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#include "caffe2/operators/gather_ranges_to_dense_op.h"
namespace caffe2 {
namespace {
OPERATOR_SCHEMA(GatherRangesToDense)
.NumInputs(2, 3)
.NumOutputs(1, INT_MAX)
.SetDoc(R"DOC(
Given DATA tensor of rank 1, and RANGES tensor of rank 3, gather values
corresponding to each range into a separate output tensor. If the optional input
KEY tensor is also given, the output will be sorted by KEY for each example.
RANGES dimensions 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)
Each feature has fixed lengths which are passed as lengths argument and a
separate tensor will be produced for each feature.
i.e. DATA.dim(1) = len(lengths) = NumOuptuts.
Missing features (represented by empty ranges) filled with default_value.
Example 1:
DATA = [1, 2, 3, 4, 5, 6, 7, 8]
RANGES = [
[
[2, 4],
[0, 2],
],
[
[0, 0],
[6, 2],
]
]
lengths = [4, 2]
OUTPUT[0] = [[3, 4, 5, 6], [0, 0, 0, 0]]
OUTPUT[1] = [[1, 2], [7, 8]]
Example 2 (with KEY):
DATA = [1, 2, 3, 4, 5, 6, 7, 8]
KEY = [0, 1, 3, 2, 1, 0, 1, 0]
RANGES = [
[
[2, 4],
[0, 2],
],
[
[0, 0],
[6, 2],
]
]
lengths = [4, 2]
OUTPUT[0] = [[6, 5, 4, 3], [0, 0, 0, 0]]
OUTPUT[1] = [[1, 2], [8, 7]]
Contrast Example 2 with Example 1. For each data point per feature, the values
are sorted by the corresponding KEY.
)DOC")
.Input(0, "DATA", "Tensor of rank 1.")
.Input(
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 dimension represents a range in the format (start, lengths)")
.Input(2, "KEY", "Tensor of rank 1 and type int64.")
.Output(0, "OUTPUT", "1-D tensor of size sum of range lengths")
.Arg("lengths", "Expected lengths for ranges")
.Arg(
"min_observation",
"The number of observations needed before deciding that the ratio of "
"mismatched ranges is alarming, also determines whether an info "
"sumarizing the empty and mismatch ratio will be printed at the end.")
.Arg(
"max_mismatched_ratio",
"An error is raised when ratio of mismatched ranges exceeds this.")
.Arg(
"max_empty_ratio",
"An error is raised when ratio of empty ranges exceeds this (default is"
" 1, which means by default no error will be triggered).")
.TensorInferenceFunction([](const OperatorDef& def,
const vector<TensorShape>& in) {
ArgumentHelper helper(def);
auto lengths = helper.GetRepeatedArgument<int>("lengths");
CAFFE_ENFORCE_EQ(in[0].dims_size(), 1, "DATA should be 1-D tensor.");
CAFFE_ENFORCE_EQ(in[1].dims_size(), 3, "RANGES should be 3-D tensor.");
if (in.size() > 2) {
CAFFE_ENFORCE_EQ(in[2].dims_size(), 1, "KEY should be 1-D tensor.");
}
CAFFE_ENFORCE_GT(lengths.size(), 0, "lengths should be non-empty.");
std::vector<TensorShape> out(lengths.size());
// NOLINTNEXTLINE(clang-diagnostic-sign-compare)
for (int i = 0; i < lengths.size(); ++i) {
out[i].set_data_type(in[0].data_type());
out[i].add_dims(in[1].dims(0));
out[i].add_dims(lengths[i]);
}
return out;
});
REGISTER_CPU_OPERATOR(GatherRangesToDense, GatherRangesToDenseOp<CPUContext>);
NO_GRADIENT(GatherRangesToDense);
} // namespace
} // namespace caffe2
using GatherRangesToDenseCPUOp =
caffe2::GatherRangesToDenseOp<caffe2::CPUContext>;
C10_EXPORT_CAFFE2_OP_TO_C10_CPU(
GatherRangesToDense,
"_caffe2::GatherRangesToDense(Tensor data, Tensor ranges, Tensor? key, int[] lengths, int min_observation, float max_mismatched_ratio, float max_empty_ratio) -> Tensor[] outputs",
GatherRangesToDenseCPUOp);
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