#include "convolutional_layer.h"
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#include "utils.h"
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#include "batchnorm_layer.h"
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#include "im2col.h"
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#include "col2im.h"
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#include "blas.h"
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#include "gemm.h"
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#include "box.h"
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#include <stdio.h>
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#include <time.h>
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#ifdef AI2
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#include "xnor_layer.h"
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#endif
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#ifdef __cplusplus
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#define PUT_IN_REGISTER
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#else
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#define PUT_IN_REGISTER register
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#endif
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#ifndef AI2
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#define AI2 0
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void forward_xnor_layer(layer l, network_state state);
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#endif
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void swap_binary(convolutional_layer *l)
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{
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float *swap = l->weights;
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l->weights = l->binary_weights;
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l->binary_weights = swap;
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#ifdef GPU
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swap = l->weights_gpu;
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l->weights_gpu = l->binary_weights_gpu;
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l->binary_weights_gpu = swap;
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#endif
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}
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void binarize_weights(float *weights, int n, int size, float *binary)
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{
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int i, f;
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for(f = 0; f < n; ++f){
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float mean = 0;
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for(i = 0; i < size; ++i){
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mean += fabs(weights[f*size + i]);
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}
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mean = mean / size;
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for(i = 0; i < size; ++i){
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binary[f*size + i] = (weights[f*size + i] > 0) ? mean: -mean;
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}
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}
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}
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void binarize_cpu(float *input, int n, float *binary)
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{
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int i;
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for(i = 0; i < n; ++i){
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binary[i] = (input[i] > 0) ? 1 : -1;
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}
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}
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void binarize_input(float *input, int n, int size, float *binary)
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{
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int i, s;
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for(s = 0; s < size; ++s){
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float mean = 0;
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for(i = 0; i < n; ++i){
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mean += fabs(input[i*size + s]);
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}
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mean = mean / n;
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for(i = 0; i < n; ++i){
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binary[i*size + s] = (input[i*size + s] > 0) ? mean : -mean;
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}
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}
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}
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int convolutional_out_height(convolutional_layer l)
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{
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return (l.h + 2*l.pad - l.size) / l.stride_y + 1;
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}
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int convolutional_out_width(convolutional_layer l)
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{
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return (l.w + 2*l.pad - l.size) / l.stride_x + 1;
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}
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image get_convolutional_image(convolutional_layer l)
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{
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int h,w,c;
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h = convolutional_out_height(l);
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w = convolutional_out_width(l);
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c = l.n;
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return float_to_image(w,h,c,l.output);
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}
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image get_convolutional_delta(convolutional_layer l)
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{
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int h,w,c;
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h = convolutional_out_height(l);
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w = convolutional_out_width(l);
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c = l.n;
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return float_to_image(w,h,c,l.delta);
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}
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size_t get_workspace_size32(layer l){
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#ifdef CUDNN
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if(gpu_index >= 0){
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size_t most = 0;
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size_t s = 0;
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CHECK_CUDNN(cudnnGetConvolutionForwardWorkspaceSize(cudnn_handle(),
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l.srcTensorDesc,
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l.weightDesc,
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l.convDesc,
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l.dstTensorDesc,
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l.fw_algo,
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&s));
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if (s > most) most = s;
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CHECK_CUDNN(cudnnGetConvolutionBackwardFilterWorkspaceSize(cudnn_handle(),
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l.srcTensorDesc,
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l.ddstTensorDesc,
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l.convDesc,
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l.dweightDesc,
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l.bf_algo,
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&s));
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if (s > most && l.train) most = s;
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CHECK_CUDNN(cudnnGetConvolutionBackwardDataWorkspaceSize(cudnn_handle(),
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l.weightDesc,
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l.ddstTensorDesc,
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l.convDesc,
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l.dsrcTensorDesc,
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l.bd_algo,
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&s));
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if (s > most && l.train) most = s;
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return most;
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}
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#endif
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if (l.xnor) {
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size_t re_packed_input_size = l.c * l.w * l.h * sizeof(float);
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size_t workspace_size = (size_t)l.bit_align*l.size*l.size*l.c * sizeof(float);
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if (workspace_size < re_packed_input_size) workspace_size = re_packed_input_size;
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return workspace_size;
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}
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return (size_t)l.out_h*l.out_w*l.size*l.size*(l.c / l.groups)*sizeof(float);
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}
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size_t get_workspace_size16(layer l) {
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#if defined(CUDNN) && defined(CUDNN_HALF)
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if (gpu_index >= 0) {
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size_t most = 0;
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size_t s = 0;
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CHECK_CUDNN(cudnnGetConvolutionForwardWorkspaceSize(cudnn_handle(),
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l.srcTensorDesc16,
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l.weightDesc16,
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l.convDesc,
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l.dstTensorDesc16,
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l.fw_algo16,
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&s));
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if (s > most) most = s;
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CHECK_CUDNN(cudnnGetConvolutionBackwardFilterWorkspaceSize(cudnn_handle(),
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l.srcTensorDesc16,
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l.ddstTensorDesc16,
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l.convDesc,
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l.dweightDesc16,
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l.bf_algo16,
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&s));
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if (s > most && l.train) most = s;
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CHECK_CUDNN(cudnnGetConvolutionBackwardDataWorkspaceSize(cudnn_handle(),
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l.weightDesc16,
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l.ddstTensorDesc16,
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l.convDesc,
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l.dsrcTensorDesc16,
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l.bd_algo16,
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&s));
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if (s > most && l.train) most = s;
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return most;
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}
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#endif
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return 0;
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//if (l.xnor) return (size_t)l.bit_align*l.size*l.size*l.c * sizeof(float);
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//return (size_t)l.out_h*l.out_w*l.size*l.size*l.c * sizeof(float);
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}
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size_t get_convolutional_workspace_size(layer l) {
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size_t workspace_size = get_workspace_size32(l);
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size_t workspace_size16 = get_workspace_size16(l);
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if (workspace_size16 > workspace_size) workspace_size = workspace_size16;
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return workspace_size;
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}
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#ifdef GPU
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#ifdef CUDNN
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void create_convolutional_cudnn_tensors(layer *l)
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{
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CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->normTensorDesc));
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CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->normDstTensorDesc));
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CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->srcTensorDesc));
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CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->dstTensorDesc));
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CHECK_CUDNN(cudnnCreateFilterDescriptor(&l->weightDesc));
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CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->dsrcTensorDesc));
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CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->ddstTensorDesc));
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CHECK_CUDNN(cudnnCreateFilterDescriptor(&l->dweightDesc));
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CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->normDstTensorDescF16));
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CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->srcTensorDesc16));
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CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->dstTensorDesc16));
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CHECK_CUDNN(cudnnCreateFilterDescriptor(&l->weightDesc16));
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CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->dsrcTensorDesc16));
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CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->ddstTensorDesc16));
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CHECK_CUDNN(cudnnCreateFilterDescriptor(&l->dweightDesc16));
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CHECK_CUDNN(cudnnCreateConvolutionDescriptor(&l->convDesc));
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}
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void cudnn_convolutional_setup(layer *l, int cudnn_preference, size_t workspace_size_specify)
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{
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// CUDNN_HALF
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// TRUE_HALF_CONFIG is only supported on architectures with true fp16 support (compute capability 5.3 and 6.0):
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// Tegra X1, Jetson TX1, DRIVE CX, DRIVE PX, Quadro GP100, Tesla P100
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// PSEUDO_HALF_CONFIG is required for Tensor Cores - our case!
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cudnnDataType_t data_type = CUDNN_DATA_FLOAT;
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#if(CUDNN_MAJOR >= 7)
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// Tensor Core uses CUDNN_TENSOR_OP_MATH instead of CUDNN_DEFAULT_MATH
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// For *_ALGO_WINOGRAD_NONFUSED can be used CUDNN_DATA_FLOAT
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// otherwise Input, Filter and Output descriptors (xDesc, yDesc, wDesc, dxDesc, dyDesc and dwDesc as applicable) have dataType = CUDNN_DATA_HALF
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// Three techniques for training using Mixed-precision: https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/
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// 1. Accumulation into FP32
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// 2. Loss Scaling - required only for: activation gradients. We do not use.
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// 3. FP32 Master Copy of Weights
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// More: http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#tensor_ops
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if (l->groups < 1) l->groups = 1;
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if (l->stride_x < 1) l->stride_x = 1;
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if (l->stride_y < 1) l->stride_y = 1;
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CHECK_CUDNN(cudnnSetConvolutionGroupCount(l->convDesc, l->groups));
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CHECK_CUDNN(cudnnSetConvolutionMathType(l->convDesc, CUDNN_TENSOR_OP_MATH));
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#if((CUDNN_MAJOR*10 + CUDNN_MINOR) >= 72) // cuDNN >= 7.2
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//CHECK_CUDNN(cudnnSetConvolutionMathType(l->convDesc, CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION)); // reduces the speed of regular and group convolution
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#endif
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#else //if(CUDNN_MAJOR >= 7)
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if (l->groups > 1) {
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error("CUDNN < 7 doesn't support groups, please upgrade!");
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}
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#endif
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// INT8_CONFIG, INT8_EXT_CONFIG, INT8x4_CONFIG and INT8x4_EXT_CONFIG are only supported
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// on architectures with DP4A support (compute capability 6.1 and later).
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//cudnnDataType_t data_type = CUDNN_DATA_INT8;
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// backward delta
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CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->dsrcTensorDesc, CUDNN_TENSOR_NCHW, data_type, l->batch, l->c, l->h, l->w));
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CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->ddstTensorDesc, CUDNN_TENSOR_NCHW, data_type, l->batch, l->out_c, l->out_h, l->out_w));
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CHECK_CUDNN(cudnnSetFilter4dDescriptor(l->dweightDesc, data_type, CUDNN_TENSOR_NCHW, l->n, l->c / l->groups, l->size, l->size));
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// forward
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CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->srcTensorDesc, CUDNN_TENSOR_NCHW, data_type, l->batch, l->c, l->h, l->w));
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CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, data_type, l->batch, l->out_c, l->out_h, l->out_w));
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CHECK_CUDNN(cudnnSetFilter4dDescriptor(l->weightDesc, data_type, CUDNN_TENSOR_NCHW, l->n, l->c / l->groups, l->size, l->size));
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// backward delta
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CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->dsrcTensorDesc16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->c, l->h, l->w));
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CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->ddstTensorDesc16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->out_c, l->out_h, l->out_w));
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CHECK_CUDNN(cudnnSetFilter4dDescriptor(l->dweightDesc16, CUDNN_DATA_HALF, CUDNN_TENSOR_NCHW, l->n, l->c / l->groups, l->size, l->size));
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// forward
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CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->srcTensorDesc16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->c, l->h, l->w));
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CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->dstTensorDesc16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->out_c, l->out_h, l->out_w));
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CHECK_CUDNN(cudnnSetFilter4dDescriptor(l->weightDesc16, CUDNN_DATA_HALF, CUDNN_TENSOR_NCHW, l->n, l->c / l->groups, l->size, l->size));
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// batch norm
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CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->normDstTensorDescF16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->out_c, l->out_h, l->out_w));
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// batch norm
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CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l->out_c, 1, 1));
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CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->normDstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w));
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//printf("\n l->dilation = %d, l->pad = %d, l->size = %d, l->stride = %d, l->stride_x = %d, l->stride_y = %d, l->groups = %d, l->w = %d, l->h = %d, l->c = %d, l->n = %d, l->out_w = %d, l->out_h = %d, l->out_c = %d, l->batch = %d, data_type = %d \n",
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// l->dilation, l->pad, l->size, l->stride, l->stride_x, l->stride_y, l->groups, l->w, l->h, l->c, l->n, l->out_w, l->out_h, l->out_c, l->batch, data_type);
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#if(CUDNN_MAJOR >= 6)
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CHECK_CUDNN(cudnnSetConvolution2dDescriptor(l->convDesc, l->pad * l->dilation, l->pad * l->dilation, l->stride_y, l->stride_x, l->dilation, l->dilation, CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT)); // cudnn >= 6.0
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#else
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CHECK_CUDNN(cudnnSetConvolution2dDescriptor(l->convDesc, l->pad * l->dilation, l->pad * l->dilation, l->stride_y, l->stride_x, l->dilation, l->dilation, CUDNN_CROSS_CORRELATION)); // cudnn 5.1
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#endif
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int forward_algo = CUDNN_CONVOLUTION_FWD_PREFER_FASTEST;
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int backward_algo = CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST;
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int backward_filter = CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST;
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if (cudnn_preference == cudnn_smallest)
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{
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forward_algo = CUDNN_CONVOLUTION_FWD_NO_WORKSPACE;
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backward_algo = CUDNN_CONVOLUTION_BWD_DATA_NO_WORKSPACE;
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backward_filter = CUDNN_CONVOLUTION_BWD_FILTER_NO_WORKSPACE;
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printf(" CUDNN-slow ");
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}
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if (cudnn_preference == cudnn_specify)
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{
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forward_algo = CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT;
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backward_algo = CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT;
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backward_filter = CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT;
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//printf(" CUDNN-specified %zu ", workspace_size_specify);
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}
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CHECK_CUDNN(cudnnGetConvolutionForwardAlgorithm(cudnn_handle(),
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l->srcTensorDesc,
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l->weightDesc,
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l->convDesc,
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l->dstTensorDesc,
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(cudnnConvolutionFwdPreference_t)forward_algo,
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workspace_size_specify,
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&l->fw_algo));
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CHECK_CUDNN(cudnnGetConvolutionBackwardDataAlgorithm(cudnn_handle(),
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l->weightDesc,
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l->ddstTensorDesc,
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l->convDesc,
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l->dsrcTensorDesc,
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(cudnnConvolutionBwdDataPreference_t)backward_algo,
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workspace_size_specify,
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&l->bd_algo));
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CHECK_CUDNN(cudnnGetConvolutionBackwardFilterAlgorithm(cudnn_handle(),
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l->srcTensorDesc,
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l->ddstTensorDesc,
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l->convDesc,
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l->dweightDesc,
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(cudnnConvolutionBwdFilterPreference_t)backward_filter,
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workspace_size_specify,
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&l->bf_algo));
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//if (data_type == CUDNN_DATA_HALF)
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{
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// HALF-16 if(data_type == CUDNN_DATA_HALF)
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l->fw_algo16 = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM;
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l->bd_algo16 = CUDNN_CONVOLUTION_BWD_DATA_ALGO_1;
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l->bf_algo16 = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1;
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// FLOAT-32 if(data_type == CUDNN_DATA_FLOAT)
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//l->fw_algo16 = CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED;
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//l->bd_algo16 = CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD_NONFUSED;
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//l->bf_algo16 = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD_NONFUSED;
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}
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}
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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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{
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if (!l->share_layer) {
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if (l->scales) free(l->scales), l->scales = NULL;
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if (l->scale_updates) free(l->scale_updates), l->scale_updates = NULL;
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if (l->mean) free(l->mean), l->mean = NULL;
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if (l->variance) free(l->variance), l->variance = NULL;
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if (l->mean_delta) free(l->mean_delta), l->mean_delta = NULL;
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if (l->variance_delta) free(l->variance_delta), l->variance_delta = NULL;
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if (l->rolling_mean) free(l->rolling_mean), l->rolling_mean = NULL;
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if (l->rolling_variance) free(l->rolling_variance), l->rolling_variance = NULL;
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if (l->x) free(l->x), l->x = NULL;
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if (l->x_norm) free(l->x_norm), l->x_norm = NULL;
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#ifdef GPU
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if (l->scales_gpu) cuda_free(l->scales_gpu), l->scales_gpu = NULL;
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if (l->scale_updates_gpu) cuda_free(l->scale_updates_gpu), l->scale_updates_gpu = NULL;
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if (l->mean_gpu) cuda_free(l->mean_gpu), l->mean_gpu = NULL;
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if (l->variance_gpu) cuda_free(l->variance_gpu), l->variance_gpu = NULL;
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if (l->mean_delta_gpu) cuda_free(l->mean_delta_gpu), l->mean_delta_gpu = NULL;
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if (l->variance_delta_gpu) cuda_free(l->variance_delta_gpu), l->variance_delta_gpu = NULL;
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if (l->rolling_mean_gpu) cuda_free(l->rolling_mean_gpu), l->rolling_mean_gpu = NULL;
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if (l->rolling_variance_gpu) cuda_free(l->rolling_variance_gpu), l->rolling_variance_gpu = NULL;
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if (l->x_gpu) cuda_free(l->x_gpu), l->x_gpu = NULL;
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if (l->x_norm_gpu) cuda_free(l->x_norm_gpu), l->x_norm_gpu = NULL;
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#endif
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}
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}
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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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int total_batch = batch*steps;
|
int i;
|
convolutional_layer l = { (LAYER_TYPE)0 };
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l.type = CONVOLUTIONAL;
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l.train = train;
|
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if (xnor) groups = 1; // disable groups for XNOR-net
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if (groups < 1) groups = 1;
|
|
const int blur_stride_x = stride_x;
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const int blur_stride_y = stride_y;
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l.antialiasing = antialiasing;
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if (antialiasing) {
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stride_x = stride_y = l.stride = l.stride_x = l.stride_y = 1; // use stride=1 in host-layer
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}
|
|
l.deform = deform;
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l.assisted_excitation = assisted_excitation;
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l.share_layer = share_layer;
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l.index = index;
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l.h = h;
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l.w = w;
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l.c = c;
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l.groups = groups;
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l.n = n;
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l.binary = binary;
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l.xnor = xnor;
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l.use_bin_output = use_bin_output;
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l.batch = batch;
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l.steps = steps;
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l.stride = stride_x;
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l.stride_x = stride_x;
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l.stride_y = stride_y;
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l.dilation = dilation;
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l.size = size;
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l.pad = padding;
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l.batch_normalize = batch_normalize;
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l.learning_rate_scale = 1;
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l.nweights = (c / groups) * n * size * size;
|
|
if (l.share_layer) {
|
if (l.size != l.share_layer->size || l.nweights != l.share_layer->nweights || l.c != l.share_layer->c || l.n != l.share_layer->n) {
|
printf(" Layer size, nweights, channels or filters don't match for the share_layer");
|
getchar();
|
}
|
|
l.weights = l.share_layer->weights;
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l.weight_updates = l.share_layer->weight_updates;
|
|
l.biases = l.share_layer->biases;
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l.bias_updates = l.share_layer->bias_updates;
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}
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else {
|
l.weights = (float*)xcalloc(l.nweights, sizeof(float));
|
l.biases = (float*)xcalloc(n, sizeof(float));
|
|
if (train) {
|
l.weight_updates = (float*)xcalloc(l.nweights, sizeof(float));
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l.bias_updates = (float*)xcalloc(n, sizeof(float));
|
}
|
}
|
|
// float scale = 1./sqrt(size*size*c);
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float scale = sqrt(2./(size*size*c/groups));
|
if (l.activation == NORM_CHAN || l.activation == NORM_CHAN_SOFTMAX || l.activation == NORM_CHAN_SOFTMAX_MAXVAL) {
|
for (i = 0; i < l.nweights; ++i) l.weights[i] = 1; // rand_normal();
|
}
|
else {
|
for (i = 0; i < l.nweights; ++i) l.weights[i] = scale*rand_uniform(-1, 1); // rand_normal();
|
}
|
int out_h = convolutional_out_height(l);
|
int out_w = convolutional_out_width(l);
|
l.out_h = out_h;
|
l.out_w = out_w;
|
l.out_c = n;
|
l.outputs = l.out_h * l.out_w * l.out_c;
|
l.inputs = l.w * l.h * l.c;
|
l.activation = activation;
|
|
l.output = (float*)xcalloc(total_batch*l.outputs, sizeof(float));
|
#ifndef GPU
|
if (train) l.delta = (float*)xcalloc(total_batch*l.outputs, sizeof(float));
|
#endif // not GPU
|
|
l.forward = forward_convolutional_layer;
|
l.backward = backward_convolutional_layer;
|
l.update = update_convolutional_layer;
|
if(binary){
|
l.binary_weights = (float*)xcalloc(l.nweights, sizeof(float));
|
l.cweights = (char*)xcalloc(l.nweights, sizeof(char));
|
l.scales = (float*)xcalloc(n, sizeof(float));
|
}
|
if(xnor){
|
l.binary_weights = (float*)xcalloc(l.nweights, sizeof(float));
|
l.binary_input = (float*)xcalloc(l.inputs * l.batch, sizeof(float));
|
|
int align = 32;// 8;
|
int src_align = l.out_h*l.out_w;
|
l.bit_align = src_align + (align - src_align % align);
|
|
l.mean_arr = (float*)xcalloc(l.n, sizeof(float));
|
|
const size_t new_c = l.c / 32;
|
size_t in_re_packed_input_size = new_c * l.w * l.h + 1;
|
l.bin_re_packed_input = (uint32_t*)xcalloc(in_re_packed_input_size, sizeof(uint32_t));
|
|
l.lda_align = 256; // AVX2
|
int k = l.size*l.size*l.c;
|
size_t k_aligned = k + (l.lda_align - k%l.lda_align);
|
size_t t_bit_input_size = k_aligned * l.bit_align / 8;
|
l.t_bit_input = (char*)xcalloc(t_bit_input_size, sizeof(char));
|
}
|
|
if(batch_normalize){
|
if (l.share_layer) {
|
l.scales = l.share_layer->scales;
|
l.scale_updates = l.share_layer->scale_updates;
|
l.mean = l.share_layer->mean;
|
l.variance = l.share_layer->variance;
|
l.mean_delta = l.share_layer->mean_delta;
|
l.variance_delta = l.share_layer->variance_delta;
|
l.rolling_mean = l.share_layer->rolling_mean;
|
l.rolling_variance = l.share_layer->rolling_variance;
|
}
|
else {
|
l.scales = (float*)xcalloc(n, sizeof(float));
|
for (i = 0; i < n; ++i) {
|
l.scales[i] = 1;
|
}
|
if (train) {
|
l.scale_updates = (float*)xcalloc(n, sizeof(float));
|
|
l.mean = (float*)xcalloc(n, sizeof(float));
|
l.variance = (float*)xcalloc(n, sizeof(float));
|
|
l.mean_delta = (float*)xcalloc(n, sizeof(float));
|
l.variance_delta = (float*)xcalloc(n, sizeof(float));
|
}
|
l.rolling_mean = (float*)xcalloc(n, sizeof(float));
|
l.rolling_variance = (float*)xcalloc(n, sizeof(float));
|
}
|
|
#ifndef GPU
|
if (train) {
|
l.x = (float*)xcalloc(total_batch * l.outputs, sizeof(float));
|
l.x_norm = (float*)xcalloc(total_batch * l.outputs, sizeof(float));
|
}
|
#endif // not GPU
|
}
|
|
#ifndef GPU
|
if (l.activation == SWISH || l.activation == MISH) l.activation_input = (float*)calloc(total_batch*l.outputs, sizeof(float));
|
#endif // not GPU
|
|
if(adam){
|
l.adam = 1;
|
l.m = (float*)xcalloc(l.nweights, sizeof(float));
|
l.v = (float*)xcalloc(l.nweights, sizeof(float));
|
l.bias_m = (float*)xcalloc(n, sizeof(float));
|
l.scale_m = (float*)xcalloc(n, sizeof(float));
|
l.bias_v = (float*)xcalloc(n, sizeof(float));
|
l.scale_v = (float*)xcalloc(n, sizeof(float));
|
}
|
|
#ifdef GPU
|
|
|
l.forward_gpu = forward_convolutional_layer_gpu;
|
l.backward_gpu = backward_convolutional_layer_gpu;
|
l.update_gpu = update_convolutional_layer_gpu;
|
|
if(gpu_index >= 0){
|
|
if (l.activation == SWISH || l.activation == MISH) {
|
l.activation_input_gpu = cuda_make_array(l.activation_input, total_batch*l.outputs);
|
}
|
|
if (l.deform) l.weight_deform_gpu = cuda_make_array(NULL, l.nweights);
|
|
if (adam) {
|
l.m_gpu = cuda_make_array(l.m, l.nweights);
|
l.v_gpu = cuda_make_array(l.v, l.nweights);
|
l.bias_m_gpu = cuda_make_array(l.bias_m, n);
|
l.bias_v_gpu = cuda_make_array(l.bias_v, n);
|
l.scale_m_gpu = cuda_make_array(l.scale_m, n);
|
l.scale_v_gpu = cuda_make_array(l.scale_v, n);
|
}
|
if (l.share_layer) {
|
l.weights_gpu = l.share_layer->weights_gpu;
|
l.weight_updates_gpu = l.share_layer->weight_updates_gpu;
|
l.weights_gpu16 = l.share_layer->weights_gpu16;
|
l.weight_updates_gpu16 = l.share_layer->weight_updates_gpu16;
|
l.biases_gpu = l.share_layer->biases_gpu;
|
l.bias_updates_gpu = l.share_layer->bias_updates_gpu;
|
}
|
else {
|
l.weights_gpu = cuda_make_array(l.weights, l.nweights);
|
if (train) l.weight_updates_gpu = cuda_make_array(l.weight_updates, l.nweights);
|
#ifdef CUDNN_HALF
|
l.weights_gpu16 = cuda_make_array(NULL, l.nweights / 2 + 1);
|
if (train) l.weight_updates_gpu16 = cuda_make_array(NULL, l.nweights / 2 + 1);
|
#endif // CUDNN_HALF
|
l.biases_gpu = cuda_make_array(l.biases, n);
|
if (train) l.bias_updates_gpu = cuda_make_array(l.bias_updates, n);
|
}
|
|
l.output_gpu = cuda_make_array(l.output, total_batch*out_h*out_w*n);
|
if (train) l.delta_gpu = cuda_make_array(l.delta, total_batch*out_h*out_w*n);
|
|
if(binary){
|
l.binary_weights_gpu = cuda_make_array(l.weights, l.nweights);
|
}
|
if(xnor){
|
l.binary_weights_gpu = cuda_make_array(l.weights, l.nweights);
|
l.mean_arr_gpu = cuda_make_array(0, l.n);
|
l.binary_input_gpu = cuda_make_array(0, l.inputs*l.batch);
|
}
|
|
if(batch_normalize){
|
if (l.share_layer) {
|
l.scales_gpu = l.share_layer->scales_gpu;
|
l.scale_updates_gpu = l.share_layer->scale_updates_gpu;
|
l.mean_gpu = l.share_layer->mean_gpu;
|
l.variance_gpu = l.share_layer->variance_gpu;
|
l.rolling_mean_gpu = l.share_layer->rolling_mean_gpu;
|
l.rolling_variance_gpu = l.share_layer->rolling_variance_gpu;
|
l.mean_delta_gpu = l.share_layer->mean_delta_gpu;
|
l.variance_delta_gpu = l.share_layer->variance_delta_gpu;
|
}
|
else {
|
l.scales_gpu = cuda_make_array(l.scales, n);
|
|
if (train) {
|
l.scale_updates_gpu = cuda_make_array(l.scale_updates, n);
|
|
l.mean_gpu = cuda_make_array(l.mean, n);
|
l.variance_gpu = cuda_make_array(l.variance, n);
|
l.m_cbn_avg_gpu = cuda_make_array(l.mean, n);
|
l.v_cbn_avg_gpu = cuda_make_array(l.variance, n);
|
#ifndef CUDNN
|
l.mean_delta_gpu = cuda_make_array(l.mean, n);
|
l.variance_delta_gpu = cuda_make_array(l.variance, n);
|
#endif // CUDNN
|
}
|
|
l.rolling_mean_gpu = cuda_make_array(l.mean, n);
|
l.rolling_variance_gpu = cuda_make_array(l.variance, n);
|
}
|
|
if (train) {
|
l.x_gpu = cuda_make_array(l.output, total_batch*out_h*out_w*n);
|
#ifndef CUDNN
|
l.x_norm_gpu = cuda_make_array(l.output, total_batch*out_h*out_w*n);
|
#endif // CUDNN
|
}
|
}
|
|
if (l.assisted_excitation)
|
{
|
const int size = l.out_w * l.out_h * l.batch;
|
l.gt_gpu = cuda_make_array(NULL, size);
|
l.a_avg_gpu = cuda_make_array(NULL, size);
|
}
|
#ifdef CUDNN
|
create_convolutional_cudnn_tensors(&l);
|
cudnn_convolutional_setup(&l, cudnn_fastest, 0);
|
#endif // CUDNN
|
}
|
#endif // GPU
|
l.workspace_size = get_convolutional_workspace_size(l);
|
|
//fprintf(stderr, "conv %5d %2d x%2d /%2d %4d x%4d x%4d -> %4d x%4d x%4d\n", n, size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c);
|
l.bflops = (2.0 * l.nweights * l.out_h*l.out_w) / 1000000000.;
|
if (l.xnor) l.bflops = l.bflops / 32;
|
if (l.xnor && l.use_bin_output) fprintf(stderr, "convXB");
|
else if (l.xnor) fprintf(stderr, "convX ");
|
else if (l.share_layer) fprintf(stderr, "convS ");
|
else if (l.assisted_excitation) fprintf(stderr, "convAE");
|
else fprintf(stderr, "conv ");
|
|
if (groups > 1) fprintf(stderr, "%5d/%4d ", n, groups);
|
else fprintf(stderr, "%5d ", n);
|
|
if (stride_x != stride_y) fprintf(stderr, "%2dx%2d/%2dx%2d ", size, size, stride_x, stride_y);
|
else {
|
if (dilation > 1) fprintf(stderr, "%2d x%2d/%2d(%1d)", size, size, stride_x, dilation);
|
else fprintf(stderr, "%2d x%2d/%2d ", size, size, stride_x);
|
}
|
|
fprintf(stderr, "%4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", w, h, c, l.out_w, l.out_h, l.out_c, l.bflops);
|
|
//fprintf(stderr, "%5d/%2d %2d x%2d /%2d(%d)%4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", n, groups, size, size, stride, dilation, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops);
|
|
if (l.antialiasing) {
|
printf("AA: ");
|
l.input_layer = (layer*)calloc(1, sizeof(layer));
|
int blur_size = 3;
|
int blur_pad = blur_size / 2;
|
if (l.antialiasing == 2) {
|
blur_size = 2;
|
blur_pad = 0;
|
}
|
*(l.input_layer) = make_convolutional_layer(batch, steps, out_h, out_w, n, n, n, blur_size, blur_stride_x, blur_stride_y, 1, blur_pad, LINEAR, 0, 0, 0, 0, 0, index, 0, NULL, 0, 0, train);
|
const int blur_nweights = n * blur_size * blur_size; // (n / n) * n * blur_size * blur_size;
|
int i;
|
if (blur_size == 2) {
|
for (i = 0; i < blur_nweights; i += (blur_size*blur_size)) {
|
l.input_layer->weights[i + 0] = 1 / 4.f;
|
l.input_layer->weights[i + 1] = 1 / 4.f;
|
l.input_layer->weights[i + 2] = 1 / 4.f;
|
l.input_layer->weights[i + 3] = 1 / 4.f;
|
}
|
}
|
else {
|
for (i = 0; i < blur_nweights; i += (blur_size*blur_size)) {
|
l.input_layer->weights[i + 0] = 1 / 16.f;
|
l.input_layer->weights[i + 1] = 2 / 16.f;
|
l.input_layer->weights[i + 2] = 1 / 16.f;
|
|
l.input_layer->weights[i + 3] = 2 / 16.f;
|
l.input_layer->weights[i + 4] = 4 / 16.f;
|
l.input_layer->weights[i + 5] = 2 / 16.f;
|
|
l.input_layer->weights[i + 6] = 1 / 16.f;
|
l.input_layer->weights[i + 7] = 2 / 16.f;
|
l.input_layer->weights[i + 8] = 1 / 16.f;
|
}
|
}
|
for (i = 0; i < n; ++i) l.input_layer->biases[i] = 0;
|
#ifdef GPU
|
if (gpu_index >= 0) {
|
l.input_antialiasing_gpu = cuda_make_array(NULL, l.batch*l.outputs);
|
push_convolutional_layer(*(l.input_layer));
|
}
|
#endif // GPU
|
}
|
|
return l;
|
}
|
|
void denormalize_convolutional_layer(convolutional_layer l)
|
{
|
int i, j;
|
for(i = 0; i < l.n; ++i){
|
float scale = l.scales[i]/sqrt(l.rolling_variance[i] + .00001);
|
for(j = 0; j < l.nweights; ++j){
|
l.weights[i*l.nweights + j] *= scale;
|
}
|
l.biases[i] -= l.rolling_mean[i] * scale;
|
l.scales[i] = 1;
|
l.rolling_mean[i] = 0;
|
l.rolling_variance[i] = 1;
|
}
|
}
|
|
void test_convolutional_layer()
|
{
|
convolutional_layer l = make_convolutional_layer(1, 1, 5, 5, 3, 2, 1, 5, 2, 2, 1, 1, LEAKY, 1, 0, 0, 0, 0, 0, 0, NULL, 0, 0, 0);
|
l.batch_normalize = 1;
|
float data[] = {1,1,1,1,1,
|
1,1,1,1,1,
|
1,1,1,1,1,
|
1,1,1,1,1,
|
1,1,1,1,1,
|
2,2,2,2,2,
|
2,2,2,2,2,
|
2,2,2,2,2,
|
2,2,2,2,2,
|
2,2,2,2,2,
|
3,3,3,3,3,
|
3,3,3,3,3,
|
3,3,3,3,3,
|
3,3,3,3,3,
|
3,3,3,3,3};
|
network_state state = {0};
|
state.input = data;
|
forward_convolutional_layer(l, state);
|
}
|
|
void resize_convolutional_layer(convolutional_layer *l, int w, int h)
|
{
|
int total_batch = l->batch*l->steps;
|
int old_w = l->w;
|
int old_h = l->h;
|
l->w = w;
|
l->h = h;
|
int out_w = convolutional_out_width(*l);
|
int out_h = convolutional_out_height(*l);
|
|
l->out_w = out_w;
|
l->out_h = out_h;
|
|
l->outputs = l->out_h * l->out_w * l->out_c;
|
l->inputs = l->w * l->h * l->c;
|
|
|
l->output = (float*)xrealloc(l->output, total_batch * l->outputs * sizeof(float));
|
if (l->train) {
|
l->delta = (float*)xrealloc(l->delta, total_batch * l->outputs * sizeof(float));
|
|
if (l->batch_normalize) {
|
l->x = (float*)xrealloc(l->x, total_batch * l->outputs * sizeof(float));
|
l->x_norm = (float*)xrealloc(l->x_norm, total_batch * l->outputs * sizeof(float));
|
}
|
}
|
|
if (l->xnor) {
|
//l->binary_input = realloc(l->inputs*l->batch, sizeof(float));
|
}
|
|
if (l->activation == SWISH || l->activation == MISH) l->activation_input = (float*)realloc(l->activation_input, total_batch*l->outputs * sizeof(float));
|
#ifdef GPU
|
if (old_w < w || old_h < h || l->dynamic_minibatch) {
|
if (l->train) {
|
cuda_free(l->delta_gpu);
|
l->delta_gpu = cuda_make_array(l->delta, total_batch*l->outputs);
|
}
|
|
cuda_free(l->output_gpu);
|
l->output_gpu = cuda_make_array(l->output, total_batch*l->outputs);
|
|
if (l->batch_normalize) {
|
cuda_free(l->x_gpu);
|
l->x_gpu = cuda_make_array(l->output, total_batch*l->outputs);
|
|
#ifndef CUDNN
|
cuda_free(l->x_norm_gpu);
|
l->x_norm_gpu = cuda_make_array(l->output, total_batch*l->outputs);
|
#endif // CUDNN
|
}
|
|
if (l->xnor) {
|
cuda_free(l->binary_input_gpu);
|
l->binary_input_gpu = cuda_make_array(0, l->inputs*l->batch);
|
}
|
|
if (l->activation == SWISH || l->activation == MISH) {
|
cuda_free(l->activation_input_gpu);
|
l->activation_input_gpu = cuda_make_array(l->activation_input, total_batch*l->outputs);
|
}
|
|
if (l->assisted_excitation)
|
{
|
cuda_free(l->gt_gpu);
|
cuda_free(l->a_avg_gpu);
|
|
const int size = l->out_w * l->out_h * l->batch;
|
l->gt_gpu = cuda_make_array(NULL, size);
|
l->a_avg_gpu = cuda_make_array(NULL, size);
|
}
|
}
|
#ifdef CUDNN
|
cudnn_convolutional_setup(l, cudnn_fastest, 0);
|
#endif
|
#endif
|
l->workspace_size = get_convolutional_workspace_size(*l);
|
|
#ifdef CUDNN
|
// check for excessive memory consumption
|
size_t free_byte;
|
size_t total_byte;
|
CHECK_CUDA(cudaMemGetInfo(&free_byte, &total_byte));
|
if (l->workspace_size > free_byte || l->workspace_size >= total_byte / 2) {
|
printf(" used slow CUDNN algo without Workspace! Need memory: %zu, available: %zu\n", l->workspace_size, (free_byte < total_byte/2) ? free_byte : total_byte/2);
|
cudnn_convolutional_setup(l, cudnn_smallest, 0);
|
l->workspace_size = get_convolutional_workspace_size(*l);
|
}
|
#endif
|
}
|
|
void set_specified_workspace_limit(convolutional_layer *l, size_t workspace_size_limit)
|
{
|
#ifdef CUDNN
|
size_t free_byte;
|
size_t total_byte;
|
CHECK_CUDA(cudaMemGetInfo(&free_byte, &total_byte));
|
cudnn_convolutional_setup(l, cudnn_specify, workspace_size_limit);
|
l->workspace_size = get_convolutional_workspace_size(*l);
|
//printf("Set specified workspace limit for cuDNN: %zu, available: %zu, workspace = %zu \n", workspace_size_limit, free_byte, l->workspace_size);
|
#endif // CUDNN
|
}
|
|
void add_bias(float *output, float *biases, int batch, int n, int size)
|
{
|
int i,j,b;
|
for(b = 0; b < batch; ++b){
|
for(i = 0; i < n; ++i){
|
for(j = 0; j < size; ++j){
|
output[(b*n + i)*size + j] += biases[i];
|
}
|
}
|
}
|
}
|
|
void scale_bias(float *output, float *scales, int batch, int n, int size)
|
{
|
int i,j,b;
|
for(b = 0; b < batch; ++b){
|
for(i = 0; i < n; ++i){
|
for(j = 0; j < size; ++j){
|
output[(b*n + i)*size + j] *= scales[i];
|
}
|
}
|
}
|
}
|
|
void backward_bias(float *bias_updates, float *delta, int batch, int n, int size)
|
{
|
int i,b;
|
for(b = 0; b < batch; ++b){
|
for(i = 0; i < n; ++i){
|
bias_updates[i] += sum_array(delta+size*(i+b*n), size);
|
}
|
}
|
}
|
|
void gemm_nn_custom(int M, int N, int K, float ALPHA,
|
float *A, int lda,
|
float *B, int ldb,
|
float *C, int ldc)
|
{
|
int i, j, k;
|
for (i = 0; i < M; ++i) {
|
for (k = 0; k < K; ++k) {
|
PUT_IN_REGISTER float A_PART = ALPHA * A[i * lda + k];
|
//printf("\n weight = %f \n", A_PART);
|
for (j = 0; j < N; ++j) {
|
C[i*ldc + j] += A_PART*B[k*ldb + j];
|
}
|
}
|
}
|
}
|
|
|
void get_mean_array(float *src, size_t size, size_t filters, float *mean_arr) {
|
size_t i, counter;
|
counter = 0;
|
for (i = 0; i < size; i += size / filters) {
|
mean_arr[counter++] = fabs(src[i]);
|
}
|
}
|
|
/*
|
void float_to_bit(float *src, unsigned char *dst, size_t size) {
|
|
size_t dst_size = size / 8 + 1;
|
memset(dst, 0, dst_size);
|
size_t i, dst_i, dst_shift;
|
for (i = 0; i < size; ++i) {
|
if (src[i] > 0) set_bit(dst, i);
|
}
|
}
|
*/
|
|
void bit_to_float(unsigned char *src, float *dst, size_t size, size_t filters, float *mean_arr) {
|
memset(dst, 0, size *sizeof(float));
|
size_t i;
|
|
for (i = 0; i < size; ++i) {
|
float mean_val = 1;
|
if(mean_arr != NULL) mean_val = fabs(mean_arr[i / (size / filters)]);
|
if(get_bit(src, i)) dst[i] = mean_val;
|
else dst[i] = -mean_val;
|
}
|
}
|
|
void binary_align_weights(convolutional_layer *l)
|
{
|
int m = l->n; // (l->n / l->groups)
|
int k = l->size*l->size*l->c; // ->size*l->size*(l->c / l->groups)
|
size_t new_lda = k + (l->lda_align - k % l->lda_align); // (k / 8 + 1) * 8;
|
l->new_lda = new_lda;
|
|
binarize_weights(l->weights, m, k, l->binary_weights);
|
|
size_t align_weights_size = new_lda * m;
|
l->align_bit_weights_size = align_weights_size / 8 + 1;
|
float* align_weights = (float*)xcalloc(align_weights_size, sizeof(float));
|
l->align_bit_weights = (char*)xcalloc(l->align_bit_weights_size, sizeof(char));
|
|
size_t i, j;
|
// align A without transpose
|
for (i = 0; i < m; ++i) {
|
for (j = 0; j < k; ++j) {
|
align_weights[i*new_lda + j] = l->binary_weights[i*k + j];
|
}
|
}
|
|
|
if (l->c % 32 == 0)
|
//if(gpu_index < 0 && l->stride == 1 && l->pad == 1 && l->c % 32 == 0)
|
//if (l->stride == 1 && l->pad == 1 && l->c % 32 == 0)
|
{
|
int fil, chan;
|
const int items_per_filter = l->c * l->size * l->size;
|
//const int dst_items_per_filter = new_lda;
|
for (fil = 0; fil < l->n; ++fil)
|
{
|
for (chan = 0; chan < l->c; chan += 32)
|
{
|
const int items_per_channel = l->size*l->size;
|
for (i = 0; i < items_per_channel; ++i)
|
{
|
//uint32_t val = 0;
|
int c_pack;
|
for (c_pack = 0; c_pack < 32; ++c_pack) {
|
float src = l->binary_weights[fil*items_per_filter + (chan + c_pack)*items_per_channel + i];
|
|
//align_weights[fil*items_per_filter + chan*items_per_channel + i * 32 + c_pack] = src;
|
|
align_weights[fil*new_lda + chan*items_per_channel + i*32 + c_pack] = src;
|
//val |= (src << c);
|
}
|
|
}
|
}
|
}
|
|
//printf("\n l.index = %d \t aw[0] = %f, aw[1] = %f, aw[2] = %f, aw[3] = %f \n", l->index, align_weights[0], align_weights[1], align_weights[2], align_weights[3]);
|
//memcpy(l->binary_weights, align_weights, (l->size * l->size * l->c * l->n) * sizeof(float));
|
|
float_to_bit(align_weights, (unsigned char*)l->align_bit_weights, align_weights_size);
|
|
//if (l->n >= 32)
|
if(gpu_index >= 0)
|
{
|
//int M = l->n;
|
//int N = l->out_w*l->out_h;
|
//printf("\n M = %d, N = %d, M %% 8 = %d, N %% 8 = %d - weights \n", M, N, M % 8, N % 8);
|
//printf("\n l.w = %d, l.c = %d, l.n = %d \n", l->w, l->c, l->n);
|
for (i = 0; i < align_weights_size / 8; ++i) l->align_bit_weights[i] = ~(l->align_bit_weights[i]);
|
}
|
|
|
|
get_mean_array(l->binary_weights, m*k, l->n, l->mean_arr);
|
//get_mean_array(l->binary_weights, m*new_lda, l->n, l->mean_arr);
|
}
|
else {
|
float_to_bit(align_weights, (unsigned char*)l->align_bit_weights, align_weights_size);
|
|
get_mean_array(l->binary_weights, m*k, l->n, l->mean_arr);
|
}
|
|
//l->mean_arr = calloc(l->n, sizeof(float));
|
|
//get_mean_array(align_weights, align_weights_size, l->n, l->mean_arr);
|
|
|
|
|
#ifdef GPU
|
cudaError_t status;
|
l->align_workspace_size = l->bit_align * l->size * l->size * l->c;
|
status = cudaMalloc((void **)&l->align_workspace_gpu, l->align_workspace_size * sizeof(float));
|
status = cudaMalloc((void **)&l->transposed_align_workspace_gpu, l->align_workspace_size * sizeof(float));
|
CHECK_CUDA(status);
|
|
//l->align_bit_weights_gpu = cuda_make_array(l->align_bit_weights, l->align_bit_weights_size * sizeof(char)/sizeof(float));
|
status = cudaMalloc((void **)&l->align_bit_weights_gpu, l->align_bit_weights_size);
|
CHECK_CUDA(status);
|
status = cudaMemcpy(l->align_bit_weights_gpu, l->align_bit_weights, l->align_bit_weights_size, cudaMemcpyHostToDevice);
|
CHECK_CUDA(status);
|
status = cudaMemcpy(l->binary_weights_gpu, l->binary_weights, m*k * sizeof(float), cudaMemcpyHostToDevice);
|
CHECK_CUDA(status);
|
|
//l->mean_arr_gpu = cuda_make_array(l->mean_arr, l->n);
|
cuda_push_array(l->mean_arr_gpu, l->mean_arr, l->n);
|
CHECK_CUDA(cudaDeviceSynchronize());
|
#endif // GPU
|
|
free(align_weights);
|
}
|
|
// binary transpose
|
size_t binary_transpose_align_input(int k, int n, float *b, char **t_bit_input, size_t ldb_align, int bit_align)
|
{
|
size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8;
|
//printf("\n n = %d, bit_align = %d \n", n, bit_align);
|
size_t t_intput_size = new_ldb * bit_align;// n;
|
size_t t_bit_input_size = t_intput_size / 8;// +1;
|
|
memset(*t_bit_input, 0, t_bit_input_size * sizeof(char));
|
//int src_size = k * bit_align;
|
|
// b - [bit_align, k] - [l.bit_align, l.size*l.size*l.c] = src_size
|
// t_input - [bit_align, k] - [n', k]
|
// t_bit_input - [new_ldb, n] - [k', n]
|
|
//transpose_bin(t_input, *t_bit_input, k, n, bit_align, new_ldb, 8);
|
transpose_bin((uint32_t*)b, (uint32_t*)*t_bit_input, k, n, bit_align, new_ldb, 8);
|
|
return t_intput_size;
|
}
|
|
|
void forward_convolutional_layer(convolutional_layer l, network_state state)
|
{
|
int out_h = convolutional_out_height(l);
|
int out_w = convolutional_out_width(l);
|
int i, j;
|
|
fill_cpu(l.outputs*l.batch, 0, l.output, 1);
|
|
if (l.xnor && (!l.align_bit_weights || state.train)) {
|
if (!l.align_bit_weights || state.train) {
|
binarize_weights(l.weights, l.n, l.nweights, l.binary_weights);
|
//printf("\n binarize_weights l.align_bit_weights = %p \n", l.align_bit_weights);
|
}
|
swap_binary(&l);
|
binarize_cpu(state.input, l.c*l.h*l.w*l.batch, l.binary_input);
|
state.input = l.binary_input;
|
}
|
|
int m = l.n / l.groups;
|
int k = l.size*l.size*l.c / l.groups;
|
int n = out_h*out_w;
|
|
static int u = 0;
|
u++;
|
|
for(i = 0; i < l.batch; ++i)
|
{
|
for (j = 0; j < l.groups; ++j)
|
{
|
float *a = l.weights +j*l.nweights / l.groups;
|
float *b = state.workspace;
|
float *c = l.output +(i*l.groups + j)*n*m;
|
|
//gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
|
//gemm_nn_custom(m, n, k, 1, a, k, b, n, c, n);
|
if (l.xnor && l.align_bit_weights && !state.train && l.stride_x == l.stride_y)
|
{
|
memset(b, 0, l.bit_align*l.size*l.size*l.c * sizeof(float));
|
|
if (l.c % 32 == 0)
|
{
|
//printf(" l.index = %d - new XNOR \n", l.index);
|
|
int ldb_align = l.lda_align;
|
size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8;
|
//size_t t_intput_size = new_ldb * l.bit_align;// n;
|
//size_t t_bit_input_size = t_intput_size / 8;// +1;
|
|
int re_packed_input_size = l.c * l.w * l.h;
|
memset(state.workspace, 0, re_packed_input_size * sizeof(float));
|
|
const size_t new_c = l.c / 32;
|
size_t in_re_packed_input_size = new_c * l.w * l.h + 1;
|
memset(l.bin_re_packed_input, 0, in_re_packed_input_size * sizeof(uint32_t));
|
|
//float *re_packed_input = calloc(l.c * l.w * l.h, sizeof(float));
|
//uint32_t *bin_re_packed_input = calloc(new_c * l.w * l.h + 1, sizeof(uint32_t));
|
|
// float32x4 by channel (as in cuDNN)
|
repack_input(state.input, state.workspace, l.w, l.h, l.c);
|
|
// 32 x floats -> 1 x uint32_t
|
float_to_bit(state.workspace, (unsigned char *)l.bin_re_packed_input, l.c * l.w * l.h);
|
|
//free(re_packed_input);
|
|
// slow - convolution the packed inputs and weights: float x 32 by channel (as in cuDNN)
|
//convolution_repacked((uint32_t *)bin_re_packed_input, (uint32_t *)l.align_bit_weights, l.output,
|
// l.w, l.h, l.c, l.n, l.size, l.pad, l.new_lda, l.mean_arr);
|
|
// // then exit from if()
|
|
|
im2col_cpu_custom((float *)l.bin_re_packed_input, new_c, l.h, l.w, l.size, l.stride, l.pad, state.workspace);
|
//im2col_cpu((float *)bin_re_packed_input, new_c, l.h, l.w, l.size, l.stride, l.pad, b);
|
|
//free(bin_re_packed_input);
|
|
int new_k = l.size*l.size*l.c / 32;
|
|
// good for (l.c == 64)
|
//gemm_nn_bin_32bit_packed(m, n, new_k, 1,
|
// l.align_bit_weights, l.new_lda/32,
|
// b, n,
|
// c, n, l.mean_arr);
|
|
// // then exit from if()
|
|
transpose_uint32((uint32_t *)state.workspace, (uint32_t*)l.t_bit_input, new_k, n, n, new_ldb);
|
|
// the main GEMM function
|
gemm_nn_custom_bin_mean_transposed(m, n, k, 1, (unsigned char*)l.align_bit_weights, new_ldb, (unsigned char*)l.t_bit_input, new_ldb, c, n, l.mean_arr);
|
|
// // alternative GEMM
|
//gemm_nn_bin_transposed_32bit_packed(m, n, new_k, 1,
|
// l.align_bit_weights, l.new_lda/32,
|
// t_bit_input, new_ldb / 32,
|
// c, n, l.mean_arr);
|
|
//free(t_bit_input);
|
|
}
|
else
|
{ // else (l.c % 32 != 0)
|
|
//--------------------------------------------------------
|
//printf(" l.index = %d - old XNOR \n", l.index);
|
|
//im2col_cpu_custom_align(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b, l.bit_align);
|
im2col_cpu_custom_bin(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, state.workspace, l.bit_align);
|
|
//size_t output_size = l.outputs;
|
//float *count_output = calloc(output_size, sizeof(float));
|
//size_t bit_output_size = output_size / 8 + 1;
|
//char *bit_output = calloc(bit_output_size, sizeof(char));
|
|
//size_t intput_size = n * k; // (out_h*out_w) X (l.size*l.size*l.c) : after im2col()
|
//size_t bit_input_size = intput_size / 8 + 1;
|
//char *bit_input = calloc(bit_input_size, sizeof(char));
|
|
//size_t weights_size = k * m; //l.size*l.size*l.c*l.n; // l.nweights
|
//size_t bit_weights_size = weights_size / 8 + 1;
|
|
//char *bit_weights = calloc(bit_weights_size, sizeof(char));
|
//float *mean_arr = calloc(l.n, sizeof(float));
|
|
// transpose B from NxK to KxN (x-axis (ldb = l.size*l.size*l.c) - should be multiple of 8 bits)
|
{
|
//size_t ldb_align = 256; // 256 bit for AVX2
|
int ldb_align = l.lda_align;
|
size_t new_ldb = k + (ldb_align - k%ldb_align);
|
size_t t_intput_size = binary_transpose_align_input(k, n, state.workspace, &l.t_bit_input, ldb_align, l.bit_align);
|
|
// 5x times faster than gemm()-float32
|
gemm_nn_custom_bin_mean_transposed(m, n, k, 1, (unsigned char*)l.align_bit_weights, new_ldb, (unsigned char*)l.t_bit_input, new_ldb, c, n, l.mean_arr);
|
|
//gemm_nn_custom_bin_mean_transposed(m, n, k, 1, bit_weights, k, t_bit_input, new_ldb, c, n, mean_arr);
|
|
//free(t_input);
|
//free(t_bit_input);
|
//}
|
}
|
|
}
|
|
add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w);
|
|
//activate_array(l.output, m*n*l.batch, l.activation);
|
if (l.activation == SWISH) activate_array_swish(l.output, l.outputs*l.batch, l.activation_input, l.output);
|
else if (l.activation == MISH) activate_array_mish(l.output, l.outputs*l.batch, l.activation_input, l.output);
|
else if (l.activation == NORM_CHAN) activate_array_normalize_channels(l.output, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output);
|
else if (l.activation == NORM_CHAN_SOFTMAX) activate_array_normalize_channels_softmax(l.output, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output, 0);
|
else if (l.activation == NORM_CHAN_SOFTMAX_MAXVAL) activate_array_normalize_channels_softmax(l.output, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output, 1);
|
else activate_array_cpu_custom(l.output, m*n*l.batch, l.activation);
|
return;
|
|
}
|
else {
|
//printf(" l.index = %d - FP32 \n", l.index);
|
float *im = state.input + (i*l.groups + j)*(l.c / l.groups)*l.h*l.w;
|
if (l.size == 1) {
|
b = im;
|
}
|
else {
|
//im2col_cpu(im, l.c / l.groups, l.h, l.w, l.size, l.stride, l.pad, b);
|
|
im2col_cpu_ext(im, // input
|
l.c / l.groups, // input channels
|
l.h, l.w, // input size (h, w)
|
l.size, l.size, // kernel size (h, w)
|
l.pad * l.dilation, l.pad * l.dilation, // padding (h, w)
|
l.stride_y, l.stride_x, // stride (h, w)
|
l.dilation, l.dilation, // dilation (h, w)
|
b); // output
|
|
}
|
|
gemm(0, 0, m, n, k, 1, a, k, b, n, 1, c, n);
|
// bit-count to float
|
}
|
//c += n*m;
|
//state.input += l.c*l.h*l.w;
|
}
|
}
|
|
if(l.batch_normalize){
|
forward_batchnorm_layer(l, state);
|
}
|
else {
|
add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w);
|
}
|
|
//activate_array(l.output, m*n*l.batch, l.activation);
|
if (l.activation == SWISH) activate_array_swish(l.output, l.outputs*l.batch, l.activation_input, l.output);
|
else if (l.activation == MISH) activate_array_mish(l.output, l.outputs*l.batch, l.activation_input, l.output);
|
else if (l.activation == NORM_CHAN) activate_array_normalize_channels(l.output, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output);
|
else if (l.activation == NORM_CHAN_SOFTMAX) activate_array_normalize_channels_softmax(l.output, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output, 0);
|
else if (l.activation == NORM_CHAN_SOFTMAX_MAXVAL) activate_array_normalize_channels_softmax(l.output, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output, 1);
|
else activate_array_cpu_custom(l.output, l.outputs*l.batch, l.activation);
|
|
if(l.binary || l.xnor) swap_binary(&l);
|
|
//visualize_convolutional_layer(l, "conv_visual", NULL);
|
//wait_until_press_key_cv();
|
|
if(l.assisted_excitation && state.train) assisted_excitation_forward(l, state);
|
|
if (l.antialiasing) {
|
network_state s = { 0 };
|
s.train = state.train;
|
s.workspace = state.workspace;
|
s.net = state.net;
|
s.input = l.output;
|
forward_convolutional_layer(*(l.input_layer), s);
|
//simple_copy_ongpu(l.outputs*l.batch, l.output, l.input_antialiasing);
|
memcpy(l.output, l.input_layer->output, l.input_layer->outputs * l.input_layer->batch * sizeof(float));
|
}
|
}
|
|
void assisted_excitation_forward(convolutional_layer l, network_state state)
|
{
|
const int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions);
|
|
// epoch
|
//const float epoch = (float)(*state.net.seen) / state.net.train_images_num;
|
|
// calculate alpha
|
//const float alpha = (1 + cos(3.141592 * iteration_num)) / (2 * state.net.max_batches);
|
//const float alpha = (1 + cos(3.141592 * epoch)) / (2 * state.net.max_batches);
|
float alpha = (1 + cos(3.141592 * iteration_num / state.net.max_batches));
|
|
if (l.assisted_excitation > 1) {
|
if (iteration_num > l.assisted_excitation) alpha = 0;
|
else alpha = (1 + cos(3.141592 * iteration_num / l.assisted_excitation));
|
}
|
|
//printf("\n epoch = %f, alpha = %f, seen = %d, max_batches = %d, train_images_num = %d \n",
|
// epoch, alpha, (*state.net.seen), state.net.max_batches, state.net.train_images_num);
|
|
float *a_avg = (float *)xcalloc(l.out_w * l.out_h * l.batch, sizeof(float));
|
float *g = (float *)xcalloc(l.out_w * l.out_h * l.batch, sizeof(float));
|
|
int b;
|
int w, h, c;
|
|
l.max_boxes = state.net.num_boxes;
|
l.truths = l.max_boxes*(4 + 1);
|
|
for (b = 0; b < l.batch; ++b)
|
{
|
// calculate G
|
int t;
|
for (t = 0; t < state.net.num_boxes; ++t) {
|
box truth = float_to_box_stride(state.truth + t*(4 + 1) + b*l.truths, 1);
|
if (!truth.x) break; // continue;
|
|
int left = floor((truth.x - truth.w / 2) * l.out_w);
|
int right = ceil((truth.x + truth.w / 2) * l.out_w);
|
int top = floor((truth.y - truth.h / 2) * l.out_h);
|
int bottom = ceil((truth.y + truth.h / 2) * l.out_h);
|
|
for (w = left; w <= right; w++) {
|
for (h = top; h < bottom; h++) {
|
g[w + l.out_w * h + l.out_w*l.out_h*b] = 1;
|
}
|
}
|
}
|
}
|
|
for (b = 0; b < l.batch; ++b)
|
{
|
// calculate average A
|
for (w = 0; w < l.out_w; w++) {
|
for (h = 0; h < l.out_h; h++) {
|
for (c = 0; c < l.out_c; c++) {
|
a_avg[w + l.out_w*(h + l.out_h*b)] += l.output[w + l.out_w*(h + l.out_h*(c + l.out_c*b))];
|
}
|
a_avg[w + l.out_w*(h + l.out_h*b)] /= l.out_c; // a_avg / d
|
}
|
}
|
}
|
|
// change activation
|
for (b = 0; b < l.batch; ++b)
|
{
|
for (w = 0; w < l.out_w; w++) {
|
for (h = 0; h < l.out_h; h++) {
|
for (c = 0; c < l.out_c; c++)
|
{
|
// a = a + alpha(t) + e(c,i,j) = a + alpha(t) + g(i,j) * avg_a(i,j) / channels
|
l.output[w + l.out_w*(h + l.out_h*(c + l.out_c*b))] +=
|
alpha *
|
g[w + l.out_w*(h + l.out_h*b)] *
|
a_avg[w + l.out_w*(h + l.out_h*b)];
|
|
//l.output[w + l.out_w*(h + l.out_h*(c + l.out_c*b))] =
|
// alpha * g[w + l.out_w*(h + l.out_h*b)] * a_avg[w + l.out_w*(h + l.out_h*b)];
|
}
|
}
|
}
|
}
|
|
if(0) // visualize ground truth
|
{
|
#ifdef OPENCV
|
for (b = 0; b < l.batch; ++b)
|
{
|
image img = float_to_image(l.out_w, l.out_h, 1, &g[l.out_w*l.out_h*b]);
|
char buff[100];
|
sprintf(buff, "a_excitation_%d", b);
|
show_image_cv(img, buff);
|
|
image img2 = float_to_image(l.out_w, l.out_h, 1, &l.output[l.out_w*l.out_h*l.out_c*b]);
|
char buff2[100];
|
sprintf(buff2, "a_excitation_act_%d", b);
|
show_image_cv(img2, buff2);
|
wait_key_cv(5);
|
}
|
wait_until_press_key_cv();
|
#endif // OPENCV
|
}
|
|
free(g);
|
free(a_avg);
|
}
|
|
|
void backward_convolutional_layer(convolutional_layer l, network_state state)
|
{
|
int i, j;
|
int m = l.n / l.groups;
|
int n = l.size*l.size*l.c / l.groups;
|
int k = l.out_w*l.out_h;
|
|
if (l.activation == SWISH) gradient_array_swish(l.output, l.outputs*l.batch, l.activation_input, l.delta);
|
else if (l.activation == MISH) gradient_array_mish(l.outputs*l.batch, l.activation_input, l.delta);
|
else if (l.activation == NORM_CHAN_SOFTMAX || l.activation == NORM_CHAN_SOFTMAX_MAXVAL) gradient_array_normalize_channels_softmax(l.output, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.delta);
|
else if (l.activation == NORM_CHAN) gradient_array_normalize_channels(l.output, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.delta);
|
else gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta);
|
|
if (l.batch_normalize) {
|
backward_batchnorm_layer(l, state);
|
}
|
else {
|
backward_bias(l.bias_updates, l.delta, l.batch, l.n, k);
|
}
|
|
for (i = 0; i < l.batch; ++i) {
|
for (j = 0; j < l.groups; ++j) {
|
float *a = l.delta + (i*l.groups + j)*m*k;
|
float *b = state.workspace;
|
float *c = l.weight_updates + j*l.nweights / l.groups;
|
|
float *im = state.input + (i*l.groups + j)* (l.c / l.groups)*l.h*l.w;
|
|
//im2col_cpu(im, l.c / l.groups, l.h, l.w, l.size, l.stride, l.pad, b);
|
im2col_cpu_ext(
|
im, // input
|
l.c / l.groups, // input channels
|
l.h, l.w, // input size (h, w)
|
l.size, l.size, // kernel size (h, w)
|
l.pad * l.dilation, l.pad * l.dilation, // padding (h, w)
|
l.stride_y, l.stride_x, // stride (h, w)
|
l.dilation, l.dilation, // dilation (h, w)
|
b); // output
|
|
gemm(0, 1, m, n, k, 1, a, k, b, k, 1, c, n);
|
|
if (state.delta) {
|
a = l.weights + j*l.nweights / l.groups;
|
b = l.delta + (i*l.groups + j)*m*k;
|
c = state.workspace;
|
|
gemm(1, 0, n, k, m, 1, a, n, b, k, 0, c, k);
|
|
//col2im_cpu(state.workspace, l.c / l.groups, l.h, l.w, l.size, l.stride,
|
// l.pad, state.delta + (i*l.groups + j)*l.c / l.groups*l.h*l.w);
|
|
col2im_cpu_ext(
|
state.workspace, // input
|
l.c / l.groups, // input channels (h, w)
|
l.h, l.w, // input size (h, w)
|
l.size, l.size, // kernel size (h, w)
|
l.pad * l.dilation, l.pad * l.dilation, // padding (h, w)
|
l.stride_y, l.stride_x, // stride (h, w)
|
l.dilation, l.dilation, // dilation (h, w)
|
state.delta + (i*l.groups + j)* (l.c / l.groups)*l.h*l.w); // output (delta)
|
}
|
}
|
}
|
}
|
|
void update_convolutional_layer(convolutional_layer l, int batch, float learning_rate_init, float momentum, float decay)
|
{
|
float learning_rate = learning_rate_init*l.learning_rate_scale;
|
//float momentum = a.momentum;
|
//float decay = a.decay;
|
//int batch = a.batch;
|
|
axpy_cpu(l.nweights, -decay*batch, l.weights, 1, l.weight_updates, 1);
|
axpy_cpu(l.nweights, learning_rate / batch, l.weight_updates, 1, l.weights, 1);
|
scal_cpu(l.nweights, momentum, l.weight_updates, 1);
|
|
axpy_cpu(l.n, learning_rate / batch, l.bias_updates, 1, l.biases, 1);
|
scal_cpu(l.n, momentum, l.bias_updates, 1);
|
|
if (l.scales) {
|
axpy_cpu(l.n, learning_rate / batch, l.scale_updates, 1, l.scales, 1);
|
scal_cpu(l.n, momentum, l.scale_updates, 1);
|
}
|
}
|
|
|
|
image get_convolutional_weight(convolutional_layer l, int i)
|
{
|
int h = l.size;
|
int w = l.size;
|
int c = l.c / l.groups;
|
return float_to_image(w, h, c, l.weights + i*h*w*c);
|
}
|
|
void rgbgr_weights(convolutional_layer l)
|
{
|
int i;
|
for (i = 0; i < l.n; ++i) {
|
image im = get_convolutional_weight(l, i);
|
if (im.c == 3) {
|
rgbgr_image(im);
|
}
|
}
|
}
|
|
void rescale_weights(convolutional_layer l, float scale, float trans)
|
{
|
int i;
|
for (i = 0; i < l.n; ++i) {
|
image im = get_convolutional_weight(l, i);
|
if (im.c == 3) {
|
scale_image(im, scale);
|
float sum = sum_array(im.data, im.w*im.h*im.c);
|
l.biases[i] += sum*trans;
|
}
|
}
|
}
|
|
image *get_weights(convolutional_layer l)
|
{
|
image *weights = (image *)xcalloc(l.n, sizeof(image));
|
int i;
|
for (i = 0; i < l.n; ++i) {
|
weights[i] = copy_image(get_convolutional_weight(l, i));
|
normalize_image(weights[i]);
|
/*
|
char buff[256];
|
sprintf(buff, "filter%d", i);
|
save_image(weights[i], buff);
|
*/
|
}
|
//error("hey");
|
return weights;
|
}
|
|
image *visualize_convolutional_layer(convolutional_layer l, char *window, image *prev_weights)
|
{
|
image *single_weights = get_weights(l);
|
show_images(single_weights, l.n, window);
|
|
image delta = get_convolutional_image(l);
|
image dc = collapse_image_layers(delta, 1);
|
char buff[256];
|
sprintf(buff, "%s: Output", window);
|
show_image(dc, buff);
|
//save_image(dc, buff);
|
free_image(dc);
|
return single_weights;
|
}
|
|
