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Summary: As titled. Test Plan: ``` buck test caffe2/caffe2/python/operator_test:torch_integration_test -- test_batch_bucket_one_hot_op ``` Reviewed By: yf225 Differential Revision: D23005981 fbshipit-source-id: 1daa8d3e7d6ad75e97e94964db95ccfb58541672
101 lines
2.5 KiB
C++
101 lines
2.5 KiB
C++
#ifndef CAFFE_OPERATORS_ONE_HOT_OPS_H_
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#define CAFFE_OPERATORS_ONE_HOT_OPS_H_
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#include "caffe2/core/context.h"
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#include "caffe2/core/export_caffe2_op_to_c10.h"
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#include "caffe2/core/logging.h"
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#include "caffe2/core/operator.h"
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#include "caffe2/utils/math.h"
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C10_DECLARE_EXPORT_CAFFE2_OP_TO_C10(BatchBucketOneHot);
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namespace caffe2 {
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template <class Context>
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class OneHotOp final : public Operator<Context> {
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public:
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USE_OPERATOR_CONTEXT_FUNCTIONS;
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template <class... Args>
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explicit OneHotOp(Args&&... args)
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: Operator<Context>(std::forward<Args>(args)...) {}
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bool RunOnDevice() override {
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auto& indices = Input(0);
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CAFFE_ENFORCE_EQ(
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indices.dim(),
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1,
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"indices input must be 1D tensor of data type int64_t");
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// Index size input must be in CPU context
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auto& index_size_tensor = this->template Input<Tensor>(1, CPU);
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CAFFE_ENFORCE_EQ(
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index_size_tensor.numel(),
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1,
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"index_size_tensor input must be scalar of data type int64_t");
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auto batch_size = indices.numel();
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auto index_size = *index_size_tensor.template data<int64_t>();
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auto one_hots = Output(0);
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one_hots->Resize(batch_size, index_size);
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auto output_size = one_hots->numel();
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if (output_size == 0) {
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return true;
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}
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DoOneHotOp(batch_size, index_size, indices, one_hots);
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return true;
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}
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protected:
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void DoOneHotOp(
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int64_t batch_size,
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int64_t index_size,
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const Tensor& indices,
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Tensor* output);
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};
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template <class Context>
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class BatchOneHotOp final : public Operator<Context> {
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public:
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USE_OPERATOR_CONTEXT_FUNCTIONS;
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template <class... Args>
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explicit BatchOneHotOp(Args&&... args)
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: Operator<Context>(std::forward<Args>(args)...) {}
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bool RunOnDevice() override {
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return DispatchHelper<TensorTypes<int32_t, int64_t>>::call(this, Input(X));
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}
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template <typename T>
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bool DoRunWithType();
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INPUT_TAGS(X, LENS, VALS);
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protected:
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OUTPUT_TAGS(ONE_HOT);
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private:
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// allows for fast random access to a given dict and is re-used across runs
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std::vector<int64_t> valsOffsets_;
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};
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template <class Context>
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class BatchBucketOneHotOp final : public Operator<Context> {
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public:
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USE_OPERATOR_CONTEXT_FUNCTIONS;
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template <class... Args>
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explicit BatchBucketOneHotOp(Args&&... args)
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: Operator<Context>(std::forward<Args>(args)...) {}
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bool RunOnDevice() override;
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protected:
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INPUT_TAGS(X, LENS, BOUNDARIES);
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OUTPUT_TAGS(ONE_HOT);
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};
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} // namespace caffe2
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#endif // CAFFE_OPERATORS_ONE_HOT_OPS_H_
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