summaryrefslogtreecommitdiffstats
path: root/ml/dlib/dlib/dnn/cpu_dlib.h
diff options
context:
space:
mode:
Diffstat (limited to 'ml/dlib/dlib/dnn/cpu_dlib.h')
-rw-r--r--ml/dlib/dlib/dnn/cpu_dlib.h505
1 files changed, 0 insertions, 505 deletions
diff --git a/ml/dlib/dlib/dnn/cpu_dlib.h b/ml/dlib/dlib/dnn/cpu_dlib.h
deleted file mode 100644
index 330df01a2..000000000
--- a/ml/dlib/dlib/dnn/cpu_dlib.h
+++ /dev/null
@@ -1,505 +0,0 @@
-// Copyright (C) 2015 Davis E. King (davis@dlib.net)
-// License: Boost Software License See LICENSE.txt for the full license.
-#ifndef DLIB_DNN_CPU_H_
-#define DLIB_DNN_CPU_H_
-
-// This file contains CPU implementations of the GPU based functions in cuda_dlib.h
-// and cudnn_dlibapi.h
-
-#include "tensor.h"
-#include "../geometry/rectangle.h"
-
-namespace dlib
-{
- namespace cpu
- {
-
- // -----------------------------------------------------------------------------------
-
- void multiply (
- bool add_to,
- tensor& dest,
- const tensor& src1,
- const tensor& src2
- );
-
- void multiply_conv (
- bool add_to,
- tensor& dest,
- const tensor& src1,
- const tensor& src2
- );
-
- void multiply_zero_padded (
- bool add_to,
- tensor& dest,
- const tensor& src1,
- const tensor& src2
- );
-
- void scale_channels (
- bool add_to,
- tensor& dest,
- const tensor& src,
- const tensor& scales
- );
-
- void add(
- float beta,
- tensor& dest,
- float alpha,
- const tensor& src
- );
-
- void assign_bias_gradient (
- tensor& grad,
- const tensor& gradient_input
- );
-
- void add (
- tensor& dest,
- const tensor& src1,
- const tensor& src2
- );
-
- void assign_conv_bias_gradient (
- tensor& grad,
- const tensor& gradient_input
- );
-
- // -----------------------------------------------------------------------------------
-
- void affine_transform(
- tensor& dest,
- const tensor& src,
- const float A,
- const float B
- );
-
- void affine_transform(
- tensor& dest,
- const tensor& src1,
- const tensor& src2,
- const float A,
- const float B,
- const float C
- );
-
- void affine_transform(
- tensor& dest,
- const tensor& src1,
- const tensor& src2,
- const tensor& src3,
- const float A,
- const float B,
- const float C,
- const float D
- );
-
- void affine_transform_range(
- size_t begin,
- size_t end,
- tensor& dest,
- const tensor& src1,
- const tensor& src2,
- const tensor& src3,
- const float A,
- const float B,
- const float C
- );
-
- // -----------------------------------------------------------------------------------
-
- void affine_transform(
- tensor& dest,
- const tensor& src,
- const tensor& A,
- const tensor& B
- );
-
- // -----------------------------------------------------------------------------------
-
- void affine_transform_conv(
- tensor& dest,
- const tensor& src,
- const tensor& A,
- const tensor& B
- );
-
- // -----------------------------------------------------------------------------------
-
- void affine_transform(
- const rectangle& rect,
- tensor& dest,
- const tensor& src1,
- const tensor& src2,
- const tensor& src3,
- float A,
- float B,
- float C
- );
-
- // -----------------------------------------------------------------------------------
-
- void compute_adam_update (
- size_t begin,
- size_t end,
- tensor& s,
- tensor& m,
- tensor& v,
- const float t,
- const float learning_rate,
- const float weight_decay,
- const float momentum1,
- const float momentum2,
- const tensor& params,
- const tensor& params_grad
- );
-
- // -----------------------------------------------------------------------------------
-
- void batch_normalize_inference (
- const double eps,
- resizable_tensor& dest,
- const tensor& src,
- const tensor& gamma,
- const tensor& beta,
- const tensor& running_means,
- const tensor& running_variances
- );
-
- void batch_normalize (
- const double eps,
- resizable_tensor& dest,
- resizable_tensor& means,
- resizable_tensor& invstds,
- const double averaging_factor,
- resizable_tensor& running_means,
- resizable_tensor& running_variances,
- const tensor& src,
- const tensor& gamma,
- const tensor& beta
- );
-
- void batch_normalize_gradient (
- const double eps,
- const tensor& gradient_input,
- const tensor& means,
- const tensor& invstds,
- const tensor& src,
- const tensor& gamma,
- tensor& src_grad,
- tensor& gamma_grad,
- tensor& beta_grad
- );
-
- void batch_normalize_conv_inference (
- const double eps,
- resizable_tensor& dest,
- const tensor& src,
- const tensor& gamma,
- const tensor& beta,
- const tensor& running_means,
- const tensor& running_variances
- );
-
- void batch_normalize_conv (
- const double eps,
- resizable_tensor& dest,
- resizable_tensor& means,
- resizable_tensor& invstds,
- const double averaging_factor,
- resizable_tensor& running_means,
- resizable_tensor& running_variances,
- const tensor& src,
- const tensor& gamma,
- const tensor& beta
- );
-
- void batch_normalize_conv_gradient (
- const double eps,
- const tensor& gradient_input,
- const tensor& means,
- const tensor& invstds,
- const tensor& src,
- const tensor& gamma,
- tensor& src_grad,
- tensor& gamma_grad,
- tensor& beta_grad
- );
-
- // -----------------------------------------------------------------------------------
-
- void threshold (
- tensor& data,
- float thresh
- );
-
- void dot (
- const tensor& a,
- const tensor& b,
- tensor& result,
- size_t idx
- );
-
- // -----------------------------------------------------------------------------------
-
- void softmax (
- tensor& dest,
- const tensor& src
- );
-
- void softmax_gradient (
- tensor& grad,
- const tensor& dest,
- const tensor& gradient_input
- );
-
- // ------------------------------------------------------------------------------------
-
- void softmax_all (
- tensor& dest,
- const tensor& src
- );
-
- void softmax_all_gradient (
- tensor& grad,
- const tensor& dest,
- const tensor& gradient_input
- );
-
- // ------------------------------------------------------------------------------------
-
- void sigmoid (
- tensor& dest,
- const tensor& src
- );
-
- void sigmoid_gradient (
- tensor& grad,
- const tensor& dest,
- const tensor& gradient_input
- );
-
- // ------------------------------------------------------------------------------------
-
- void relu (
- tensor& dest,
- const tensor& src
- );
-
- void relu_gradient (
- tensor& grad,
- const tensor& dest,
- const tensor& gradient_input
- );
-
- // ----------------------------------------------------------------------------------------
-
- void prelu (
- tensor& dest,
- const tensor& src,
- const tensor& param
- );
-
- void prelu_gradient (
- tensor& grad,
- const tensor& src,
- const tensor& gradient_input,
- const tensor& param,
- tensor& params_grad
- );
-
- // ------------------------------------------------------------------------------------
-
- void tanh (
- tensor& dest,
- const tensor& src
- );
-
- void tanh_gradient (
- tensor& grad,
- const tensor& dest,
- const tensor& gradient_input
- );
-
- // ----------------------------------------------------------------------------------------
-
- void resize_bilinear (
- tensor& dest,
- long dest_row_stride,
- long dest_channel_stride,
- const tensor& src,
- long src_row_stride,
- long src_channel_stride
- );
-
- void resize_bilinear_gradient (
- tensor& grad,
- long grad_row_stride,
- long grad_channel_stride,
- const tensor& gradient_input,
- long gradient_input_row_stride,
- long gradient_input_channel_stride
- );
-
- inline void resize_bilinear (
- tensor& dest,
- const tensor& src
- ) { resize_bilinear(dest, dest.nc(), dest.nr()*dest.nc(), src, src.nc(), src.nr()*src.nc()); }
-
- inline void resize_bilinear_gradient (
- tensor& grad,
- const tensor& gradient_input
- ) { resize_bilinear_gradient(grad, grad.nc(), grad.nr()*grad.nc(), gradient_input, gradient_input.nc(), gradient_input.nr()*gradient_input.nc()); }
-
- // -----------------------------------------------------------------------------------
-
- class pooling
- {
- public:
-
- pooling(const pooling&) = delete;
- pooling& operator=(const pooling&) = delete;
-
- pooling (
- );
-
- void clear(
- );
-
- void setup_max_pooling(
- int window_height,
- int window_width,
- int stride_y,
- int stride_x,
- int padding_y,
- int padding_x
- );
-
- void setup_avg_pooling(
- int window_height,
- int window_width,
- int stride_y,
- int stride_x,
- int padding_y,
- int padding_x
- );
-
- bool does_max_pooling(
- ) const { return do_max_pooling; }
-
- void operator() (
- resizable_tensor& dest,
- const tensor& src
- );
-
- void get_gradient(
- const tensor& gradient_input,
- const tensor& dest,
- const tensor& src,
- tensor& grad
- );
-
- private:
- int window_height;
- int window_width;
- int stride_y;
- int stride_x;
- int padding_y;
- int padding_x;
- bool do_max_pooling;
-
- };
-
- // -----------------------------------------------------------------------------------
-
- class tensor_conv
- {
- public:
- tensor_conv(const tensor_conv&) = delete;
- tensor_conv& operator=(const tensor_conv&) = delete;
-
- tensor_conv() {}
-
- void clear(
- ) {}
-
- void setup(
- const tensor& data, /* not used but required for interface */
- const tensor& filters, /* not used but required for interface */
- int stride_y,
- int stride_x,
- int padding_y,
- int padding_x
- )
- {
- (void)data; /* silence compiler */
- DLIB_CASSERT(stride_y > 0 && stride_x > 0);
- DLIB_CASSERT(0 <= padding_y && padding_y < filters.nr());
- DLIB_CASSERT(0 <= padding_x && padding_x < filters.nc());
- last_stride_y = stride_y;
- last_stride_x = stride_x;
- last_padding_y = padding_y;
- last_padding_x = padding_x;
- }
-
- void operator() (
- const bool add_to_output,
- resizable_tensor& output,
- const tensor& data,
- const tensor& filters
- );
-
- void operator() (
- const bool add_to_output,
- tensor& output,
- const tensor& data,
- const tensor& filters
- );
-
- void get_gradient_for_data (
- const bool add_to_output,
- const tensor& gradient_input,
- const tensor& filters,
- tensor& data_gradient
- );
-
- void get_gradient_for_filters (
- const bool add_to_output,
- const tensor& gradient_input,
- const tensor& data,
- tensor& filters_gradient
- );
-
- private:
-
- long last_stride_y = 0;
- long last_stride_x = 0;
- long last_padding_y = 0;
- long last_padding_x = 0;
- };
-
- // -----------------------------------------------------------------------------------
-
- void copy_tensor(
- bool add_to,
- tensor& dest,
- size_t dest_k_offset,
- const tensor& src,
- size_t src_k_offset,
- size_t count_k
- );
-
- // -----------------------------------------------------------------------------------
-
- }
-}
-
-#ifdef NO_MAKEFILE
-#include "cpu_dlib.cpp"
-#endif
-
-#endif // DLIB_DNN_CPU_H_
-
-
generated by cgit v1.2.3 (git 2.39.1) at 2024-05-19 04:38:51 +0000