mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
4cb534f92e
Summary:
This is an automatic change generated by the following script:
```
#!/usr/bin/env python3
from subprocess import check_output, check_call
import os
def get_compiled_files_list():
import json
with open("build/compile_commands.json") as f:
data = json.load(f)
files = [os.path.relpath(node['file']) for node in data]
for idx, fname in enumerate(files):
if fname.startswith('build/') and fname.endswith('.DEFAULT.cpp'):
files[idx] = fname[len('build/'):-len('.DEFAULT.cpp')]
return files
def run_clang_tidy(fname):
check_call(["python3", "tools/clang_tidy.py", "-c", "build", "-x", fname,"-s"])
changes = check_output(["git", "ls-files", "-m"])
if len(changes) == 0:
return
check_call(["git", "commit","--all", "-m", f"NOLINT stubs for {fname}"])
def main():
git_files = check_output(["git", "ls-files"]).decode("ascii").split("\n")
compiled_files = get_compiled_files_list()
for idx, fname in enumerate(git_files):
if fname not in compiled_files:
continue
if fname.startswith("caffe2/contrib/aten/"):
continue
print(f"[{idx}/{len(git_files)}] Processing {fname}")
run_clang_tidy(fname)
if __name__ == "__main__":
main()
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56892
Reviewed By: H-Huang
Differential Revision: D27991944
Pulled By: malfet
fbshipit-source-id: 5415e1eb2c1b34319a4f03024bfaa087007d7179
81 lines
2.5 KiB
C++
81 lines
2.5 KiB
C++
#include "caffe2/operators/bucketize_op.h"
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#include "caffe2/core/operator.h"
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#include "caffe2/core/tensor.h"
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namespace caffe2 {
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template <>
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bool BucketizeOp<CPUContext>::RunOnDevice() {
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auto& input = Input(X);
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CAFFE_ENFORCE_GE(input.dim(), 1);
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auto N = input.numel();
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auto* output = Output(INDICES, input.sizes(), at::dtype<int32_t>());
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const auto* input_data = input.template data<float>();
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auto* output_data = output->template mutable_data<int32_t>();
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math::Set<int32_t, CPUContext>(output->numel(), 0.0, output_data, &context_);
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for (int64_t pos = 0; pos < N; pos++) {
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// here we assume the boundary values for each feature are sorted
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auto bucket_idx =
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std::lower_bound(
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boundaries_.begin(), boundaries_.end(), input_data[pos]) -
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boundaries_.begin();
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output_data[pos] = bucket_idx;
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}
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return true;
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};
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
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REGISTER_CPU_OPERATOR(Bucketize, BucketizeOp<CPUContext>);
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
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OPERATOR_SCHEMA(Bucketize)
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.NumInputs(1)
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.NumOutputs(1)
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.SetDoc(R"DOC(
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This operator works as bucketize in tensorflow and digitize
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in numpy. It bucketizes the input 'X' based on argument 'boundaries'.
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For each value x in input 'data', the operator returns index i given
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boundaries[i-1] < x <= boundaries[i].
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If values in 'data' are beyond the bounds of boundaries, 0 or
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len(boundaries) is returned as appropriate.
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The boundaries need to be monotonically increasing.
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For example
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If data = [2, 4, 1] and boundaries = [0.1, 2.5], then
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output = [1, 2, 1]
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If data = [[2, 3], [4, 1], [2, 5]] and boundaries = [0.1, 2.5], then
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output = [[1, 2], [2, 1], [1, 2]]
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)DOC")
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.Input(0, "data", "input tensor")
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.Output(
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0,
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"output",
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"indices of bins given by boundaries to which each value"
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"in data belongs")
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.TensorInferenceFunction([](const OperatorDef& /* def */,
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const vector<TensorShape>& in) {
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vector<TensorShape> out(in);
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out[0].set_data_type(TensorProto::INT32);
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return out;
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})
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.Arg("boundaries", "bucketization boundaries");
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
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NO_GRADIENT(BucketizeOp);
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} // namespace caffe2
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using BucketizeInt = caffe2::BucketizeOp<caffe2::CPUContext>;
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C10_EXPORT_CAFFE2_OP_TO_C10_CPU(
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Bucketize,
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"_caffe2::Bucketize(Tensor data, float[] boundaries) -> Tensor output",
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BucketizeInt);
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