[phi] move yolov3_loss to phi #40944
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你的PR提交成功，感谢你对开源项目的贡献! |
wuyefeilin
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YuanRisheng
reviewed
Mar 28, 2022
| const T* loss_grad_data = loss_grad.data<T>(); | ||
| const T* obj_mask_data = objness_mask->data<T>(); | ||
| const int* gt_match_mask_data = gt_match_mask.data<int>(); | ||
| T* input_grad_data = |
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可以先调用resize，再使用dev_ctx的Alloc方法进行内存分配
| const T* gt_score_data; | ||
| DenseTensor gtscore; | ||
| if (!(gt_score.is_initialized())) { | ||
| gtscore.mutable_data<T>({n, b}, dev_ctx.GetPlace()); |
| const T* input_data = input->data<T>(); | ||
| const T* gt_box_data = gt_box.data<T>(); | ||
| const int* gt_label_data = gt_label.data<int>(); | ||
| T* loss_data = loss->mutable_data<T>({n}, dev_ctx.GetPlace()); |
| T* loss_data = loss->mutable_data<T>({n}, dev_ctx.GetPlace()); | ||
| memset(loss_data, 0, loss->numel() * sizeof(T)); | ||
| T* obj_mask_data = | ||
| objness_mask->mutable_data<T>({n, mask_num, h, w}, dev_ctx.GetPlace()); |
| objness_mask->mutable_data<T>({n, mask_num, h, w}, dev_ctx.GetPlace()); | ||
| memset(obj_mask_data, 0, objness_mask->numel() * sizeof(T)); | ||
| int* gt_match_mask_data = | ||
| gt_match_mask->mutable_data<int>({n, b}, dev_ctx.GetPlace()); |
| const T* gt_score_data; | ||
| DenseTensor gtscore; | ||
| if (!(gt_score.is_initialized())) { | ||
| gtscore.mutable_data<T>({n, b}, dev_ctx.GetPlace()); |
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| // calc valid gt box mask, avoid calc duplicately in following code | ||
| DenseTensor gt_valid_mask; | ||
| bool* gt_valid_mask_data = |
zyfncg
reviewed
Mar 28, 2022
| #include "paddle/phi/backends/cpu/cpu_context.h" | ||
| #include "paddle/phi/core/kernel_registry.h" | ||
| #include "paddle/phi/kernels/funcs/math_function.h" | ||
| #include "paddle/phi/kernels/yolov3_loss_grad_kernel.h" |
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yolov3_loss_grad_kernel.h放在开头
| #include "paddle/phi/backends/cpu/cpu_context.h" | ||
| #include "paddle/phi/core/kernel_registry.h" | ||
| #include "paddle/phi/kernels/funcs/math_function.h" | ||
| #include "paddle/phi/kernels/yolov3_loss_kernel.h" |
| const DenseTensor& x, | ||
| const DenseTensor& gt_box, | ||
| const DenseTensor& gt_label, | ||
| const paddle::optional<const DenseTensor&> gt_score, |
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| static inline Box<T> GetGtBox(const T* gt, int batch, int max_boxes, int idx) { | ||
| Box<T> b; | ||
| b.x = gt[(batch * max_boxes + idx) * 4]; | ||
| b.y = gt[(batch * max_boxes + idx) * 4 + 1]; | ||
| b.w = gt[(batch * max_boxes + idx) * 4 + 2]; | ||
| b.h = gt[(batch * max_boxes + idx) * 4 + 3]; | ||
| return b; | ||
| } | ||
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| template <typename T> | ||
| static inline T BoxOverlap(T c1, T w1, T c2, T w2) { | ||
| T l1 = c1 - w1 / 2.0; | ||
| T l2 = c2 - w2 / 2.0; | ||
| T left = l1 > l2 ? l1 : l2; | ||
| T r1 = c1 + w1 / 2.0; | ||
| T r2 = c2 + w2 / 2.0; | ||
| T right = r1 < r2 ? r1 : r2; | ||
| return right - left; | ||
| } | ||
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| template <typename T> | ||
| static inline T CalcBoxIoU(Box<T> b1, Box<T> b2) { | ||
| T w = BoxOverlap(b1.x, b1.w, b2.x, b2.w); | ||
| T h = BoxOverlap(b1.y, b1.h, b2.y, b2.h); | ||
| T inter_area = (w < 0 || h < 0) ? 0.0 : w * h; | ||
| T union_area = b1.w * b1.h + b2.w * b2.h - inter_area; | ||
| return inter_area / union_area; | ||
| } | ||
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| static inline int GetEntryIndex(int batch, | ||
| int an_idx, | ||
| int hw_idx, | ||
| int an_num, | ||
| int an_stride, | ||
| int stride, | ||
| int entry) { | ||
| return (batch * an_num + an_idx) * an_stride + entry * stride + hw_idx; | ||
| } | ||
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| template <typename T> | ||
| static void CalcBoxLocationLoss(T* loss, | ||
| const T* input, | ||
| Box<T> gt, | ||
| std::vector<int> anchors, | ||
| int an_idx, | ||
| int box_idx, | ||
| int gi, | ||
| int gj, | ||
| int grid_size, | ||
| int input_size, | ||
| int stride, | ||
| T score) { |
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像GetGtBox这些前反向都用到的函数可以统一写到一个cpu目录下的yoloxxx_functor.h文件中，避免维护两份重复的代码
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fix as review |
zyfncg
previously approved these changes
Mar 28, 2022
chenwhql
approved these changes
Mar 30, 2022
MingMingShangTian
approved these changes
Mar 31, 2022
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move yolov3_loss to phi