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| // Copyright (c) Facebook, Inc. and its affiliates.All Rights Reserved | |
| // Please refer to original code: https://github.com/NVlabs/instant-ngp | |
| // and the pytorch wrapper from https://github.com/ashawkey/torch-ngp | |
| // inputs: [B, N, D], float, in [0, 1] | |
| // embeddings: [B, sO, C], float | |
| // offsets: [L + 1], uint32_t | |
| // outputs: [B, N, L * C], float | |
| // H: base resolution | |
| void hash_encode_forward(at::Tensor inputs, at::Tensor embeddings, at::Tensor offsets, at::Tensor outputs, const float beta, const uint32_t B, const uint32_t N, const uint32_t D, const uint32_t C, const uint32_t L, const uint32_t H, const bool calc_grad_inputs, at::Tensor dy_dx, const uint32_t mode); | |
| void hash_encode_backward(at::Tensor grad, at::Tensor inputs, at::Tensor embeddings, at::Tensor offsets, at::Tensor grad_embeddings, const float beta, const uint32_t B, const uint32_t N, const uint32_t D, const uint32_t C, const uint32_t L, const uint32_t H, const bool calc_grad_inputs, at::Tensor dy_dx, at::Tensor grad_inputs, const uint32_t mode); | |
| void hash_encode_forward_cuda(const float *inputs, const float *embeddings, const int *offsets, float *outputs, const float beta, const uint32_t B, const uint32_t N, const uint32_t D, const uint32_t C, const uint32_t L, const uint32_t H, const bool calc_grad_inputs, float *dy_dx, const uint32_t mode); | |
| void hash_encode_backward_cuda(const float *grad, const float *inputs, const float *embeddings, const int *offsets, float *grad_embeddings, const float beta, const uint32_t B, const uint32_t N, const uint32_t D, const uint32_t C, const uint32_t L, const uint32_t H, const bool calc_grad_inputs, float *dy_dx, float *grad_inputs, const uint32_t mode); | |