#ifndef CAFFE2_OPERATORS_NUMPY_TILE_OP_H_
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#define CAFFE2_OPERATORS_NUMPY_TILE_OP_H_
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#include "caffe2/core/common_omp.h"
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#include "caffe2/core/context.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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namespace caffe2 {
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// Copy a Blob n times along a specified axis.
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template <class Context>
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class NumpyTileOp : 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 NumpyTileOp(Args&&... args)
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: Operator<Context>(std::forward<Args>(args)...) {}
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~NumpyTileOp() {}
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bool RunOnDevice() override {
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const auto& input = Input(0);
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const auto& repeats = Input(1);
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// Check that the `repeats` tensor has the correct rank, has a number of
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// elements equal to the number of axes of `input`.
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CAFFE_ENFORCE_EQ(repeats.dim(), 1, "repeats input must be a 1-d tensor");
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CAFFE_ENFORCE_EQ(
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repeats.numel(),
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input.dim(),
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"repeats input have the same"
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" number of elements as `inputs` has dimensions.");
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const int64_t *repeats_data = repeats.template data<int64_t>();
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for (size_t i = 0; i < repeats.numel(); ++i) {
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CAFFE_ENFORCE_GE(repeats_data[i], 0);
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}
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auto* output = Output(0);
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// Alternate inputs and outputs between two buffers. Repeatedly apply the
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// Tile kernel along each axis. Then copy out the resulting data into the
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// output tensor.
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Tensor *src = &buffer, *dst = output;
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src->CopyFrom(input);
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vector<int64_t> output_dims(input.sizes().vec());
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for (size_t i = 0; i < repeats.numel(); ++i) {
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if (repeats_data[i] == 1) {
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continue;
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}
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// size up to (and not including) axis
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const auto outer_dim = src->size_to_dim(i);
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// size from axis up
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const auto inner_dim = src->size_from_dim(i);
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dst->Resize(outer_dim, inner_dim * repeats_data[i]);
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/**
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* How this works:
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* Imagine a 2D tensor (matrix) of size 3x10, tiled 2 times.
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* - Tiling along axis 0 (row) means copying the entire 3x10 Matrix 2
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* times. outer_dim = 0, inner_dim = 30.
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* - Tiling along axis 1 (column) means copying each row 2 times, then
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* proceed to the next row, until the end. outer_dim = 3, inner_dim = 10.
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*/
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const char* src_data = static_cast<const char*>(src->raw_data());
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char* dst_data = static_cast<char*>(dst->raw_mutable_data(src->dtype()));
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DoTile(
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src->dtype(),
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src->itemsize(),
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outer_dim,
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inner_dim,
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repeats_data[i],
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src_data,
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dst_data);
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output_dims[i] *= repeats_data[i];
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dst->Reshape(output_dims);
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std::swap(src, dst);
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}
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// NB: because we have the swap at the end of the above loop, our real
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// result tensor is going to live in *src when we reach this line
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// whether we entered the loop or not :)
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if (output != src)
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output->CopyFrom(*src);
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return true;
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}
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private:
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void DoTile(
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const TypeMeta& meta,
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int item_size,
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int outer_dim,
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int inner_dim,
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int64_t num_tiles,
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const char* input_data,
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char* output_data) {
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for (auto i = 0; i < outer_dim; ++i) {
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for (auto t = 0; t < num_tiles; ++t) {
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context_.CopyItemsSameDevice(meta, inner_dim, input_data, output_data);
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output_data += inner_dim * item_size;
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}
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input_data += inner_dim * item_size;
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}
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}
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Tensor buffer{Context::GetDeviceType()};
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};
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} // namespace caffe2
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#endif // CAFFE2_OPERATORS_NUMPY_TILE_OP_H_
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