. |
Optional third input blob can be used to weight the samples for the loss. For the spatial version, weighting is by x,y position of the input.
Interface
Inputs | |
logits |
Unscaled log probabilities |
labels |
Ground truth |
weight_tensor |
Optional blob to be used to weight the samples for the loss. With spatial set, weighting is by x,y of the input |
Outputs | |
softmax |
Tensor with softmax cross entropy loss |
loss |
Average loss |
Code
caffe2/operators/softmax_with_loss_op.cc
SoftmaxWithLossGradient
No documentation yet.
Code
caffe2/operators/softmax_with_loss_op.cc
Softplus
Softplus takes one input data (Tensor
Interface
Inputs | |
X |
1D input tensor |
Outputs | |
Y |
1D input tensor |
Code
caffe2/operators/softplus_op.cc
SoftplusGradient
No documentation yet.
Code
caffe2/operators/softplus_op.cc
Softsign
Calculates the softsign (x/1+ | x | ) of the given input tensor element-wise. This operation can be done in an in-place fashion too, by providing the same input and output blobs. |
Interface
Inputs | |||
input |
1-D input tensor | ||
Outputs | |||
output |
The softsign (x/1+ | x | ) values of the input tensor computed element-wise |
Code
caffe2/operators/softsign_op.cc
SoftsignGradient
Calculates the softsign gradient (sgn(x)/(1+ | x | )^2) of the given input tensor element-wise. |
Interface
Inputs | |||
input |
1-D input tensor | ||
input |
1-D input tensor | ||
Outputs | |||
output |
The softsign gradient (sgn(x)/(1+ | x | )^2) values of the input tensor computed element-wise |
Code
caffe2/operators/softsign_op.cc
SortAndShuffle
Compute the sorted indices given a field index to sort by and break the sorted indices into chunks of shuffle_size * batch_size and shuffle each chunk, finally we shuffle between batches. If sort_by_field_idx is -1 we skip sort. For example, we have data sorted as 1,2,3,4,5,6,7,8,9,10,11,12 and batchSize = 2 and shuffleSize = 3, when we shuffle we get: [3,1,4,6,5,2] [12,10,11,8,9,7] After this we will shuffle among different batches with size 2 [3,1],[4,6],[5,2],[12,10],[11,8],[9,7] We may end up with something like [9,7],[5,2],[12,10],[4,6],[3,1],[11,8] Input(0) is a blob pointing to a TreeCursor, and [Input(1),… Input(num_fields)] a list of tensors containing the data for each field of the dataset. SortAndShuffle is thread safe.
Interface
Inputs | |
cursor |
A blob containing a pointer to the cursor. |
dataset_field_0 |
First dataset field |
Outputs | |
indices |
Tensor containing sorted indices. |
Code
caffe2/operators/dataset_ops.cc
SortedSegmentMean
Applies ‘Mean’ to each segment of input tensor. Segments need to be sorted and contiguous. See also UnsortedSegmentMean that doesn’t have this requirement.
SEGMENT_IDS is a vector that maps each of the first dimension slices of the DATA to a particular group (segment). Values belonging to the same segment are aggregated together.
The first dimension of the output is equal to the number of input segments, i.e. SEGMENT_IDS[-1]+1
. Other dimensions are inherited from the input tensor.
Mean computes the element-wise mean of the input slices. Operation doesn’t change the shape of the individual blocks.
Interface
Inputs | |
DATA |
Input tensor, slices of which are aggregated. |
SEGMENT_IDS |
Vector with the same length as the first dimension of DATA and values in the range 0..K-1 and in increasing order that maps each slice of DATA to one of the segments |
Outputs | |
OUTPUT |
Aggregated output tensor. Has the first dimension of K (the number of segments). |
Code
caffe2/operators/segment_reduction_op.cc
SortedSegmentMeanGradient
No documentation yet.
Code
caffe2/operators/segment_reduction_op.cc
SortedSegmentRangeLogMeanExp
Applies ‘LogMeanExp’ to each segment of input tensor. In order to allow for more efficient implementation of ‘LogMeanExp’, the input segments have to be contiguous and non-empty.
SEGMENT_IDS is a vector that maps each of the first dimension slices of the DATA to a particular group (segment). Values belonging to the same segment are aggregated together.
The first dimension of the output is equal to the number of input segments, i.e. SEGMENT_IDS[-1]+1
. Other dimensions are inherited from the input tensor.
LogMeanExp computes the element-wise log of the mean of exponentials of input slices. Operation doesn’t change the shape of individual blocks.
Interface
Inputs | |
DATA |
Input tensor to be aggregated |
SEGMENT_IDS |
Vector with the same length as the first dimension of DATA and values in the range 0..K-1 and in increasing order that maps each slice of DATA to one of the segments |
Outputs | |
OUTPUT |
Aggregated tensor with the first dimension of K and the other dimentsions inherited from DATA |
Code
caffe2/operators/segment_reduction_op.cc
SortedSegmentRangeLogMeanExpGradient
No documentation yet.
Code
caffe2/operators/segment_reduction_op.cc
SortedSegmentRangeLogSumExp
Applies ‘LogSumExp’ to each segment of input tensor. In order to allow for more efficient implementation of ‘LogSumExp’, the input segments have to be contiguous and non-empty.
SEGMENT_IDS is a vector that maps each of the first dimension slices of the DATA to a particular group (segment). Values belonging to the same segment are aggregated together.
The first dimension of the output is equal to the number of input segments, i.e. SEGMENT_IDS[-1]+1
. Other dimensions are inherited from the input tensor.
LogSumExp computes the element-wise log of the sum of exponentials of input slices. Operation doesn’t change the shape of individual blocks.
Interface
Inputs | |
DATA |
Input tensor to be aggregated |
SEGMENT_IDS |
Vector with the same length as the first dimension of DATA and values in the range 0..K-1 and in increasing order that maps each slice of DATA to one of the segments |
Outputs | |
OUTPUT |
Aggregated tensor with the first dimension of K and the other dimentsions inherited from DATA |
Code
caffe2/operators/segment_reduction_op.cc
SortedSegmentRangeLogSumExpGradient
No documentation yet.
Code
caffe2/operators/segment_reduction_op.cc
SortedSegmentRangeMax
Applies ‘Max’ to each segment of input tensor. In order to allow for more efficient implementation of ‘Max’, the input segments have to be contiguous and non-empty.
SEGMENT_IDS is a vector that maps each of the first dimension slices of the DATA to a particular group (segment). Values belonging to the same segment are aggregated together.
The first dimension of the output is equal to the number of input segments, i.e. SEGMENT_IDS[-1]+1
. Other dimensions are inherited from the input tensor.
Max computation is done element-wise, so that each element of the output slice corresponds to the max value of the respective elements in the input slices. Operation doesn’t change the shape of individual blocks. This implementation imitates torch nn.Max operator. If the maximum value occurs more than once, the operator will return the first occurence of value. When computing the gradient using the backward propagation, the gradient input corresponding to the first occurence of the maximum value will be used.
Interface
Inputs | |
DATA |
Input tensor to be aggregated |
SEGMENT_IDS |
Vector with the same length as the first dimension of DATA and values in the range 0..K-1 and in increasing order that maps each slice of DATA to one of the segments |
Outputs | |
OUTPUT |
Aggregated tensor with the first dimension of K and the other dimentsions inherited from DATA |
Code
caffe2/operators/segment_reduction_op.cc
SortedSegmentRangeMaxGradient
No documentation yet.
Code
caffe2/operators/segment_reduction_op.cc
SortedSegmentRangeMean
Applies ‘Mean’ to each segment of input tensor. In order to allow for more efficient implementation of ‘Mean’, the input segments have to be contiguous and non-empty.
SEGMENT_IDS is a vector that maps each of the first dimension slices of the DATA to a particular group (segment). Values belonging to the same segment are aggregated together.
The first dimension of the output is equal to the number of input segments, i.e. SEGMENT_IDS[-1]+1
. Other dimensions are inherited from the input tensor.
Mean computation is done element-wise, so that each element of the output slice corresponds to the average value of the respective elements in the input slices. Operation doesn’t change the shape of individual blocks.
Interface
Inputs | |
DATA |
Input tensor to be aggregated |
SEGMENT_IDS |
Vector with the same length as the first dimension of DATA and values in the range 0..K-1 and in increasing order that maps each slice of DATA to one of the segments |
Outputs | |
OUTPUT |
Aggregated tensor with the first dimension of K and the other dimentsions inherited from DATA |
Code
caffe2/operators/segment_reduction_op.cc
SortedSegmentRangeMeanGradient
No documentation yet.
Code
caffe2/operators/segment_reduction_op.cc
SortedSegmentRangeSum
Applies ‘Sum’ to each segment of input tensor. In order to allow for more efficient implementation of ‘Sum’, the input segments have to be contiguous and non-empty.
SEGMENT_IDS is a vector that maps each of the first dimension slices of the DATA to a particular group (segment). Values belonging to the same segment are aggregated together.
The first dimension of the output is equal to the number of input segments, i.e. SEGMENT_IDS[-1]+1
. Other dimensions are inherited from the input tensor.
Summation is done element-wise across slices of the input tensor and doesn’t change the shape of the individual blocks.
Interface
Inputs | |
DATA |
Input tensor to be aggregated |
SEGMENT_IDS |
Vector with the same length as the first dimension of DATA and values in the range 0..K-1 and in increasing order that maps each slice of DATA to one of the segments |
Outputs | |
OUTPUT |
Aggregated tensor with the first dimension of K and the other dimentsions inherited from DATA |
Code
caffe2/operators/segment_reduction_op.cc
SortedSegmentRangeSumGradient
No documentation yet.
Code
caffe2/operators/segment_reduction_op.cc
SortedSegmentSum
Applies ‘Sum’ to each segment of input tensor. Segments need to be sorted and contiguous. See also UnsortedSegmentSum that doesn’t have this requirement.
SEGMENT_IDS is a vector that maps each of the first dimension slices of the DATA to a particular group (segment). Values belonging to the same segment are aggregated together.
The first dimension of the output is equal to the number of input segments, i.e. SEGMENT_IDS[-1]+1
. Other dimensions are inherited from the input tensor.
Summation is done element-wise across slices of the input tensor and doesn’t change the shape of the individual blocks.
Interface
Inputs | |
DATA |
Input tensor, slices of which are aggregated. |
SEGMENT_IDS |
Vector with the same length as the first dimension of DATA and values in the range 0..K-1 and in increasing order that maps each slice of DATA to one of the segments |
Outputs | |
OUTPUT |
Aggregated output tensor. Has the first dimension of K (the number of segments). |
Code
caffe2/operators/segment_reduction_op.cc
SortedSegmentSumGradient
No documentation yet.
Code
caffe2/operators/segment_reduction_op.cc
SortedSegmentWeightedSum
Applies ‘WeightedSum’ to each segment of input tensor. Segments need to be sorted and contiguous. See also UnsortedSegmentWeightedSum that doesn’t have this requirement.
SEGMENT_IDS is a vector that maps each of the first dimension slices of the DATA to a particular group (segment). Values belonging to the same segment are aggregated together.
The first dimension of the output is equal to the number of input segments, i.e. SEGMENT_IDS[-1]+1
. Other dimensions are inherited from the input tensor.
Input slices are first scaled by SCALARS and then summed element-wise. It doesn’t change the shape of the individual blocks.
Interface
Arguments | |
grad_on_weights |
Produce also gradient for weights . For now it’s only supported in Lengths -based operators |
Inputs | |
DATA |
Input tensor for the summation |
SCALARS |
Scalar multipliers for the input slices. Must be a vector with the length matching the first dimension of DATA |
SEGMENT_IDS |
Vector with the same length as the first dimension of DATA and values in the range 0..K-1 and in increasing order that maps each slice of DATA to one of the segments |
Outputs | |
OUTPUT |
Aggregated output tensor. Has the first dimension of K (the number of segments). |
Code
caffe2/operators/segment_reduction_op.cc
SortedSegmentWeightedSumGradient
No documentation yet.
Code
caffe2/operators/segment_reduction_op.cc
SpaceToBatch
SpaceToBatch for 4-D tensors of type T. Zero-pads and then rearranges (permutes) blocks of spatial data into batch. More specifically, this op outputs a copy of the input tensor where values from the height and width dimensions are moved to the batch dimension. After the zero-padding, both height and width of the input must be divisible by the block size.
Code
caffe2/operators/space_batch_op.cc
SparseLengthsMean
Pulls in slices of the input tensor, groups them into segments and applies ‘Mean’ to each segment. Segments are defined by their LENGTHS.
This op is basically Gather and LengthsMean fused together.
INDICES should contain integers in range 0..N-1 where N is the first dimension of DATA. INDICES represent which slices of DATA need to be pulled in.
LENGTHS is a vector that defines slice sizes by first dimention of DATA. Values belonging to the same segment are aggregated together. sum(LENGTHS) has to match INDICES size.
The first dimension of the output is equal to the number of input segment, i.e. len(LENGTHS)
. Other dimensions are inherited from the input tensor.
Mean computes the element-wise mean of the input slices. Operation doesn’t change the shape of the individual blocks.
Interface
Inputs | |
DATA |
Input tensor, slices of which are aggregated. |
INDICES |
Integer vector containing indices of the first dimension of DATA for the slices that are being aggregated |
LENGTHS |
Non negative vector with sum of elements equal to INDICES length |
Outputs | |
OUTPUT |
Aggregated output tensor. Has the first dimension of K (the number of segments). |
Code
caffe2/operators/segment_reduction_op.cc
SparseLengthsMeanGradient
No documentation yet.
Code
caffe2/operators/segment_reduction_op.cc
SparseLengthsSum
Pulls in slices of the input tensor, groups them into segments and applies ‘Sum’ to each segment. Segments are defined by their LENGTHS.
This op is basically Gather and LengthsSum fused together.
INDICES should contain integers in range 0..N-1 where N is the first dimension of DATA. INDICES represent which slices of DATA need to be pulled in.
LENGTHS is a vector that defines slice sizes by first dimention of DATA. Values belonging to the same segment are aggregated together. sum(LENGTHS) has to match INDICES size.
The first dimension of the output is equal to the number of input segment, i.e. len(LENGTHS)
. Other dimensions are inherited from the input tensor.
Summation is done element-wise across slices of the input tensor and doesn’t change the shape of the individual blocks.
Interface
Inputs | |
DATA |
Input tensor, slices of which are aggregated. |
INDICES |
Integer vector containing indices of the first dimension of DATA for the slices that are being aggregated |
LENGTHS |
Non negative vector with sum of elements equal to INDICES length |
Outputs | |
OUTPUT |
Aggregated output tensor. Has the first dimension of K (the number of segments). |
Code
caffe2/operators/segment_reduction_op.cc
SparseLengthsSumGradient
No documentation yet.
Code
caffe2/operators/segment_reduction_op.cc
SparseLengthsWeightedSum
Pulls in slices of the input tensor, groups them into segments and applies ‘WeightedSum’ to each segment. Segments are defined by their LENGTHS.
This op is basically Gather and LengthsWeightedSum fused together.
INDICES should contain integers in range 0..N-1 where N is the first dimension of DATA. INDICES represent which slices of DATA need to be pulled in.
LENGTHS is a vector that defines slice sizes by first dimention of DATA. Values belonging to the same segment are aggregated together. sum(LENGTHS) has to match INDICES size.
The first dimension of the output is equal to the number of input segment, i.e. len(LENGTHS)
. Other dimensions are inherited from the input tensor.
Input slices are first scaled by SCALARS and then summed element-wise. It doesn’t change the shape of the individual blocks.
Interface
Arguments | |
grad_on_weights |
Produce also gradient for weights . For now it’s only supported in Lengths -based operators |
Inputs | |
DATA |
Input tensor for the summation |
SCALARS |
Scalar multipliers for the input slices. Must be a vector with the length matching the first dimension of DATA |
INDICES |
Integer vector containing indices of the first dimension of DATA for the slices that are being aggregated |
LENGTHS |
Non negative vector with sum of elements equal to INDICES length |
Outputs | |
OUTPUT |
Aggregated output tensor. Has the first dimension of K (the number of segments). |
Code
caffe2/operators/segment_reduction_op.cc
SparseLengthsWeightedSumGradient
No documentation yet.
Code
caffe2/operators/segment_reduction_op.cc
SparseLengthsWeightedSumWithMainInputGradient
No documentation yet.
Code
caffe2/operators/segment_reduction_op.cc
SparseSortedSegmentMean
Pulls in slices of the input tensor, groups them into segments and applies ‘Mean’ to each segment. Segments need to be sorted and contiguous. See also SparseUnsortedSegmentMean that doesn’t have this requirement.
This op is basically Gather and SortedSegmentMean fused together.
INDICES should contain integers in range 0..N-1 where N is the first dimension of DATA. INDICES represent which slices of DATA need to be pulled in.
SEGMENT_IDS is a vector that maps each referenced slice of the DATA to a particular group (segment). Values belonging to the same segment are aggregated together. SEGMENT_IDS should have the same dimension as INDICES.
The first dimension of the output is equal to the number of input segments, i.e. SEGMENT_IDS[-1]+1
. Other dimensions are inherited from the input tensor.
Mean computes the element-wise mean of the input slices. Operation doesn’t change the shape of the individual blocks.
Interface
Inputs | |
DATA |
Input tensor, slices of which are aggregated. |
INDICES |
Integer vector containing indices of the first dimension of DATA for the slices that are being aggregated |
SEGMENT_IDS |
Vector with the same length as INDICES and values in the range 0..K-1 and in increasing order that maps each slice of DATA referenced by INDICES to one of the segments |
Outputs | |
OUTPUT |
Aggregated output tensor. Has the first dimension of K (the number of segments). |
Code
caffe2/operators/segment_reduction_op.cc
SparseSortedSegmentMeanGradient
No documentation yet.
Code
caffe2/operators/segment_reduction_op.cc
SparseSortedSegmentSum
Pulls in slices of the input tensor, groups them into segments and applies ‘Sum’ to each segment. Segments need to be sorted and contiguous. See also SparseUnsortedSegmentSum that doesn’t have this requirement.
This op is basically Gather and SortedSegmentSum fused together.
INDICES should contain integers in range 0..N-1 where N is the first dimension of DATA. INDICES represent which slices of DATA need to be pulled in.
SEGMENT_IDS is a vector that maps each referenced slice of the DATA to a particular group (segment). Values belonging to the same segment are aggregated together. SEGMENT_IDS should have the same dimension as INDICES.
The first dimension of the output is equal to the number of input segments, i.e. SEGMENT_IDS[-1]+1
. Other dimensions are inherited from the input tensor.
Summation is done element-wise across slices of the input tensor and doesn’t change the shape of the individual blocks.
Interface
Inputs | |
DATA |
Input tensor, slices of which are aggregated. |
INDICES |
Integer vector containing indices of the first dimension of DATA for the slices that are being aggregated |
SEGMENT_IDS |
Vector with the same length as INDICES and values in the range 0..K-1 and in increasing order that maps each slice of DATA referenced by INDICES to one of the segments |
Outputs | |
OUTPUT |
Aggregated output tensor. Has the first dimension of K (the number of segments). |
Code
caffe2/operators/segment_reduction_op.cc
SparseSortedSegmentSumGradient
No documentation yet.
Code
caffe2/operators/segment_reduction_op.cc
SparseSortedSegmentWeightedSum
Pulls in slices of the input tensor, groups them into segments and applies ‘WeightedSum’ to each segment. Segments need to be sorted and contiguous. See also SparseUnsortedSegmentWeightedSum that doesn’t have this requirement.
This op is basically Gather and SortedSegmentWeightedSum fused together.
INDICES should contain integers in range 0..N-1 where N is the first dimension of DATA. INDICES represent which slices of DATA need to be pulled in.
SEGMENT_IDS is a vector that maps each referenced slice of the DATA to a particular group (segment). Values belonging to the same segment are aggregated together. SEGMENT_IDS should have the same dimension as INDICES.
The first dimension of the output is equal to the number of input segments, i.e. SEGMENT_IDS[-1]+1
. Other dimensions are inherited from the input tensor.
Input slices are first scaled by SCALARS and then summed element-wise. It doesn’t change the shape of the individual blocks.
Interface
Arguments | |
grad_on_weights |
Produce also gradient for weights . For now it’s only supported in Lengths -based operators |
Inputs | |
DATA |
Input tensor for the summation |
SCALARS |
Scalar multipliers for the input slices. Must be a vector with the length matching the first dimension of DATA |
INDICES |
Integer vector containing indices of the first dimension of DATA for the slices that are being aggregated |
SEGMENT_IDS |
Vector with the same length as INDICES and values in the range 0..K-1 and in increasing order that maps each slice of DATA referenced by INDICES to one of the segments |
Outputs | |
OUTPUT |
Aggregated output tensor. Has the first dimension of K (the number of segments). |
Code
caffe2/operators/segment_reduction_op.cc
SparseSortedSegmentWeightedSumGradient
No documentation yet.
Code
caffe2/operators/segment_reduction_op.cc
SparseToDense
Convert sparse representations to dense with given indices.
Transforms a sparse representation of map<id, value> represented as indices
vector and values
tensor into a compacted tensor where the first dimension is determined by the first dimension of the 3rd input if it is given or the max index. Missing values are filled with zeros.
The op supports duplicated indices and performs summation over corresponding values. This behavior is useful for converting GradientSlices into dense representation.
After running this op: ``` output[indices[i], :] += values[i]
1 | # sum over all indices[i] equal to the index |
output[j, …] = 0 if j not in indices ```
Interface
Inputs | |
indices |
1-D int32/int64 tensor of concatenated ids of data |
values |
Data tensor, first dimension has to match indices , basic numeric types are supported |
data_to_infer_dim |
Optional: if provided, the first dimension of output is the first dimension of this tensor. |
Outputs | |
output |
Output tensor of the same type as values of shape [len(lengths), len(mask)] + shape(default_value) (if lengths is not provided the first dimension is omitted) |
Code
caffe2/operators/sparse_to_dense_op.cc
SparseToDenseMask
Convert sparse representations to dense with given indices.
Transforms a sparse representation of map<id, value> represented as indices
vector and values
tensor into a compacted tensor where the first dimension corresponds to each id provided in mask argument. Missing values are filled with the value of default_value
. After running this op: output[j, :] = values[i] # where mask[j] == indices[i] output[j, ...] = default_value # when mask[j] doesn't appear in indices
If lengths
is provided and not empty, and extra “batch” dimension is prepended to the output.
values
and default_value
can have additional matching dimensions, operation is performed on the entire subtensor in thise case.
For example, if lengths
is supplied and values
is 1-D vector of floats and default_value
is a float scalar, the output is going to be a float matrix of size len(lengths) X len(mask)
Interface
Arguments | |
mask |
list(int) argument with desired ids on the ‘dense’ output dimension |
return_presence_mask |
bool whether to return presence mask, false by default |
Inputs | |
indices |
1-D int32/int64 tensor of concatenated ids of data |
values |
Data tensor, first dimension has to match indices |
default_value |
Default value for the output if the id is not present in indices . Must have the same type as values and the same shape, but without the first dimension |
lengths |
Optional lengths to represent a batch of indices and values . |
Outputs | |
output |
Output tensor of the same type as values of shape [len(lengths), len(mask)] + shape(default_value) (if lengths is not provided the first dimension is omitted) |
presence_mask |
Bool tensor of shape [len(lengths), len(mask)] (if lengths is not provided the first dimension is omitted). True when a value for given id was present, false otherwise. |
Code
caffe2/operators/sparse_to_dense_mask_op.cc
SparseUnsortedSegmentMean
Pulls in slices of the input tensor, groups them into segments and applies ‘Mean’ to each segment. Segments ids can appear in arbitrary order (unlike in SparseSortedSegmentMean).
This op is basically Gather and UnsortedSegmentMean fused together.
INDICES should contain integers in range 0..N-1 where N is the first dimension of DATA. INDICES represent which slices of DATA need to be pulled in.
SEGMENT_IDS is a vector that maps each referenced slice of the DATA to a particular group (segment). Values belonging to the same segment are aggregated together. SEGMENT_IDS should have the same dimension as INDICES.
If num_segments
argument is passed it would be used as a first dimension for the output. Otherwise, it’d be dynamically calculated from as the max value of SEGMENT_IDS plus one. Other output dimensions are inherited from the input tensor.
Mean computes the element-wise mean of the input slices. Operation doesn’t change the shape of the individual blocks.
Interface
Inputs | |
DATA |
Input tensor, slices of which are aggregated. |
INDICES |
Integer vector containing indices of the first dimension of DATA for the slices that are being aggregated |
SEGMENT_IDS |
Integer vector with the same length as INDICES that maps each slice of DATA referenced by INDICES to one of the segments |
Outputs | |
OUTPUT |
Aggregated output tensor. Has the first dimension of equal to the number of segments. |
Code
caffe2/operators/segment_reduction_op.cc
SparseUnsortedSegmentMeanGradient
No documentation yet.
Code
caffe2/operators/segment_reduction_op.cc
SparseUnsortedSegmentSum
Pulls in slices of the input tensor, groups them into segments and applies ‘Sum’ to each segment. Segments ids can appear in arbitrary order (unlike in SparseSortedSegmentSum).
This op is basically Gather and UnsortedSegmentSum fused together.
INDICES should contain integers in range 0..N-1 where N is the first dimension of DATA. INDICES represent which slices of DATA need to be pulled in.
SEGMENT_IDS is a vector that maps each referenced slice of the DATA to a particular group (segment). Values belonging to the same segment are aggregated together. SEGMENT_IDS should have the same dimension as INDICES.
If num_segments
argument is passed it would be used as a first dimension for the output. Otherwise, it’d be dynamically calculated from as the max value of SEGMENT_IDS plus one. Other output dimensions are inherited from the input tensor.
Summation is done element-wise across slices of the input tensor and doesn’t change the shape of the individual blocks.
Interface
Inputs | |
DATA |
Input tensor, slices of which are aggregated. |
INDICES |
Integer vector containing indices of the first dimension of DATA for the slices that are being aggregated |
SEGMENT_IDS |
Integer vector with the same length as INDICES that maps each slice of DATA referenced by INDICES to one of the segments |
Outputs | |
OUTPUT |
Aggregated output tensor. Has the first dimension of equal to the number of segments. |
Code
caffe2/operators/segment_reduction_op.cc
SparseUnsortedSegmentSumGradient
No documentation yet.
Code
caffe2/operators/segment_reduction_op.cc
SparseUnsortedSegmentWeightedSum
Pulls in slices of the input tensor, groups them into segments and applies ‘WeightedSum’ to each segment. Segments ids can appear in arbitrary order (unlike in SparseSortedSegmentWeightedSum).
This op is basically Gather and UnsortedSegmentWeightedSum fused together.
INDICES should contain integers in range 0..N-1 where N is the first dimension of DATA. INDICES represent which slices of DATA need to be pulled in.
SEGMENT_IDS is a vector that maps each referenced slice of the DATA to a particular group (segment). Values belonging to the same segment are aggregated together. SEGMENT_IDS should have the same dimension as INDICES.
If num_segments
argument is passed it would be used as a first dimension for the output. Otherwise, it’d be dynamically calculated from as the max value of SEGMENT_IDS plus one. Other output dimensions are inherited from the input tensor.
Input slices are first scaled by SCALARS and then summed element-wise. It doesn’t change the shape of the individual blocks.
Interface
Arguments | |
grad_on_weights |
Produce also gradient for weights . For now it’s only supported in Lengths -based operators |
Inputs | |
DATA |
Input tensor for the summation |
SCALARS |
Scalar multipliers for the input slices. Must be a vector with the length matching the first dimension of DATA |
INDICES |
Integer vector containing indices of the first dimension of DATA for the slices that are being aggregated |
SEGMENT_IDS |
Integer vector with the same length as INDICES that maps each slice of DATA referenced by INDICES to one of the segments |
Outputs | |
OUTPUT |
Aggregated output tensor. Has the first dimension of equal to the number of segments. |
Code
caffe2/operators/segment_reduction_op.cc
SparseUnsortedSegmentWeightedSumGradient
No documentation yet.
Code
caffe2/operators/segment_reduction_op.cc
SpatialBN
Carries out spatial batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, there are multiple cases for the number of outputs, which we list below: Output case #1: Y, mean, var, saved_mean, saved_var
1 | (training mode) |
Output case #2: Y (test mode)
Interface
Arguments | |
is_test |
If set to nonzero, run spatial batch normalization in test mode. |
epsilon |
The epsilon value to use to avoid division by zero. |
order |
A StorageOrder string. |
Inputs | |
X |
The input 4-dimensional tensor of shape NCHW or NHWC depending on the order parameter. |
scale |
The scale as a 1-dimensional tensor of size C to be applied to the output. |
bias |
The bias as a 1-dimensional tensor of size C to be applied to the output. |
mean |
The running mean (training) or the estimated mean (testing) as a 1-dimensional tensor of size C. |
var |
The running variance (training) or the estimated variance (testing) as a 1-dimensional tensor of size C. |
Outputs | |
Y |
The output 4-dimensional tensor of the same shape as X. |
mean |
The running mean after the spatial BN operator. Must be in-place with the input mean. Should not be used for testing. |
var |
The running variance after the spatial BN operator. Must be in-place with the input var. Should not be used for testing. |
saved_mean |
Saved mean used during training to speed up gradient computation. Should not be used for testing. |
saved_var |
Saved variance used during training to speed up gradient computation. Should not be used for testing. |
Code
caffe2/operators/spatial_batch_norm_op.cc
SpatialBNGradient
No documentation yet.
Code
caffe2/operators/spatial_batch_norm_gradient_op.cc
Split
Split a tensor into a list of tensors, along the specified
1 2 3 | 'axis'. The lengths of the split can be specified using argument 'axis' or optional second input blob to the operator. Otherwise, the tensor is split to equal sized parts. |
Interface
Arguments | |
axis |
Which axis to split on |
split |
length of each output |
order |
Either NHWC or NCWH, will split on C axis |
Inputs | |
input |
The tensor to split |
split |
Optional list of output lengths (see also arg ‘split’) |
Code
caffe2/operators/concat_split_op.cc
Sqr
Square (x^2) the elements of the input
Interface
Inputs | |
input |
Input tensor |
Outputs | |
output |
Squared elements of the input |
Code
SquareRootDivide
Given DATA tensor with first dimention N and SCALE vector of the same size N produces an output tensor with same dimensions as DATA. Which consists of DATA slices. i-th slice is divided by sqrt(SCALE[i]) elementwise. If SCALE[i] == 0 output slice is identical to the input one (no scaling) Example:
1 2 3 4 5 6 7 8 9 10 11 12 | Data = [ [1.0, 2.0], [3.0, 4.0] ] SCALE = [4, 9] OUTPUT = [ [2.0, 4.0], [9.0, 12.0] ] |
Code
caffe2/operators/square_root_divide_op.cc
SquaredL2Distance
1 2 | Given two input float tensors X, Y, and produces one output float tensor of the L2 difference between X and Y that is computed as ||(X - Y)^2 / 2||. |
Interface
Inputs | |
X |
1D input tensor |
Outputs | |
Y |
1D input tensor |
Code
caffe2/operators/distance_op.cc
SquaredL2DistanceGradient
No documentation yet.
Code
caffe2/operators/distance_op.cc
Squeeze
Remove single-dimensional entries from the shape of a tensor. Takes a
1 | parameter `dims` with a list of dimension to squeeze. |
If the same blob is provided in input and output, the operation is copy-free.
This is the exact inverse operation of ExpandDims given the same dims
arg.
Interface
Inputs | |
data |
Tensors with at least max(dims) dimensions. |
Outputs | |
squeezed |
Reshaped tensor with same data as input. |
Code
caffe2/operators/utility_ops.cc
StatRegistryCreate
Create a StatRegistry object that will contain a map of performance counters keyed by name. A StatRegistry is used to gather and retrieve performance counts throuhgout the caffe2 codebase.
Interface
Outputs | |
handle |
A Blob pointing to the newly created StatRegistry. |
Code
StatRegistryExport
No documentation yet.
Interface
Arguments | |
reset |
(default true) Whether to atomically reset the counters afterwards. |
Inputs | |
handle |
If provided, export values from given StatRegistry.Otherwise, export values from the global singleton StatRegistry. |
Outputs | |
keys |
1D string tensor with exported key names |
values |
1D int64 tensor with exported values |
timestamps |
The unix timestamp at counter retrieval. |
Code
StatRegistryUpdate
Update the given StatRegistry, or the global StatRegistry, with the values of counters for the given keys.
Interface
Inputs | |
keys |
1D string tensor with the key names to update. |
values |
1D int64 tensor with the values to update. |
handle |
If provided, update the given StatRegistry. Otherwise, update the global singleton. |
Code
StopGradient
StopGradient is a helper operator that does no actual numerical computation, and in the gradient computation phase stops the gradient from being computed through it.
Code
caffe2/operators/stop_gradient.cc
StringEndsWith
Performs the ends-with check on each string in the input tensor. Returns tensor of boolean of the same dimension of input.
Interface
Arguments | |
suffix |
The suffix to check input strings against. |
Inputs | |
strings |
Tensor of std::string. |
Outputs | |
bools |
Tensor of bools of same shape as input. |
Code
caffe2/operators/string_ops.cc
StringIndexCreate
Creates a dictionary that maps string keys to consecutive integers from 1 to max_elements. Zero is reserved for unknown keys.
Interface
Arguments | |
max_elements |
Max number of elements, including the zero entry. |
Outputs | |
handle |
Pointer to an Index instance. |
Code
StringJoin
Takes a 1-D or a 2-D tensor as input and joins elements in each row with the provided delimieter. Output is a 1-D tensor of size equal to the first dimension of the input. Each element in the output tensor is a string of concatenated elements corresponding to each row in the input tensor. For 1-D input, each element is treated as a row.
Interface
Arguments | |
delimiter |
Delimiter for join (Default: “,”). |
Inputs | |
input |
1-D or 2-D tensor |
Outputs | |
strings |
1-D tensor of strings created by joining row elements from the input tensor. |
Code
caffe2/operators/string_ops.cc
StringPrefix
Computes the element-wise string prefix of the string tensor. Input strings that are shorter than prefix length will be returned unchanged. NOTE: Prefix is computed on number of bytes, which may lead to wrong behavior and potentially invalid strings for variable-length encodings such as utf-8.
Interface
Arguments | |
length |
Maximum size of the prefix, in bytes. |
Inputs | |
strings |
Tensor of std::string. |
Outputs | |
prefixes |
Tensor of std::string containing prefixes for each input. |
Code
caffe2/operators/string_ops.cc
StringStartsWith
Performs the starts-with check on each string in the input tensor. Returns tensor of boolean of the same dimension of input.
Interface
Arguments | |
prefix |
The prefix to check input strings against. |
Inputs | |
strings |
Tensor of std::string. |
Outputs | |
bools |
Tensor of bools of same shape as input. |
Code
caffe2/operators/string_ops.cc
StringSuffix
Computes the element-wise string suffix of the string tensor. Input strings that are shorter than suffix length will be returned unchanged. NOTE: Prefix is computed on number of bytes, which may lead to wrong behavior and potentially invalid strings for variable-length encodings such as utf-8.
Interface
Arguments | |
length |
Maximum size of the suffix, in bytes. |
Inputs | |
strings |
Tensor of std::string. |
Outputs | |
suffixes |
Tensor of std::string containing suffixes for each output. |
Code
caffe2/operators/string_ops.cc
Sub
Performs element-wise binary subtraction (with limited broadcast support). If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of size 1 (a scalar value), or having its shape as a contiguous subset of the first tensor’s shape. The starting of the mutually equal shape is specified by the argument “axis”, and if it is not set, suffix matching is assumed. 1-dim expansion doesn’t work yet. For example, the following tensor shapes are supported (with broadcast=1):
1 2 3 4 5 6 | shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 |
Argument broadcast=1
needs to be passed to enable broadcasting.
Interface
Arguments | |
broadcast |
Pass 1 to enable broadcasting |
axis |
If set, defines the broadcast dimensions. See doc for details. |
Inputs | |
A |
First operand, should share the type with the second operand. |
B |
Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size. |
Outputs | |
C |
Result, has same dimensions and type as A |
Code
caffe2/operators/elementwise_op_schema.cc
Sum
Element-wise sum of each of the input tensors. The first input tensor can be used in-place as the output tensor, in which case the sum will be done in place and results will be accumulated in input0. All inputs and outputs must have the same shape and data type.
Interface
Inputs | |
data_0 |
First of the input tensors. Can be inplace. |
Outputs | |
sum |
Output tensor. Same dimension as inputs. |
Code
caffe2/operators/utility_ops.cc
SumElements
Sums the elements of the input tensor.
Interface
Arguments | |
average |
whether to average or not |
Inputs | |
X |
Tensor to sum up |
Outputs | |
sum |
Scalar sum |
Code
caffe2/operators/reduction_ops.cc
SumElementsGradient
No documentation yet.
Code
caffe2/operators/reduction_ops.cc
SumInt
No documentation yet.
Code
caffe2/operators/utility_ops.cc
SumReduceLike
SumReduceLike operator takes 2 tensors as input. It performs reduce sum to the first input so that the output looks like the second one. It assumes that the first input has more dimensions than the second, and the dimensions of the second input is the contiguous subset of the dimensions of the first. For example, the following tensor shapes are supported:
1 2 3 4 | shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 2, 5), shape(B) = (2), with axis=0 |
Interface
Arguments | |
axis |
If set, defines the starting dimension for reduction. Args axis and axis_str cannot be used simultaneously. |
axis_str |
If set, it could only be N or C or H or W. order arg should also be provided. It defines the reduction dimensions on NCHW or NHWC. Args axis and axis_str cannot be used simultaneously. |
order |
Either NHWC or HCWH |
Inputs | |
A |
First operand, should share the type with the second operand. |
B |
Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size. |
Outputs | |
C |
Result, has same dimensions and type as B |
Code
caffe2/operators/elementwise_op_schema.cc
SumSqrElements
Sums the squares elements of the input tensor.
Interface
Arguments | |
average |
whether to average or not |
Inputs | |
X |
Tensor to sum up |
Outputs | |
sum |
Scalar sum of squares |
Code
caffe2/operators/reduction_ops.cc
Summarize
Summarize computes four statistics of the input tensor (Tensor
Interface
Arguments | |
to_file |
(int, default 0) flag to indicate if the summarized statistics have to be written to a log file. |
Inputs | |
data |
The input data as Tensor |
Outputs | |
output |
1-D tensor (Tensor |
Code
caffe2/operators/summarize_op.cc
TT
The TT-layer serves as a low-rank decomposition of a fully connected layer. The inputs are the same as to a fully connected layer, but the number of parameters are greatly reduced and forward computation time can be drastically reduced especially for layers with large weight matrices. The multiplication is computed as a product of the input vector with each of the cores that make up the TT layer. Given the input sizes (inp_sizes), output sizes(out_sizes), and the ranks of each of the cores (tt_ranks), the ith core will have size:
1 2 | inp_sizes[i] * tt_ranks[i] * tt_ranks[i + 1] * out_sizes[i]. |
The complexity of the computation is dictated by the sizes of inp_sizes, out_sizes, and tt_ranks, where there is the trade off between accuracy of the low-rank decomposition and the speed of the computation.
Interface
Arguments | |
inp_sizes |
(int[]) Input sizes of cores. Indicates the input size of the individual cores; the size of the input vector X must match the product of the inp_sizes array. |
out_sizes |
(int[]) Output sizes of cores. Indicates the output size of the individual cores; the size of the output vector Y must match the product of the out_sizes array. |
tt_ranks |
(int[]) Ranks of cores. Indicates the ranks of the individual cores; lower rank means larger compression, faster computation but reduce accuracy. |
Inputs | |
X |
Input tensor from previous layer with size (M x K), where M is the batch size and K is the input size. |
b |
1D blob containing the bias vector |
cores |
1D blob containing each individual cores with sizes specified above. |
Outputs | |
Y |
Output tensor from previous layer with size (M x N), where M is the batch size and N is the output size. |
Code
caffe2/operators/tt_linear_op.cc
Tanh
Calculates the hyperbolic tangent of the given input tensor element-wise. This operation can be done in an in-place fashion too, by providing the same input and output blobs.
Interface
Inputs | |
input |
1-D input tensor |
Outputs | |
output |
The hyperbolic tangent values of the input tensor computed element-wise |
Code
TanhGradient
No documentation yet.
Code
TensorProtosDBInput
TensorProtosDBInput is a simple input operator that basically reads things from a db where each key-value pair stores an index as key, and a TensorProtos object as value. These TensorProtos objects should have the same size, and they will be grouped into batches of the given size. The DB Reader is provided as input to the operator and it returns as many output tensors as the size of the TensorProtos object. Each output will simply be a tensor containing a batch of data with size specified by the ‘batch_size’ argument containing data from the corresponding index in the TensorProtos objects in the DB.
Interface
Arguments | |
batch_size |
(int, default 0) the number of samples in a batch. The default value of 0 means that the operator will attempt to insert the entire data in a single output blob. |
Inputs | |
data |
A pre-initialized DB reader. Typically, this is obtained by calling CreateDB operator with a db_name and a db_type. The resulting output blob is a DB Reader tensor |
Outputs | |
output |
The output tensor in which the batches of data are returned. The number of output tensors is equal to the size of (number of TensorProto’s in) the TensorProtos objects stored in the DB as values. Each output tensor will be of size specified by the ‘batch_size’ argument of the operator |
Code
caffe2/operators/tensor_protos_db_input.cc
TensorVectorSize
Get the size of the input vector
Interface
Inputs | |
tensor vector |
std::unique_ptr<std::vector |
Outputs | |
size |
int32_t size |
Code
caffe2/operators/dataset_ops.cc
TextFileReaderRead
Read a batch of rows from the given text file reader instance. Expects the number of fields to be equal to the number of outputs. Each output is a 1D tensor containing the values for the given field for each row. When end of file is reached, returns empty tensors.
Interface
Arguments | |
batch_size |
Maximum number of rows to read. |
Inputs | |
handler |
Pointer to an existing TextFileReaderInstance. |
Code
caffe2/operators/text_file_reader.cc
Tile
Constructs a tensor by tiling a given tensor along a specified axis. This operation creates a new tensor by replicating the input tensor ‘tiles’ times along dimension ‘axis’. The output tensor’s ‘axis’th dimension has input.dims(axis) * tiles elements, and the values of input are replicated ‘tiles’ times along the ‘axis’th dimension. For example, tiling [[a b c d]] by tile=2, axis=0 produces [[a b c d], [a b c d]].
Interface
Arguments | |
tiles |
Number of replicas |
axis |
Axis to replicate along |
Inputs | |
data |
The input tensor. |
tiles |
(optional) Number of replicas (overrides argument) |
axis |
(optional) Axis to replicate along (overrides argument) |
Outputs | |
tiled_data |
Tensor that will contain input replicated along the given axis. |
Code
TileGradient
No documentation yet.
Code
TimerBegin
Start a wallclock timer, returning a pointer to it. The timer is stopped by calling TimerEnd
Interface
Arguments | |
counter_name |
Name of the timer. If not provided, use output name. |
Outputs | |
timer |
Pointer to timer, to be passed to TimerEnd. |
Code
TimerEnd
Stop a timer started with TimerBegin, publishing a CAFFE_EVENT
Interface
Inputs | |
timer |
Pointer to timer, obtained from TimerBegin. |
Code
TopK
Retrieve the top-K elements for the last dimension. Given an input tensor of shape [a_1, a_2, …, a_n, r] and integer argument k, return two outputs: -Value tensor of shape [a_1, a_2, …, a_n, k] which contains the values of the top k elements along the last dimension -Index tensor of shape [a_1, a_2, …, a_n, k] which contains the indices of the top k elements (original indices from the input tensor). Given two equivalent values, this operator uses the indices along the last dim- ension as a tiebreaker. That is, the element with the lower index will appear first.
Interface
Arguments | |
k |
Number of top elements to retrieve |
Inputs | |
X |
Tensor of shape [a_1, a_2, …, a_n, r] |
Outputs | |
Values |
Tensor of shape [a_1, a_2, …, a_n, k] containing top K values from the input tensor |
Indices |
Tensor of shape [a_1, a_2, …, a_n, k] containing the corresponding input tensor indices for the top K values. |
Code
Transpose
Transpose the input tensor similar to numpy.transpose. For example, when axes=(1, 0, 2), given an input tensor of shape (1, 2, 3), the output shape will be (2, 1, 3).
Interface
Arguments | |
axes |
A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given. |
Inputs | |
data |
An input tensor. |
Outputs | |
transposed |
Transposed output. |
Code
caffe2/operators/transpose_op.cc
UnPackRecords
Given a packed dataset (packed by the PackRecordsOp) and the fields
argument describing the datasets schema returns the original dataset format. Number of returned tensors is equal to the number of fields in the fields
argument.
The first input is the packed tensor to be unpacked. Optionally, you can provide prototype tensors to give the expected shapes of the output tensors. This is helpful when you expected to unpack empty tensor, e.g., output of a sapmling process.
Interface
Arguments | |
fields |
List of strings representing the string names in the formatspecified in the doc for CreateTreeCursor. |
Inputs | |
packed_tensor |
The tensor to be unpacked |
Code
caffe2/operators/dataset_ops.cc
UniformFill
Fill the output tensor with FLOAT samples from uniform distribution [min, max]. The range can be defined either by arguments or input blobs. If the range is given by input blobs, you also need to give the shape as input. When the range is given as arguments, this operator enforces min <= max. When the range is given as inputs, the constraint is not enforced. When MAX < MIN, the first dimension of the output is set to 0. This behavior is allowed so that dynamically sampling indices into a dynamically sized tensor is possible. The shape of the output can be given as argument or input.
Interface
Arguments | |
min |
minimum value, inclusive |
max |
maximum value, inclusive |
shape |
shape of the output, do not set when input_as_shape=1 |
input_as_shape |
set to 1 to use the first input as shape |
Inputs | |
SHAPE |
1-D tensor of the shape of the output, must be used with input_as_shape |
MIN |
scalar blob of mininum value |
MAX |
scalar blob of maximum value |
Outputs | |
OUTPUT |
output tensor |
Code
UniformIntFill
Like UniformFill
but fill with INT32.
Code
Unique
Deduplicates input indices vector and optionally produces reverse remapping. There’s no guarantees on the ordering of the output indices.
Interface
Inputs | |
indices |
1D tensor of int32 or int64 indices. |
Outputs | |
unique_indices |
1D tensor of deduped entries. |
Code
caffe2/operators/utility_ops.cc
UniqueUniformFill
Fill the output tensor with uniform samples between min and max (inclusive). If the second input is given, its elements will be excluded from uniform sampling. Using the second input will require you to provide shape via the first input.
Interface
Arguments | |
min |
Minimum value, inclusive |
max |
Maximum value, inclusive |
dtype |
The data type for the elements of the output tensor.Strictly must be one of the types from DataType enum in TensorProto.This only supports INT32 and INT64 now. If not set, assume INT32 |
shape |
The shape of the output tensor.Cannot set the shape argument and pass in an input at the same time. |
extra_shape |
The additional dimensions appended at the end of the shape indicatedby the input blob. Cannot set the extra_shape argument when there is no input blob. |
input_as_shape |
1D tensor containing the desired output shape |
Inputs | |
input |
Input tensor to provide shape information |
avoid |
(optional) Avoid elements in this tensor. Elements must be unique. |
Outputs | |
output |
Output tensor of unique uniform samples |
Code
UnpackSegments
Map N+1 dim tensor to N dim based on length blob
Interface
Inputs | |
lengths |
1-d int/long tensor contains the length in each of the input. |
tensor |
N+1 dim Tensor. |
Outputs | |
packed_tensor |
N dim Tesor |
Code
caffe2/operators/pack_segments.cc
UnsafeCoalesce
Coalesce the N inputs into N outputs and a single coalesced output blob. This allows operations that operate over multiple small kernels (e.g. biases in a deep CNN) to be coalesced into a single larger operation, amortizing the kernel launch overhead, synchronization costs for distributed computation, etc. The operator: - computes the total size of the coalesced blob by summing the input sizes - allocates the coalesced output blob as the total size - copies the input vectors into the coalesced blob, at the correct offset.
- aliases each Output(i) to- point into the coalesced blob, at the
1 2 | corresponding offset for Input(i). |
This is ‘unsafe’ as the output vectors are aliased, so use with caution.
Code
caffe2/operators/utility_ops.cc
UnsortedSegmentMean
Applies ‘Mean’ to each segment of input tensor. Segments ids can appear in arbitrary order (unlike in SortedSegmentMean).
SEGMENT_IDS is a vector that maps each of the first dimension slices of the DATA to a particular group (segment). Values belonging to the same segment are aggregated together.
If num_segments
argument is passed it would be used as a first dimension for the output. Otherwise, it’d be dynamically calculated from as the max value of SEGMENT_IDS plus one. Other output dimensions are inherited from the input tensor.
Mean computes the element-wise mean of the input slices. Operation doesn’t change the shape of the individual blocks.
Interface
Arguments | |
num_segments |
Optional int argument specifying the number of output segments and thus the first dimension of the output |
Inputs | |
DATA |
Input tensor, slices of which are aggregated. |
SEGMENT_IDS |
Integer vector with the same length as the first dimension of DATA that maps each slice of DATA to one of the segments |
Outputs | |
OUTPUT |
Aggregated output tensor. Has the first dimension of equal to the number of segments. |
Code
caffe2/operators/segment_reduction_op.cc
UnsortedSegmentMeanGradient
No documentation yet.
Code
caffe2/operators/segment_reduction_op.cc
UnsortedSegmentSum
Applies ‘Sum’ to each segment of input tensor. Segments ids can appear in arbitrary order (unlike in SortedSegmentSum).
SEGMENT_IDS is a vector that maps each of the first dimension slices of the DATA to a particular group (segment). Values belonging to the same segment are aggregated together.
If num_segments
argument is passed it would be used as a first dimension for the output. Otherwise, it’d be dynamically calculated from as the max value of SEGMENT_IDS plus one. Other output dimensions are inherited from the input tensor.
Summation is done element-wise across slices of the input tensor and doesn’t change the shape of the individual blocks.
Interface
Arguments | |
num_segments |
Optional int argument specifying the number of output segments and thus the first dimension of the output |
Inputs | |
DATA |
Input tensor, slices of which are aggregated. |
SEGMENT_IDS |
Integer vector with the same length as the first dimension of DATA that maps each slice of DATA to one of the segments |
Outputs | |
OUTPUT |
Aggregated output tensor. Has the first dimension of equal to the number of segments. |
Code
caffe2/operators/segment_reduction_op.cc
UnsortedSegmentSumGradient
No documentation yet.
Code
caffe2/operators/segment_reduction_op.cc
UnsortedSegmentWeightedSum
Applies ‘WeightedSum’ to each segment of input tensor. Segments ids can appear in arbitrary order (unlike in SortedSegmentWeightedSum).
SEGMENT_IDS is a vector that maps each of the first dimension slices of the DATA to a particular group (segment). Values belonging to the same segment are aggregated together.
If num_segments
argument is passed it would be used as a first dimension for the output. Otherwise, it’d be dynamically calculated from as the max value of SEGMENT_IDS plus one. Other output dimensions are inherited from the input tensor.
Input slices are first scaled by SCALARS and then summed element-wise. It doesn’t change the shape of the individual blocks.
Interface
Arguments | |
num_segments |
Optional int argument specifying the number of output segments and thus the first dimension of the output |
grad_on_weights |
Produce also gradient for weights . For now it’s only supported in Lengths -based operators |
Inputs | |
DATA |
Input tensor for the summation |
SCALARS |
Scalar multipliers for the input slices. Must be a vector with the length matching the first dimension of DATA |
SEGMENT_IDS |
Integer vector with the same length as the first dimension of DATA that maps each slice of DATA to one of the segments |
Outputs | |
OUTPUT |
Aggregated output tensor. Has the first dimension of equal to the number of segments. |
Code
caffe2/operators/segment_reduction_op.cc
UnsortedSegmentWeightedSumGradient
No documentation yet.
Code
caffe2/operators/segment_reduction_op.cc
WallClockTime
Time since epoch in nanoseconds.
Interface
Outputs | |
time |
The time in nanoseconds. |
Code
caffe2/operators/utility_ops.cc
WeightedSum
Element-wise weighted sum of several data, weight tensor pairs. Input should be in the form X_0, weight_0, X_1, weight_1, … where X_i all have the same shape, and weight_i are size 1 tensors that specifies the weight of each vector. Note that if one wants to do in-place computation, it could only be done with X_0 also as the output, but not other X_i.
Interface
Inputs | |
weight_0 |
Weight of the first input in the sum. |
Outputs | |
output |
Result containing weighted elem-wise sum of inputs. |
Code
caffe2/operators/utility_ops.cc
Where
Operator Where takes three input data (Tensor
Interface
Inputs | |
C |
input tensor containing booleans |
X |
input tensor |
Y |
input tensor |
Outputs | |
Z |
output tensor |
Code
caffe2/operators/elementwise_logical_ops.cc
XavierFill
No documentation yet.
Code
Xor
Performs element-wise logical operation xor
(with limited broadcast support).
Both input operands should be of type bool
.
If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of size 1 (a scalar value), or having its shape as a contiguous subset of the first tensor’s shape. The starting of the mutually equal shape is specified by the argument “axis”, and if it is not set, suffix matching is assumed. 1-dim expansion doesn’t work yet.
For example, the following tensor shapes are supported (with broadcast=1):
1 2 3 4 5 6 | shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 |
Argument broadcast=1
needs to be passed to enable broadcasting.
Interface
Arguments | |
broadcast |
Pass 1 to enable broadcasting |
axis |
If set, defines the broadcast dimensions. See doc for details. |
Inputs | |
A |
First operand. |
B |
Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size. |
Outputs | |
C |
Result, has same dimensions and A and type bool |
Code
caffe2/operators/elementwise_op_schema.cc
rnn_internal_accumulate_gradient_input
–internal–
Code
caffe2/operators/recurrent_network_op.cc
Adagrad
Computes the AdaGrad update for an input gradient and accumulated history. Concretely, given inputs (param, grad, history, learning_rate), computes
1 2 3 | new_history = history + square(grad) new_grad = learning_rate * grad / (sqrt(new_history) + epsilon) new_param = param + new_grad |
and returns (new_param, new_history).
Interface
Arguments | |
epsilon |
Default 1e-5 |
Inputs | |
param |
Parameters to be updated |
moment |
Moment history |
grad |
Gradient computed |
lr |
learning rate |
Outputs | |
output_param |
Updated parameters |
output_moment |
Updated moment |
Code
Adam
Computes the Adam update ( https://arxiv.org/abs/1412.6980)) for an input gradient and momentum parameters. Concretely, given inputs (param, m1, m2, grad, lr, iters),
1 2 3 4 5 6 7 8 9 | t = iters + 1 corrected_local_rate = lr * sqrt(1 - power(beta2, t)) / (1 - power(beta1, t)) m1_o = (beta1 * m1) + (1 - beta1) * grad m2_o = (beta2 * m2) + (1 - beta2) * np.square(grad) grad_o = corrected_local_rate * m1_o / \ (sqrt(m2_o) + epsilon) param_o = param + grad_o |
and returns (param_o, m1_o, m2_o)
Interface
Arguments | |
beta1 |
Default 0.9 |
beta2 |
Default 0.999 |
epsilon |
Default 1e-5 |
Inputs | |
param |
Parameters to be updated |
moment_1 |
First moment history |
moment_2 |
Second moment history |
grad |
Gradient computed |
lr |
learning rate |
iter |
iteration number |
Outputs | |
output_param |
Updated parameters |
output_moment_1 |
Updated first moment |
output_moment_2 |
Updated second moment |
Code
AtomicIter
Similar to Iter, but takes a mutex as the first input to make sure that updates are carried out atomically. This can be used in e.g. Hogwild sgd algorithms.
Interface
Inputs | |
mutex |
The mutex used to do atomic increment. |
iter |
The iter counter as an int64_t TensorCPU. |
Code
CloseBlobsQueue
No documentation yet.
Code
CloseRebatchingQueue
1 | Closes the Queue. |
Interface
Inputs | |
queue |
object representing the queue |
Code
caffe2/queue/rebatching_queue_ops.cc
CreateBlobsQueue
No documentation yet.
Code
CreateDB
No documentation yet.
Code
CreateRebatchingQueue
1 | Creates the Queue. |
Interface
Arguments | |
num_blobs |
Number of input tensors the queue will support |
capacity |
Maximal number of elements the queue can hold at any given point |
Outputs | |
queue |
object representing the queue |
Code
caffe2/queue/rebatching_queue_ops.cc
DequeueBlobs
No documentation yet.
Code
DequeueRebatchingQueue
1 2 3 4 5 | Dequeue Tensors from the Queue. If the Queue is closed this might return less elements than asked. If num_elements > 1 the returned elements will be concatenated into one tensor per component. |
Interface
Arguments | |
num_elements |
Number of elements to dequeue. By default we dequeue one element. |
Inputs | |
rebatching_queue |
object representing the queue |
tensor |
First tensor to enqueue |
Code
caffe2/queue/rebatching_queue_ops.cc
EnqueueBlobs
No documentation yet.
Code
EnqueueRebatchingQueue
1 2 3 4 5 6 | Enqueues Tensors into the queue. Number of input tensors should be equal to the number of components passed during creation of the queue. If the Queue is closed this operation will fail. If enqueue_batch argument is set. We will split the input tensors by the first dimension to produce single queue elements. |
Interface
Arguments | |
enqueue_batch |
Are we enqueuing a batch or just a single element. By default we enqueue single element. |
Inputs | |
queue |
object representing the queue |
tensor |
First tensor to enque. |
Code
caffe2/queue/rebatching_queue_ops.cc
Ftrl
No documentation yet.
Code
ImageInput
Imports and processes images from a database. For each run of the operator, batch_size images will be processed. GPUs can optionally be used for part of the processing. The following transformations are applied to the image
1 2 3 4 5 6 7 8 9 | - A bounding box is applied to the initial image (optional) - The image is rescaled either up or down (with the scale argument) or just up (with the minsize argument) - The image is randomly cropped (crop size is passed as an argument but the location of the crop is random except if is_test is passed in which case the image in cropped at the center) - The image is normalized. Each of its color channels can have separate normalization values |
The dimension of the output image will always be cropxcrop
Interface
Arguments | |
batch_size |
Number of images to output for each run of the operator. Must be 1 or greater |
color |
Number of color channels (1 or 3). Defaults to 1 |
scale |
Scale the size of the smallest dimension of the image to this. Scale and minsize are mutually exclusive. Must be larger than crop |
minsize |
Scale the size of the smallest dimension of the image to this only if the size is initially smaller. Scale and minsize are mutually exclusive. Must be larger than crop. |
warp |
If 1, both dimensions of the image will be set to minsize or scale; otherwise, the other dimension is proportionally scaled. Defaults to 0 |
crop |
Size to crop the image to. Must be provided |
mirror |
Whether or not to mirror the image. Defaults to 0 |
mean |
Mean by which to normalize color channels. Defaults to 0. |
mean_per_channel |
Vector of means per color channel (1 or 3 elements). Defaults to mean argument. Channel order BGR |
std |
Standard deviation by which to normalize color channels. Defaults to 1. |
std_per_channel |
Vector of standard dev. per color channel (1 or 3 elements). Defaults to std argument. Channel order is BGR |
bounding_ymin |
Bounding box coordinate. Defaults to -1 (none) |
bounding_xmin |
Bounding box coordinate. Defaults to -1 (none) |
bounding_height |
Bounding box coordinate. Defaults to -1 (none) |
bounding_width |
Bounding box coordinate. Defaults to -1 (none) |
is_test |
Set to 1 to do deterministic cropping. Defaults to 0 |
use_caffe_datum |
1 if the input is in Caffe format. Defaults to 0 |
use_gpu_transform |
1 if GPU acceleration should be used. Defaults to 0. Can only be 1 in a CUDAContext |
decode_threads |
Number of CPU decode/transform threads. Defaults to 4 |
output_type |
If gpu_transform, can set to FLOAT or FLOAT16. |
db |
Name of the database (if not passed as input) |
db_type |
Type of database (if not passed as input). Defaults to leveldb |
Inputs | |
reader |
The input reader (a db::DBReader) |
Outputs | |
data |
Tensor containing the images |
label |
Tensor containing the labels |
Code
caffe2/image/image_input_op.cc
Iter
Stores a singe integer, that gets incremented on each call to Run(). Useful for tracking the iteration count during SGD, for example.
Code
LearningRate
Learning rate is a decreasing function of time. With low learning rates the improvements will be linear. With high learning rates they will start to look more exponential. Learning rate is controled by the following arguments: #### Required * iterations
* base_lr
: base learning rate * policy
: this controls how the learning rate is applied, options are:
1 2 3 4 5 | * `fixed` * `step`: uses `stepsize`, `gamma` * `exp`: uses `gamma` * `inv`: uses `gamma`, `power` |
#### Optional: * stepsize
: defaults to 0 * gamma
: defaults to 0 * power
: defaults to 0 Usage: train_net.LearningRate( iterations , “ label “, base_lr= float ,
1 | policy="policy_name", stepsize=*int*, gamma=*float*) |
Example usage: train_net.LearningRate(200, “LR”, base_lr=-0.1,
1 | policy="step", stepsize=20, gamma=0.9) |
Interface
Arguments | |
base_lr |
(float, required) base learning rate |
policy |
(float, default 1.0) strategy for gamma enforcement |
power |
(float, default 1.0) used only for inv policy type |
gamma |
(float, default 1.0) momentum of change |
stepsize |
(float, default 1.0) sampling rate on iterations |
max_iter |
(int, default -1) maximum iterations in this training run |
Inputs | |
input |
description needed |
Outputs | |
output |
description needed |
Code
caffe2/sgd/learning_rate_op.cc
MomentumSGD
Computes a momentum SGD update for an input gradient and momentum parameters. Concretely, given inputs (grad, m, lr) and parameters (momentum, nesterov), computes:
1 2 3 4 5 6 7 | if not nesterov: adjusted_gradient = lr * grad + momentum * m return (adjusted_gradient, adjusted_gradient) else: m_new = momentum * m + lr * grad return ((1 + momentum) * m_new - momentum * m, m_new) |
Output is (grad, momentum) Note the difference to MomemtumSGDUpdate, which actually performs the parameter update (and is thus faster).
Code
MomentumSGDUpdate
Performs a momentum SGD update for an input gradient and momentum parameters. Concretely, given inputs (grad, m, lr, param) and arguments (momentum, nesterov), computes:
1 2 3 4 5 6 7 8 9 | if not nesterov: adjusted_gradient = lr * grad + momentum * m param = param - adjusted_gradient return (adjusted_gradient, adjusted_gradient, param) else: m_new = momentum * m + lr * grad param = param - ((1 + momentum) * m_new - momentum * m), return ((1 + momentum) * m_new - momentum * m, m_new, param) |
Output is (grad, momentum, parameter). Note the difference to MomentumSGD, which returns a new gradient but does not perform the parameter update.
Code
PackedFC
Computes the result of passing an input vector X into a fully connected layer with 2D weight matrix W and 1D bias vector b. This is essentially the same as the FC operator but allows one to pack the weight matrix for more efficient inference. See the schema for the FC op for details. Unlike many other operators in Caffe2, this operator is stateful: it assumes that the input weight matrix W never changes, so it is only suitable for inference time when the weight matrix never gets updated by any other ops. Due to performance considerations, this is not checked in non-debug builds.
Code
caffe2/mkl/operators/packed_fc_op.cc
Python
No documentation yet.
Code
PythonGradient
No documentation yet.
Code
RmsProp
Computes the RMSProp update ( http://www.cs.toronto.edu/ ~tijmen/csc321/slides/lecture_slides_lec6.pdf). Concretely, given inputs (grad, mean_squares, mom, lr), computes:
1 2 3 4 | mean_squares_o = mean_squares + (1 - decay) * (squaare(grad) - mean_squares) mom_o = momentum * mom + lr * grad / sqrt(epsilon + mean_squares_o) grad_o = mom_o |
returns (grad_o, mean_squares_o, mom_o).
Code
SafeDequeueBlobs
Dequeue the blobs from queue. When the queue is closed and empty, the output status will be set to true which can be used as exit criteria for execution step. The 1st input is the queue and the last output is the status. The rest are data blobs.
Interface
Inputs | |
queue |
The shared pointer for the BlobsQueue |
Code
SafeEnqueueBlobs
Enqueue the blobs into queue. When the queue is closed and full, the output status will be set to true which can be used as exit criteria for execution step. The 1st input is the queue and the last output is the status. The rest are data blobs.
Interface
Inputs | |
queue |
The shared pointer for the BlobsQueue |
Code
SparseAdagrad
Given inputs (param, history, indices, grad, lr), runs the dense AdaGrad update on (param, grad, history[indices], lr), and returns (new_param, new_history) as in the dense case.
Interface
Arguments | |
epsilon |
Default 1e-5 |
Inputs | |
param |
Parameters to be updated |
moment |
Moment history |
indices |
Sparse indices |
grad |
Gradient computed |
lr |
learning rate |
Outputs | |
output_param |
Updated parameters |
output_moment_1 |
Updated moment |
Code
SparseAdam
Computes the Adam Update for the sparse case. Given inputs (param, moment1, moment2, indices, grad, lr, iter), runs the dense Adam on on (param, moment1[indices], momemnt2[indices], lr, iter) and returns (new_param, new_moment1, new_moment2) as in dense case
Interface
Arguments | |
beta1 |
Default 0.9 |
beta2 |
Default 0.999 |
epsilon |
Default 1e-5 |
Inputs | |
param |
Parameters to be updated |
moment_1 |
First moment history |
moment_2 |
Second moment history |
indices |
Sparse indices |
grad |
Gradient computed |
lr |
learning rate |
iter |
iteration number |
Outputs | |
output_param |
Updated parameters |
output_moment_1 |
Updated first moment |
output_moment_2 |
Updated second moment |
Code
SparseFtrl
No documentation yet.
Code
SparseMomentumSGDUpdate
Performs a momentum SGD update analogous to MomentumSGDUpdate, but using a GradientSlice and indices into the full param and momentum tables. Both param and momentum should be in-place (corresponding inputs and outputs should be the same blobs).
Interface
Arguments | |
momentum |
Momentum hyperparameter. |
nesterov |
(boolean) Whether to use Nesterov Accelerated Gradient. |
Inputs | |
grad |
GradientSlice with gradients for updated indices. |
moment |
Momentum blob, same shape as param. |
lr |
Learning rate. |
param |
Full parameter blob. |
indices |
Indices (in first dimension of param) where updates are performed. |
Outputs | |
output_grad |
Adjusted gradient. |
output_moment |
Updated momentum. |
output_param |
Updated parameter |
Code
VideoInput
No documentation yet.
Code
caffe2/video/video_input_op.cc
WeightedSampleDequeueBlobs
Dequeue the blobs from multiple queues. When one of queues is closed and empty, the output status will be set to true which can be used as exit criteria for execution step. The 1st input is the queue and the last output is the status. The rest are data blobs.
Interface
Inputs | |
weights |
Weights for sampling from multiple queues |
Code
FCGradient_Decomp
No documentation yet.
Code
caffe2/experiments/operators/fully_connected_op_decomposition.cc
FCGradient_Prune
No documentation yet.
Code
caffe2/experiments/operators/fully_connected_op_prune.cc
FC_Decomp
No documentation yet.
Code
caffe2/experiments/operators/fully_connected_op_decomposition.cc
FC_Prune
No documentation yet.
Code
caffe2/experiments/operators/fully_connected_op_prune.cc
FC_Sparse
No documentation yet.
Code
caffe2/experiments/operators/fully_connected_op_sparse.cc
FunHash
This layer compresses a fully-connected layer for sparse inputs via hashing.
It takes four required inputs and an optional fifth input.
The first three inputs scalars
, indices
, and segment_ids
are the sparse segmented representation of sparse data, which are the same as the last three inputs of the SparseSortedSegmentWeightedSum
operator. If the argument num_segments
is specified, it would be used as the first dimension for the output; otherwise it would be derived from the maximum segment ID.
The fourth input is a 1D weight vector. Each entry of the fully-connected layer would be randomly mapped from one of the entries in this vector.
When the optional fifth input vector is present, each weight of the fully-connected layer would be the linear combination of K entries randomly mapped from the weight vector, provided the input (length-K vector) serves as the coefficients.
Interface
Arguments | |
num_outputs |
Number of outputs |
num_segments |
Number of segments |
Inputs | |
scalars |
Values of the non-zero entries of the sparse data. |
indices |
Indices to the non-zero valued features. |
segment_ids |
Segment IDs corresponding to the non-zero entries. |
weight |
Weight vector |
alpha |
Optional coefficients for linear combination of hashed weights. |
Outputs | |
output |
Output tensor with the first dimension equal to the number of segments. |
Code
caffe2/experiments/operators/funhash_op.cc
FunHashGradient
No documentation yet.
Code
caffe2/experiments/operators/funhash_op.cc
SparseFunHash
This layer compresses a fully-connected layer for sparse inputs via hashing.
It takes four required inputs and an option fifth input.
The first three inputs scalars
, indices
, and segment_ids
are the sparse segmented representation of sparse data, which are the same as the last three inputs of the SparseSortedSegmentWeightedSum
operator. If the argument num_segments
is specified, it would be used as the first dimension for the output; otherwise it would be derived from the maximum segment ID.
The fourth input is a 1D weight vector. Each entry of the fully-connected layer would be randomly mapped from one of the entries in this vector.
When the optional fifth input vector is present, each weight of the fully-connected layer would be the linear combination of K entries randomly mapped from the weight vector, provided the input (length-K vector) serves as the coefficients.
Interface
Arguments | |
num_outputs |
Number of outputs |
num_segments |
Number of segments |
Inputs | |
scalars |
Values of the non-zero entries of the sparse data. |
indices |
Indices to the non-zero valued features. |
segment_ids |
Segment IDs corresponding to the non-zero entries. |
weight |
Weight vector |
alpha |
Optional coefficients for linear combination of hashed weights. |
Outputs | |
output |
Output tensor with the first dimension equal to the number of segments. |
Code
caffe2/experiments/operators/sparse_funhash_op.cc
SparseFunHashGradient
No documentation yet.
Code
caffe2/experiments/operators/sparse_funhash_op.cc
SparseMatrixReshape
Compute the indices of the reshaped sparse matrix.
It takes two 1D tensors as input: the column indices (in int64) and the row indices (in int), which correspond to INDICES
and SEGMENT_IDS
in SparseSortedSegment
family.
It outputs the corresponding reshaped column and row indices.
Two arguments are required: an argument old_shape
specifies the original shape of the matrix, and new_shape
specifies the new shape.
One of the dimension in old_shape
and new_shape
can be -1.
The valid combinations are listed below, where p, q, r, s are strictly positive integers.
old_shape=(p, q) new_shape=(r, s) old_shape=(p, q) new_shape=(-1, s) old_shape=(p, q) new_shape=(r, -1) old_shape=(-1, q) new_shape=(-1, s) Note that only the first dimension in old_shape
can be -1. In that case the second dimension in new_shape
must NOT be -1.
Interface
Arguments | |
old_shape |
Old shape. |
new_shape |
New shape. |
Inputs | |
old_col |
Original column indices. |
old_row |
Original row indices. |
Outputs | |
new_col |
New column indices. |
new_row |
New row indices. |
Code
caffe2/experiments/operators/sparse_matrix_reshape_op.cc
TTContraction
Tensor contraction C = A * B
Interface
Arguments | |
K |
i_{k-1} * r_k |
M |
r_{k-1} * o_{k-1} |
N |
o_k |
Inputs | |
A |
2D matrix of size (K x M) |
B |
tensor |
Outputs | |
C |
contracted tensor |
Code
caffe2/experiments/operators/tt_contraction_op.cc
TTContractionGradient
No documentation yet.
Code
caffe2/experiments/operators/tt_contraction_op.cc
TTPad
No documentation yet.
Code
caffe2/experiments/operators/tt_pad_op.cc
TTPadGradient
No documentation yet.
Code
caffe2/experiments/operators/tt_pad_op.cc
FC_Dcomp
No schema documented yet.
MaxPoolWithIndexGradient
No schema documented yet.
ReluFp16
No schema documented yet.
ReluFp16Gradient
No schema documented yet.
Snapshot
No schema documented yet.
SparseLabelToDense
No schema documented yet.
StumpFunc
No schema documented yet.
TTLinearGradient
No schema documented yet.