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68 lines
3.2 KiB
68 lines
3.2 KiB
#ifndef CONVOLUTIONAL_LAYER_H |
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#define CONVOLUTIONAL_LAYER_H |
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#include "dark_cuda.h" |
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#include "image.h" |
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#include "activations.h" |
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#include "layer.h" |
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#include "network.h" |
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typedef layer convolutional_layer; |
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#ifdef __cplusplus |
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extern "C" { |
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#endif |
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#ifdef GPU |
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void forward_convolutional_layer_gpu(convolutional_layer layer, network_state state); |
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void backward_convolutional_layer_gpu(convolutional_layer layer, network_state state); |
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void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float learning_rate, float momentum, float decay, float loss_scale); |
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void push_convolutional_layer(convolutional_layer layer); |
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void pull_convolutional_layer(convolutional_layer layer); |
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void add_bias_gpu(float *output, float *biases, int batch, int n, int size); |
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void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size); |
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#ifdef CUDNN |
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void cudnn_convolutional_setup(layer *l, int cudnn_preference, size_t workspace_size_specify); |
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void create_convolutional_cudnn_tensors(layer *l); |
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void cuda_convert_f32_to_f16(float* input_f32, size_t size, float *output_f16); |
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#endif |
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#endif |
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void free_convolutional_batchnorm(convolutional_layer *l); |
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size_t get_convolutional_workspace_size(layer l); |
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convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride_x, int stride_y, int dilation, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index, int antialiasing, convolutional_layer *share_layer, int assisted_excitation, int deform, int train); |
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void denormalize_convolutional_layer(convolutional_layer l); |
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void set_specified_workspace_limit(convolutional_layer *l, size_t workspace_size_limit); |
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void resize_convolutional_layer(convolutional_layer *layer, int w, int h); |
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void forward_convolutional_layer(const convolutional_layer layer, network_state state); |
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void update_convolutional_layer(convolutional_layer layer, int batch, float learning_rate, float momentum, float decay); |
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image *visualize_convolutional_layer(convolutional_layer layer, char *window, image *prev_weights); |
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void binarize_weights(float *weights, int n, int size, float *binary); |
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void swap_binary(convolutional_layer *l); |
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void binarize_weights2(float *weights, int n, int size, char *binary, float *scales); |
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void binary_align_weights(convolutional_layer *l); |
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void backward_convolutional_layer(convolutional_layer layer, network_state state); |
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void add_bias(float *output, float *biases, int batch, int n, int size); |
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void backward_bias(float *bias_updates, float *delta, int batch, int n, int size); |
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image get_convolutional_image(convolutional_layer layer); |
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image get_convolutional_delta(convolutional_layer layer); |
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image get_convolutional_weight(convolutional_layer layer, int i); |
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int convolutional_out_height(convolutional_layer layer); |
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int convolutional_out_width(convolutional_layer layer); |
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void rescale_weights(convolutional_layer l, float scale, float trans); |
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void rgbgr_weights(convolutional_layer l); |
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void assisted_excitation_forward(convolutional_layer l, network_state state); |
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void assisted_excitation_forward_gpu(convolutional_layer l, network_state state); |
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#ifdef __cplusplus |
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} |
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#endif |
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#endif
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