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iVS3D v2.0.0
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Namespace for utility functions related to neural networks and tensors. More...
Functions | |
| template<typename ReduceOp > | |
| auto | bind_reduce (ReduceOp op, int axis) |
| Binds a reduction operation on a specified axis for a tensor. | |
| template<typename ReduceIndexOp > | |
| auto | bind_reduceWithIndex (ReduceIndexOp op, int axis) |
| Binds a reduction operation with index on a specified axis for a tensor. | |
| template<typename Func > | |
| auto | bind_map (Func f) |
| Binds a mapping operation on a tensor. | |
| template<typename Func > | |
| auto | bind_map (Func f, int axis) |
| Binds a mapping operation on a tensor that converts a each element to an array of new elements. | |
| template<typename... Args> | |
| auto | bind_reshape (const std::vector< int64_t > &newShape) |
| Binds a reshape operation to change the shape of a tensor. | |
| template<typename... Args> | |
| auto | bind_squeeze () |
| Binds a squeeze operation to remove dimensions of size 1 from a tensor. | |
| template<typename... Args> | |
| auto | bind_squeeze (int64_t axis) |
| Binds a squeeze operation on a tensor along a specified axis. | |
| template<typename... Args> | |
| auto | bind_unsqueeze (int64_t axis) |
| Binds an unsqueeze operation to add a new dimension of size 1 at a specified axis in a tensor. | |
| template<typename... Args> | |
| auto | bind_toCvMat () |
| Binds a conversion operation from a tensor to an OpenCV Mat format. | |
| template<typename T , typename... Args> | |
| auto | bind_toVector () |
| Binds a conversion operation from a tensor to a vector. | |
| template<typename... Args> | |
| auto | bind_inference (NN::NeuralNetPtr model) |
| Binds an inference operation on a neural network model. | |
Namespace for utility functions related to neural networks and tensors.
This namespace contains functions to bind tensor operations such as reduction, mapping, reshaping, squeezing, unsqueezing, and converting to OpenCV Mat format. These functions are designed to be used with the Tensor class and can be easily integrated into neural network workflows. The functions return callable objects that can be used with tl::expected::and_then for chaining operations.