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#include <torch/csrc/jit/mobile/train/random.h>
#include <torch/types.h>
#include <algorithm>
#include <cstddef>
#include <vector>
namespace torch {
namespace jit {
namespace mobile {
RandomSampler::RandomSampler(int64_t size, Dtype index_dtype)
: indices_(torch::randperm(size, index_dtype)) {}
RandomSampler::~RandomSampler() = default;
void RandomSampler::reset(optional<size_t> new_size) {
// This allocates a new chunk of memory every time (just FYI). It should be
// amortized over the entire epoch hopefully.
const auto size = new_size.value_or(static_cast<size_t>(indices_.numel()));
indices_ = torch::randperm(size, indices_.options());
index_ = 0;
}
optional<std::vector<size_t>> RandomSampler::next(size_t batch_size) {
AT_ASSERT(index_ <= indices_.numel());
const size_t remaining_indices = indices_.numel() - index_;
if (remaining_indices == 0) {
return nullopt;
}
std::vector<size_t> index_batch(std::min(batch_size, remaining_indices));
auto slice = indices_.slice(/*dim=*/0, index_, index_ + index_batch.size());
// You may want to store your indices with 32-bit or less, but here we need
// to upcast to 64-bit. A batch itself won't hold too many indices, so that
// should be ok. Note that if this indeed results in a type promotion, there
// will be two allocations: one for the upcast slice, and one for the
// returned `index_batch` vector.
slice = slice.to(torch::kInt64);
const auto* data = slice.data_ptr<int64_t>();
std::copy(data, data + index_batch.size(), index_batch.begin());
index_ += index_batch.size();
return index_batch;
}
void RandomSampler::save(serialize::OutputArchive& archive) const {
TORCH_CHECK(false, "Serialization of RandomSampler not supported on mobile.");
}
void RandomSampler::load(serialize::InputArchive& archive) {
TORCH_CHECK(false, "Serialization of RandomSampler not supported on mobile.");
}
size_t RandomSampler::index() const noexcept {
return index_;
}
} // namespace mobile
} // namespace jit
} // namespace torch
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