e25f20f7a3
commit 3f4938648950a7f3bf9a19c320ca9fae7c52de20 Author: sophgo-forum-service <forum_service@sophgo.com> Date: Mon May 13 13:44:23 2024 +0800 [feat] cviruntime opensource for cv18xx soc. - a4b6a3, add cumsum and gatherelements_pt.
105 lines
3.9 KiB
C++
105 lines
3.9 KiB
C++
/*
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* Copyright (C) Cvitek Co., Ltd. 2019-2020. All rights reserved.
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*/
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#ifndef UNPOOLING_OP_H_
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#define UNPOOLING_OP_H_
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#include "tpuc/CustomOp.h"
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#include <cvikernel/cvikernel.h>
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namespace cvi {
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class UnPoolingOp : public CustomOp {
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public:
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UnPoolingOp(OpParam ¶m) : CustomOp(param) {}
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void interpretFp32(std::vector<std::shared_ptr<std::vector<float>>> &operand_tensors,
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std::vector<std::vector<int64_t>> &operand_shapes,
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std::shared_ptr<std::vector<float>> &result_tensor,
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std::vector<int64_t> &result_shape);
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void interpretInt8(std::vector<std::shared_ptr<std::vector<float>>> &operand_tensors,
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std::vector<std::vector<int64_t>> &operand_shapes,
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std::shared_ptr<std::vector<float>> &result_tensor,
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std::vector<int64_t> &result_shape);
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void quantizeInt8();
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void codeGenInt8(void *ctx,
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std::vector<std::vector<int64_t>> &operand_shapes,
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std::vector<uint64_t> &operand_gaddrs,
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std::vector<int64_t> &result_shape, uint64_t result_gaddr,
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int layer_id);
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private:
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void alloc_lmem(cvk_context_t *ctx, uint32_t tiling_c, uint32_t tiling_h,
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uint32_t input_c, uint32_t input_h, uint32_t input_w,
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uint32_t output_c, uint32_t output_h, uint32_t output_w,
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cvk_fmt_t fmt, int eu_align, cvk_tl_t &tl_ifmap, cvk_tl_t &tl_working,
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cvk_tl_t &tl_ofmap, cvk_tl_t &tl_mask);
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void tdma_load(cvk_context_t *ctx, cvk_tl_t *tlp, uint64_t ga_src, cvk_tg_stride_t stride,
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int n_pos, int c_pos, int h_pos);
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void unpooling_compute(cvk_context_t *ctx, uint32_t layer_id, int scale_h, int scale_w,
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cvk_tl_t *tl_ifmap, cvk_tl_t *tl_working, cvk_tl_t *tl_mask, cvk_tl_t *tl_ofmap);
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void tdma_store(cvk_context_t *ctx, cvk_tl_t *tlp, uint64_t ga_dst, cvk_tg_stride_t stride,
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uint32_t n_pos, uint32_t c_pos, uint32_t h_pos, uint32_t crop_h, uint32_t crop_w);
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void unpooling_codegen(cvk_context_t *ctx, uint32_t layer_id,
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uint64_t data_gaddr, uint64_t mask_gaddr, uint64_t output_gaddr,
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int input_n, int input_c, int input_h, int input_w,
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int scale, int unpool_h, int unpool_w);
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void unpooling(std::vector<std::shared_ptr<std::vector<float>>> &operand_tensors,
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std::vector<std::vector<int64_t>> &operand_shapes,
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std::shared_ptr<std::vector<float>> &result_tensor,
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std::vector<int64_t> &result_shape) {
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int in = operand_shapes[0][0];
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int ic = operand_shapes[0][1];
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int ih = operand_shapes[0][2];
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int iw = operand_shapes[0][3];
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int oh = result_shape[2];
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int ow = result_shape[3];
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float *data = operand_tensors[0]->data();
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float *mask = operand_tensors[1]->data();
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float *output = result_tensor->data();
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auto scale = param.get<int>("scale");
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auto unpool_h = param.get<int>("unpool_h");
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auto unpool_w = param.get<int>("unpool_w");
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assert(oh == unpool_h);
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assert(ow == unpool_w);
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int sh = ih * scale;
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int sw = iw * scale;
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// always use float to store int8 value
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std::vector<float> tmp_out(in * ic * sh * sw);
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for (int n = 0; n < in; n++) {
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for (int c = 0; c < ic; c++) {
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for (int h = 0; h < sh; h++) {
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for (int w = 0; w < sw; w++) {
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int isw = w / scale;
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int ish = h / scale;
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int out_idx = ((n * ic + c) * sh + h) * sw + w;
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int in_idx = ((n * ic + c) * ih + ish) * iw + isw;
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tmp_out[out_idx] = data[in_idx] * mask[out_idx];
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}
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}
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}
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}
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for (int n = 0; n < in; n++) {
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for (int c = 0; c < ic; c++) {
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for (int h = 0; h < oh; h++) {
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for (int w = 0; w < ow; w++) {
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int out_idx = ((n * ic + c) * oh + h) * ow + w;
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int in_idx = ((n * ic + c) * sh + h) * sw + w;
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output[out_idx] = tmp_out[in_idx];
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}
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}
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}
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}
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}
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};
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} // namespace cvi
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#endif
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