| /*! |
| * Copyright (c) 2015 by Contributors |
| * \file ctc_loss.cu |
| * \brief |
| * \author Sebastian Bodenstein |
| */ |
| #include <algorithm> |
| #include "./ctc_loss-inl.h" |
| #include "./ctc_include/detail/gpu_ctc.h" |
| |
| namespace mshadow { |
| |
| template <typename DType> |
| ctcStatus_t compute_ctc_cost(const Tensor<gpu, 3, DType> activations, |
| DType *costs, DType *grads, int *labels, |
| int *label_lengths, int *input_lengths, |
| void *workspace, int train) { |
| int minibatch = static_cast<int>(activations.size(1)); |
| int alphabet_size = static_cast<int>(activations.size(2)); |
| int blank_label = 0; |
| mxnet_warpctc::GpuCTC<DType> ctc(alphabet_size, minibatch, workspace, |
| activations.stream_->stream_, blank_label); |
| if (train) |
| return ctc.cost_and_grad(activations.dptr_, grads, costs, labels, |
| label_lengths, input_lengths); |
| else |
| return ctc.score_forward(activations.dptr_, costs, labels, |
| label_lengths, input_lengths); |
| } |
| |
| } // namespace mshadow |
| |
| //////////////////////////////////////////////////////////////////////////////// |
| |
| namespace mxnet { |
| namespace op { |
| template <> |
| Operator *CreateOp<gpu>(CTCLossParam param, int dtype) { |
| return new CTCLossOp<gpu>(param); |
| } |
| |
| } // namespace op |
| } // namespace mxnet |
