mirror of https://github.com/pjreddie/darknet.git
313 lines
9.6 KiB
C
313 lines
9.6 KiB
C
#include "deconvolutional_layer.h"
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#include "convolutional_layer.h"
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#include "batchnorm_layer.h"
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#include "utils.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 <stdio.h>
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#include <time.h>
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static size_t get_workspace_size(layer l){
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return (size_t)l.h*l.w*l.size*l.size*l.n*sizeof(float);
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}
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void bilinear_init(layer l)
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{
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int i,j,f;
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float center = (l.size-1) / 2.;
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for(f = 0; f < l.n; ++f){
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for(j = 0; j < l.size; ++j){
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for(i = 0; i < l.size; ++i){
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float val = (1 - fabs(i - center)) * (1 - fabs(j - center));
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int c = f%l.c;
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int ind = f*l.size*l.size*l.c + c*l.size*l.size + j*l.size + i;
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l.weights[ind] = val;
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}
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}
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}
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}
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layer make_deconvolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int adam)
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{
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int i;
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layer l = {0};
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l.type = DECONVOLUTIONAL;
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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.n = n;
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l.batch = batch;
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l.stride = stride;
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l.size = size;
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l.nweights = c*n*size*size;
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l.nbiases = n;
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l.weights = calloc(c*n*size*size, sizeof(float));
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l.weight_updates = calloc(c*n*size*size, sizeof(float));
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l.biases = calloc(n, sizeof(float));
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l.bias_updates = calloc(n, sizeof(float));
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//float scale = n/(size*size*c);
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//printf("scale: %f\n", scale);
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float scale = .02;
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for(i = 0; i < c*n*size*size; ++i) l.weights[i] = scale*rand_normal();
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//bilinear_init(l);
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for(i = 0; i < n; ++i){
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l.biases[i] = 0;
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}
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l.pad = padding;
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l.out_h = (l.h - 1) * l.stride + l.size - 2*l.pad;
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l.out_w = (l.w - 1) * l.stride + l.size - 2*l.pad;
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l.out_c = n;
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l.outputs = l.out_w * l.out_h * l.out_c;
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l.inputs = l.w * l.h * l.c;
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scal_cpu(l.nweights, (float)l.out_w*l.out_h/(l.w*l.h), l.weights, 1);
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l.output = calloc(l.batch*l.outputs, sizeof(float));
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l.delta = calloc(l.batch*l.outputs, sizeof(float));
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l.forward = forward_deconvolutional_layer;
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l.backward = backward_deconvolutional_layer;
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l.update = update_deconvolutional_layer;
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l.batch_normalize = batch_normalize;
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if(batch_normalize){
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l.scales = calloc(n, sizeof(float));
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l.scale_updates = calloc(n, sizeof(float));
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for(i = 0; i < n; ++i){
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l.scales[i] = 1;
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}
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l.mean = calloc(n, sizeof(float));
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l.variance = calloc(n, sizeof(float));
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l.mean_delta = calloc(n, sizeof(float));
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l.variance_delta = calloc(n, sizeof(float));
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l.rolling_mean = calloc(n, sizeof(float));
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l.rolling_variance = calloc(n, sizeof(float));
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l.x = calloc(l.batch*l.outputs, sizeof(float));
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l.x_norm = calloc(l.batch*l.outputs, sizeof(float));
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}
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if(adam){
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l.m = calloc(c*n*size*size, sizeof(float));
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l.v = calloc(c*n*size*size, sizeof(float));
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l.bias_m = calloc(n, sizeof(float));
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l.scale_m = calloc(n, sizeof(float));
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l.bias_v = calloc(n, sizeof(float));
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l.scale_v = calloc(n, sizeof(float));
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}
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#ifdef GPU
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l.forward_gpu = forward_deconvolutional_layer_gpu;
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l.backward_gpu = backward_deconvolutional_layer_gpu;
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l.update_gpu = update_deconvolutional_layer_gpu;
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if(gpu_index >= 0){
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if (adam) {
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l.m_gpu = cuda_make_array(l.m, c*n*size*size);
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l.v_gpu = cuda_make_array(l.v, c*n*size*size);
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l.bias_m_gpu = cuda_make_array(l.bias_m, n);
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l.bias_v_gpu = cuda_make_array(l.bias_v, n);
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l.scale_m_gpu = cuda_make_array(l.scale_m, n);
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l.scale_v_gpu = cuda_make_array(l.scale_v, n);
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}
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l.weights_gpu = cuda_make_array(l.weights, c*n*size*size);
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l.weight_updates_gpu = cuda_make_array(l.weight_updates, c*n*size*size);
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l.biases_gpu = cuda_make_array(l.biases, n);
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l.bias_updates_gpu = cuda_make_array(l.bias_updates, n);
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l.delta_gpu = cuda_make_array(l.delta, l.batch*l.out_h*l.out_w*n);
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l.output_gpu = cuda_make_array(l.output, l.batch*l.out_h*l.out_w*n);
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if(batch_normalize){
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l.mean_gpu = cuda_make_array(0, n);
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l.variance_gpu = cuda_make_array(0, n);
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l.rolling_mean_gpu = cuda_make_array(0, n);
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l.rolling_variance_gpu = cuda_make_array(0, n);
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l.mean_delta_gpu = cuda_make_array(0, n);
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l.variance_delta_gpu = cuda_make_array(0, n);
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l.scales_gpu = cuda_make_array(l.scales, n);
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l.scale_updates_gpu = cuda_make_array(0, n);
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l.x_gpu = cuda_make_array(0, l.batch*l.out_h*l.out_w*n);
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l.x_norm_gpu = cuda_make_array(0, l.batch*l.out_h*l.out_w*n);
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}
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}
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#ifdef CUDNN
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cudnnCreateTensorDescriptor(&l.dstTensorDesc);
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cudnnCreateTensorDescriptor(&l.normTensorDesc);
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cudnnSetTensor4dDescriptor(l.dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.out_c, l.out_h, l.out_w);
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cudnnSetTensor4dDescriptor(l.normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l.out_c, 1, 1);
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#endif
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#endif
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l.activation = activation;
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l.workspace_size = get_workspace_size(l);
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fprintf(stderr, "deconv%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);
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return l;
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}
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void denormalize_deconvolutional_layer(layer l)
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{
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int i, j;
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for(i = 0; i < l.n; ++i){
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float scale = l.scales[i]/sqrt(l.rolling_variance[i] + .00001);
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for(j = 0; j < l.c*l.size*l.size; ++j){
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l.weights[i*l.c*l.size*l.size + j] *= scale;
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}
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l.biases[i] -= l.rolling_mean[i] * scale;
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l.scales[i] = 1;
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l.rolling_mean[i] = 0;
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l.rolling_variance[i] = 1;
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}
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}
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void resize_deconvolutional_layer(layer *l, int h, int w)
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{
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l->h = h;
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l->w = w;
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l->out_h = (l->h - 1) * l->stride + l->size - 2*l->pad;
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l->out_w = (l->w - 1) * l->stride + l->size - 2*l->pad;
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l->outputs = l->out_h * l->out_w * l->out_c;
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l->inputs = l->w * l->h * l->c;
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l->output = realloc(l->output, l->batch*l->outputs*sizeof(float));
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l->delta = realloc(l->delta, l->batch*l->outputs*sizeof(float));
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if(l->batch_normalize){
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l->x = realloc(l->x, l->batch*l->outputs*sizeof(float));
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l->x_norm = realloc(l->x_norm, l->batch*l->outputs*sizeof(float));
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}
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#ifdef GPU
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cuda_free(l->delta_gpu);
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cuda_free(l->output_gpu);
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l->delta_gpu = cuda_make_array(l->delta, l->batch*l->outputs);
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l->output_gpu = cuda_make_array(l->output, l->batch*l->outputs);
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if(l->batch_normalize){
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cuda_free(l->x_gpu);
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cuda_free(l->x_norm_gpu);
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l->x_gpu = cuda_make_array(l->output, l->batch*l->outputs);
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l->x_norm_gpu = cuda_make_array(l->output, l->batch*l->outputs);
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}
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#ifdef CUDNN
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cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w);
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cudnnSetTensor4dDescriptor(l->normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l->out_c, 1, 1);
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#endif
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#endif
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l->workspace_size = get_workspace_size(*l);
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}
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void forward_deconvolutional_layer(const layer l, network net)
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{
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int i;
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int m = l.size*l.size*l.n;
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int n = l.h*l.w;
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int k = l.c;
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fill_cpu(l.outputs*l.batch, 0, l.output, 1);
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for(i = 0; i < l.batch; ++i){
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float *a = l.weights;
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float *b = net.input + i*l.c*l.h*l.w;
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float *c = net.workspace;
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gemm_cpu(1,0,m,n,k,1,a,m,b,n,0,c,n);
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col2im_cpu(net.workspace, l.out_c, l.out_h, l.out_w, l.size, l.stride, l.pad, l.output+i*l.outputs);
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}
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if (l.batch_normalize) {
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forward_batchnorm_layer(l, net);
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} else {
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add_bias(l.output, l.biases, l.batch, l.n, l.out_w*l.out_h);
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}
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activate_array(l.output, l.batch*l.n*l.out_w*l.out_h, l.activation);
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}
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void backward_deconvolutional_layer(layer l, network net)
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{
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int i;
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gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta);
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if(l.batch_normalize){
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backward_batchnorm_layer(l, net);
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} else {
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backward_bias(l.bias_updates, l.delta, l.batch, l.n, l.out_w*l.out_h);
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}
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//if(net.delta) memset(net.delta, 0, l.batch*l.h*l.w*l.c*sizeof(float));
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for(i = 0; i < l.batch; ++i){
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int m = l.c;
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int n = l.size*l.size*l.n;
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int k = l.h*l.w;
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float *a = net.input + i*m*k;
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float *b = net.workspace;
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float *c = l.weight_updates;
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im2col_cpu(l.delta + i*l.outputs, l.out_c, l.out_h, l.out_w,
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l.size, l.stride, l.pad, b);
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gemm_cpu(0,1,m,n,k,1,a,k,b,k,1,c,n);
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if(net.delta){
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int m = l.c;
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int n = l.h*l.w;
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int k = l.size*l.size*l.n;
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float *a = l.weights;
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float *b = net.workspace;
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float *c = net.delta + i*n*m;
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gemm_cpu(0,0,m,n,k,1,a,k,b,n,1,c,n);
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}
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}
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}
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void update_deconvolutional_layer(layer l, update_args a)
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{
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float learning_rate = a.learning_rate*l.learning_rate_scale;
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float momentum = a.momentum;
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float decay = a.decay;
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int batch = a.batch;
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int size = l.size*l.size*l.c*l.n;
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axpy_cpu(l.n, learning_rate/batch, l.bias_updates, 1, l.biases, 1);
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scal_cpu(l.n, momentum, l.bias_updates, 1);
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if(l.scales){
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axpy_cpu(l.n, learning_rate/batch, l.scale_updates, 1, l.scales, 1);
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scal_cpu(l.n, momentum, l.scale_updates, 1);
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}
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axpy_cpu(size, -decay*batch, l.weights, 1, l.weight_updates, 1);
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axpy_cpu(size, learning_rate/batch, l.weight_updates, 1, l.weights, 1);
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scal_cpu(size, momentum, l.weight_updates, 1);
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}
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