void backward_convolutional_layer(convolutional_layer l, network_state state)
{
    int i;
    int m = l.n;
    int n = l.size*l.size*l.c;
    int k = convolutional_out_height(l)*
        convolutional_out_width(l);

    gradient_array(l.output, m*k*l.batch, l.activation, l.delta);
    backward_bias(l.bias_updates, l.delta, l.batch, l.n, k);

    for(i = 0; i < l.batch; ++i){
        float *a = l.delta + i*m*k;
        float *b = l.col_image;
        float *c = l.filter_updates;

        float *im = state.input+i*l.c*l.h*l.w;

        im2col_cpu(im, l.c, l.h, l.w, 
                l.size, l.stride, l.pad, b);
        gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);

        if(state.delta){
            a = l.filters;
            b = l.delta + i*m*k;
            c = l.col_image;

            gemm(1,0,n,k,m,1,a,n,b,k,0,c,k);

            col2im_cpu(l.col_image, l.c,  l.h,  l.w,  l.size,  l.stride, l.pad, state.delta+i*l.c*l.h*l.w);
        }
    }
}
void resize_convolutional_layer(convolutional_layer *l, int w, int 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->col_image = realloc(l->col_image,
            out_h*out_w*l->size*l->size*l->c*sizeof(float));
    l->output = realloc(l->output,
            l->batch*out_h * out_w * l->n*sizeof(float));
    l->delta  = realloc(l->delta,
            l->batch*out_h * out_w * l->n*sizeof(float));

#ifdef GPU
    cuda_free(l->col_image_gpu);
    cuda_free(l->delta_gpu);
    cuda_free(l->output_gpu);

    l->col_image_gpu = cuda_make_array(l->col_image, out_h*out_w*l->size*l->size*l->c);
    l->delta_gpu =     cuda_make_array(l->delta, l->batch*out_h*out_w*l->n);
    l->output_gpu =    cuda_make_array(l->output, l->batch*out_h*out_w*l->n);
#endif
}
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;

    fill_cpu(l.outputs*l.batch, 0, l.output, 1);
    /*
       if(l.binary){
       binarize_filters(l.filters, l.n, l.c*l.size*l.size, l.binary_filters);
       binarize_filters2(l.filters, l.n, l.c*l.size*l.size, l.cfilters, l.scales);
       swap_binary(&l);
       }
     */

    if(l.binary){
        int m = l.n;
        int k = l.size*l.size*l.c;
        int n = out_h*out_w;

        char  *a = l.cfilters;
        float *b = state.workspace;
        float *c = l.output;

        for(i = 0; i < l.batch; ++i){
            im2col_cpu(state.input, l.c, l.h, l.w, 
                    l.size, l.stride, l.pad, b);
            gemm_bin(m,n,k,1,a,k,b,n,c,n);
            c += n*m;
            state.input += l.c*l.h*l.w;
        }
        scale_bias(l.output, l.scales, l.batch, l.n, out_h*out_w);
        add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w);
        activate_array(l.output, m*n*l.batch, l.activation);
        return;
    }

    int m = l.n;
    int k = l.size*l.size*l.c;
    int n = out_h*out_w;

    float *a = l.filters;
    float *b = state.workspace;
    float *c = l.output;

    for(i = 0; i < l.batch; ++i){
        im2col_cpu(state.input, l.c, l.h, l.w, 
                l.size, l.stride, l.pad, b);
        gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
        c += n*m;
        state.input += l.c*l.h*l.w;
    }

    if(l.batch_normalize){
        forward_batchnorm_layer(l, state);
    }
    add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w);

    activate_array(l.output, m*n*l.batch, l.activation);
}
void forward_convolutional_layer(const convolutional_layer l, network_state state)
{
    int out_h = convolutional_out_height(l);
    int out_w = convolutional_out_width(l);
    int i;

    bias_output(l.output, l.biases, l.batch, l.n, out_h*out_w);

    int m = l.n;
    int k = l.size*l.size*l.c;
    int n = out_h*out_w;

    float *a = l.filters;
    float *b = l.col_image;
    float *c = l.output;

    for(i = 0; i < l.batch; ++i){
        im2col_cpu(state.input, l.c, l.h, l.w, 
            l.size, l.stride, l.pad, b);
        gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
        c += n*m;
        state.input += l.c*l.h*l.w;
    }
    activate_array(l.output, m*n*l.batch, l.activation);
}
image get_convolutional_delta(convolutional_layer l)
{
    int h,w,c;
    h = convolutional_out_height(l);
    w = convolutional_out_width(l);
    c = l.n;
    return float_to_image(w,h,c,l.delta);
}
convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation)
{
    int i;
    convolutional_layer l = {0};
    l.type = CONVOLUTIONAL;

    l.h = h;
    l.w = w;
    l.c = c;
    l.n = n;
    l.batch = batch;
    l.stride = stride;
    l.size = size;
    l.pad = pad;

    l.filters = calloc(c*n*size*size, sizeof(float));
    l.filter_updates = calloc(c*n*size*size, sizeof(float));

    l.biases = calloc(n, sizeof(float));
    l.bias_updates = calloc(n, sizeof(float));
    //float scale = 1./sqrt(size*size*c);
    float scale = sqrt(2./(size*size*c));
	printf("%f\n", scale);
    for(i = 0; i < c*n*size*size; ++i) l.filters[i] = 2*scale*rand_uniform() - scale;
    for(i = 0; i < n; ++i){
        l.biases[i] = scale;
    }
    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.col_image = calloc(out_h*out_w*size*size*c, sizeof(float));
    l.output = calloc(l.batch*out_h * out_w * n, sizeof(float));
    l.delta  = calloc(l.batch*out_h * out_w * n, sizeof(float));

    #ifdef GPU
    l.filters_gpu = cuda_make_array(l.filters, c*n*size*size);
    l.filter_updates_gpu = cuda_make_array(l.filter_updates, c*n*size*size);

    l.biases_gpu = cuda_make_array(l.biases, n);
    l.bias_updates_gpu = cuda_make_array(l.bias_updates, n);

    l.col_image_gpu = cuda_make_array(l.col_image, out_h*out_w*size*size*c);
    l.delta_gpu = cuda_make_array(l.delta, l.batch*out_h*out_w*n);
    l.output_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
    #endif
    l.activation = activation;

    fprintf(stderr, "Convolutional Layer: %d x %d x %d image, %d filters -> %d x %d x %d image\n", h,w,c,n, out_h, out_w, n);

    return l;
}
Exemple #7
0
layer make_xnor_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation, int batch_normalize)
{
    int i;
    layer l = {0};
    l.type = XNOR;

    l.h = h;
    l.w = w;
    l.c = c;
    l.n = n;
    l.batch = batch;
    l.stride = stride;
    l.size = size;
    l.pad = pad;
    l.batch_normalize = batch_normalize;

    l.filters = calloc(c*n*size*size, sizeof(float));
    l.biases = calloc(n, sizeof(float));

    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.output = calloc(l.batch*out_h * out_w * n, sizeof(float));

    if(batch_normalize){
        l.scales = calloc(n, sizeof(float));
        for(i = 0; i < n; ++i){
            l.scales[i] = 1;
        }

        l.mean = calloc(n, sizeof(float));
        l.variance = calloc(n, sizeof(float));

        l.rolling_mean = calloc(n, sizeof(float));
        l.rolling_variance = calloc(n, sizeof(float));
    }

    l.activation = activation;

    fprintf(stderr, "XNOR Layer: %d x %d x %d image, %d filters -> %d x %d x %d image\n", h,w,c,n, out_h, out_w, n);

    return l;
}
void resize_convolutional_layer(convolutional_layer *l, int w, int 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 = realloc(l->output, l->batch*l->outputs*sizeof(float));
    l->delta  = realloc(l->delta,  l->batch*l->outputs*sizeof(float));
    if(l->batch_normalize){
        l->x = realloc(l->x, l->batch*l->outputs*sizeof(float));
        l->x_norm  = realloc(l->x_norm, l->batch*l->outputs*sizeof(float));
    }

#ifdef GPU
    cuda_free(l->delta_gpu);
    cuda_free(l->output_gpu);

    l->delta_gpu =  cuda_make_array(l->delta,  l->batch*l->outputs);
    l->output_gpu = cuda_make_array(l->output, l->batch*l->outputs);

    if(l->batch_normalize){
        cuda_free(l->x_gpu);
        cuda_free(l->x_norm_gpu);

        l->x_gpu = cuda_make_array(l->output, l->batch*l->outputs);
        l->x_norm_gpu = cuda_make_array(l->output, l->batch*l->outputs);
    }
#ifdef CUDNN
    cudnn_convolutional_setup(l);
#endif
#endif
    l->workspace_size = get_workspace_size(*l);
}
void forward_convolutional_layer(const convolutional_layer l, network_state state)
{
    int out_h = convolutional_out_height(l);
    int out_w = convolutional_out_width(l);
    int i;

    fill_cpu(l.outputs*l.batch, 0, l.output, 1);

    int m = l.n;
    int k = l.size*l.size*l.c;
    int n = out_h*out_w;

    float *a = l.filters;
    float *b = l.col_image;
    float *c = l.output;
//	printf("the l.size is %i \n", l.size);
///*

//printf("the m,k,n is %i,%i,%i \n", m,k,n);
    for(i = 0; i < l.batch; ++i){
        im2col_cpu(state.input, l.c, l.h, l.w, 
                l.size, l.stride, l.pad, b);
        gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
        c += n*m;
        state.input += l.c*l.h*l.w;
    }
//*/

//add by fanghao
 /*   int ii,jj,kk,mm,pp,tt;
    int lcc = l.c;
    int lhh = l.h;
    int lww = l.w;
    int kernel = l.size;
    int pad;
    if(l.pad)
         pad = l.size/2;
    else
	 pad = l.pad;
    lhh += 2*pad;
    lww += 2*pad;
    float *dataP;
    dataP = (float *)calloc(lcc*lhh*lww, sizeof(float));


//printf("the l.h is %i \n", l.h);
//printf("the l.w is %i \n", l.w);
//printf("the lhh is %i \n", lhh);
//printf("the lww is %i \n", lww);
//printf("the pad is %i \n", pad);




    for(ii=0; ii < lcc; ii++)
        for(jj=pad; jj<lhh-pad; jj++)
             for(kk=pad; kk<lww-pad; kk++)
                dataP[ii*lhh*lww + jj*lww + kk] = state.input[ii*(lhh - 2*pad)*(lww-2*pad) + (jj - pad)*(lww - 2*pad) + kk-pad];

    for(ii=0; ii<m; ii++)
        for(jj=0; jj<out_h; jj++)
            for(kk=0; kk<out_w; kk++) {
                float tempAcc = 0.0;
                    for(mm=0; mm<lcc; mm++)
                        for(pp=0; pp<kernel; pp++)
                            for(tt=0; tt<kernel; tt++)
                                tempAcc += a[ii*lcc*kernel*kernel+mm*kernel*kernel+pp*kernel+tt]*dataP[mm*lhh*lww+(l.stride*jj+pp)*lww+l.stride*kk+tt];
                c[ii*out_h*out_w+jj*out_w+kk] = tempAcc;
                        }
//	c += n*m;
//state.input += l.c*l.h*l.w;
//
*/



    if(l.batch_normalize){
        if(state.train){
            mean_cpu(l.output, l.batch, l.n, l.out_h*l.out_w, l.mean);   
            variance_cpu(l.output, l.mean, l.batch, l.n, l.out_h*l.out_w, l.variance);   
            normalize_cpu(l.output, l.mean, l.variance, l.batch, l.n, l.out_h*l.out_w);   
        } else {
            normalize_cpu(l.output, l.rolling_mean, l.rolling_variance, l.batch, l.n, l.out_h*l.out_w);
        }
        scale_bias(l.output, l.scales, l.batch, l.n, out_h*out_w);
    }
    add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w);

    activate_array(l.output, m*n*l.batch, l.activation);
}
void resize_convolutional_layer(convolutional_layer *l, int w, int 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 = realloc(l->output,
            l->batch*out_h * out_w * l->n*sizeof(float));
    l->delta  = realloc(l->delta,
            l->batch*out_h * out_w * l->n*sizeof(float));

#ifdef GPU
    cuda_free(l->delta_gpu);
    cuda_free(l->output_gpu);

    l->delta_gpu =     cuda_make_array(l->delta, l->batch*out_h*out_w*l->n);
    l->output_gpu =    cuda_make_array(l->output, l->batch*out_h*out_w*l->n);
#ifdef CUDNN
    cudnnSetTensor4dDescriptor(l->dsrcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->c, l->h, l->w); 
    cudnnSetTensor4dDescriptor(l->ddstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w); 
    cudnnSetFilter4dDescriptor(l->dfilterDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size); 

    cudnnSetTensor4dDescriptor(l->srcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->c, l->h, l->w); 
    cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w); 
    cudnnSetFilter4dDescriptor(l->filterDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size); 
    int padding = l->pad ? l->size/2 : 0;
    cudnnSetConvolution2dDescriptor(l->convDesc, padding, padding, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION);
    cudnnGetConvolutionForwardAlgorithm(cudnn_handle(),
            l->srcTensorDesc,
            l->filterDesc,
            l->convDesc,
            l->dstTensorDesc,
            CUDNN_CONVOLUTION_FWD_PREFER_FASTEST,
            0,
            &l->fw_algo);
    cudnnGetConvolutionBackwardDataAlgorithm(cudnn_handle(),
            l->filterDesc,
            l->ddstTensorDesc,
            l->convDesc,
            l->dsrcTensorDesc,
            CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST,
            0,
            &l->bd_algo);
    cudnnGetConvolutionBackwardFilterAlgorithm(cudnn_handle(),
            l->srcTensorDesc,
            l->ddstTensorDesc,
            l->convDesc,
            l->dfilterDesc,
            CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST,
            0,
            &l->bf_algo);
#endif
#endif
    l->workspace_size = get_workspace_size(*l);
}
convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation, int batch_normalize, int binary, int xnor)
{
    int i;
    convolutional_layer l = {0};
    l.type = CONVOLUTIONAL;

    l.h = h;
    l.w = w;
    l.c = c;
    l.n = n;
    l.binary = binary;
    l.xnor = xnor;
    l.batch = batch;
    l.stride = stride;
    l.size = size;
    l.pad = pad;
    l.batch_normalize = batch_normalize;

    l.filters = calloc(c*n*size*size, sizeof(float));
    l.filter_updates = calloc(c*n*size*size, sizeof(float));

    l.biases = calloc(n, sizeof(float));
    l.bias_updates = calloc(n, sizeof(float));

    // float scale = 1./sqrt(size*size*c);
    float scale = sqrt(2./(size*size*c));
    for(i = 0; i < c*n*size*size; ++i) l.filters[i] = scale*rand_uniform(-1, 1);
    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.output = calloc(l.batch*out_h * out_w * n, sizeof(float));
    l.delta  = calloc(l.batch*out_h * out_w * n, sizeof(float));

    if(binary){
        l.binary_filters = calloc(c*n*size*size, sizeof(float));
        l.cfilters = calloc(c*n*size*size, sizeof(char));
        l.scales = calloc(n, sizeof(float));
    }
    if(xnor){
        l.binary_filters = calloc(c*n*size*size, sizeof(float));
        l.binary_input = calloc(l.inputs*l.batch, sizeof(float));
    }

    if(batch_normalize){
        l.scales = calloc(n, sizeof(float));
        l.scale_updates = calloc(n, sizeof(float));
        for(i = 0; i < n; ++i){
            l.scales[i] = 1;
        }

        l.mean = calloc(n, sizeof(float));
        l.variance = calloc(n, sizeof(float));

        l.rolling_mean = calloc(n, sizeof(float));
        l.rolling_variance = calloc(n, sizeof(float));
    }

#ifdef GPU
    l.filters_gpu = cuda_make_array(l.filters, c*n*size*size);
    l.filter_updates_gpu = cuda_make_array(l.filter_updates, c*n*size*size);

    l.biases_gpu = cuda_make_array(l.biases, n);
    l.bias_updates_gpu = cuda_make_array(l.bias_updates, n);

    l.scales_gpu = cuda_make_array(l.scales, n);
    l.scale_updates_gpu = cuda_make_array(l.scale_updates, n);

    l.delta_gpu = cuda_make_array(l.delta, l.batch*out_h*out_w*n);
    l.output_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);

    if(binary){
        l.binary_filters_gpu = cuda_make_array(l.filters, c*n*size*size);
    }
    if(xnor){
        l.binary_filters_gpu = cuda_make_array(l.filters, c*n*size*size);
        l.binary_input_gpu = cuda_make_array(0, l.inputs*l.batch);
    }

    if(batch_normalize){
        l.mean_gpu = cuda_make_array(l.mean, n);
        l.variance_gpu = cuda_make_array(l.variance, n);

        l.rolling_mean_gpu = cuda_make_array(l.mean, n);
        l.rolling_variance_gpu = cuda_make_array(l.variance, n);

        l.mean_delta_gpu = cuda_make_array(l.mean, n);
        l.variance_delta_gpu = cuda_make_array(l.variance, n);

        l.x_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
        l.x_norm_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
    }
#ifdef CUDNN
    cudnnCreateTensorDescriptor(&l.srcTensorDesc);
    cudnnCreateTensorDescriptor(&l.dstTensorDesc);
    cudnnCreateFilterDescriptor(&l.filterDesc);
    cudnnCreateTensorDescriptor(&l.dsrcTensorDesc);
    cudnnCreateTensorDescriptor(&l.ddstTensorDesc);
    cudnnCreateFilterDescriptor(&l.dfilterDesc);
    cudnnCreateConvolutionDescriptor(&l.convDesc);
    cudnnSetTensor4dDescriptor(l.dsrcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.c, l.h, l.w); 
    cudnnSetTensor4dDescriptor(l.ddstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.out_c, l.out_h, l.out_w); 
    cudnnSetFilter4dDescriptor(l.dfilterDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l.n, l.c, l.size, l.size); 

    cudnnSetTensor4dDescriptor(l.srcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.c, l.h, l.w); 
    cudnnSetTensor4dDescriptor(l.dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.out_c, l.out_h, l.out_w); 
    cudnnSetFilter4dDescriptor(l.filterDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l.n, l.c, l.size, l.size); 
    int padding = l.pad ? l.size/2 : 0;
    cudnnSetConvolution2dDescriptor(l.convDesc, padding, padding, l.stride, l.stride, 1, 1, CUDNN_CROSS_CORRELATION);
    cudnnGetConvolutionForwardAlgorithm(cudnn_handle(),
            l.srcTensorDesc,
            l.filterDesc,
            l.convDesc,
            l.dstTensorDesc,
            CUDNN_CONVOLUTION_FWD_PREFER_FASTEST,
            0,
            &l.fw_algo);
    cudnnGetConvolutionBackwardDataAlgorithm(cudnn_handle(),
            l.filterDesc,
            l.ddstTensorDesc,
            l.convDesc,
            l.dsrcTensorDesc,
            CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST,
            0,
            &l.bd_algo);
    cudnnGetConvolutionBackwardFilterAlgorithm(cudnn_handle(),
            l.srcTensorDesc,
            l.ddstTensorDesc,
            l.convDesc,
            l.dfilterDesc,
            CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST,
            0,
            &l.bf_algo);
#endif
#endif
    l.workspace_size = get_workspace_size(l);
    l.activation = activation;

    fprintf(stderr, "Convolutional Layer: %d x %d x %d image, %d filters -> %d x %d x %d image\n", h,w,c,n, out_h, out_w, n);

    return l;
}
convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int groups, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam)
{
    int i;
    convolutional_layer l = {0};
    l.type = CONVOLUTIONAL;

    l.groups = groups;
    l.h = h;
    l.w = w;
    l.c = c;
    l.n = n;
    l.binary = binary;
    l.xnor = xnor;
    l.batch = batch;
    l.stride = stride;
    l.size = size;
    l.pad = padding;
    l.batch_normalize = batch_normalize;

    l.weights = calloc(c/groups*n*size*size, sizeof(float));
    l.weight_updates = calloc(c/groups*n*size*size, sizeof(float));

    l.biases = calloc(n, sizeof(float));
    l.bias_updates = calloc(n, sizeof(float));

    l.nweights = c/groups*n*size*size;
    l.nbiases = n;

    // float scale = 1./sqrt(size*size*c);
    float scale = sqrt(2./(size*size*c/l.groups));
    //scale = .02;
    //for(i = 0; i < c*n*size*size; ++i) l.weights[i] = scale*rand_uniform(-1, 1);
    for(i = 0; i < l.nweights; ++i) l.weights[i] = scale*rand_normal();
    int out_w = convolutional_out_width(l);
    int out_h = convolutional_out_height(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.output = calloc(l.batch*l.outputs, sizeof(float));
    l.delta  = calloc(l.batch*l.outputs, sizeof(float));

    l.forward = forward_convolutional_layer;
    l.backward = backward_convolutional_layer;
    l.update = update_convolutional_layer;
    if(binary){
        l.binary_weights = calloc(l.nweights, sizeof(float));
        l.cweights = calloc(l.nweights, sizeof(char));
        l.scales = calloc(n, sizeof(float));
    }
    if(xnor){
        l.binary_weights = calloc(l.nweights, sizeof(float));
        l.binary_input = calloc(l.inputs*l.batch, sizeof(float));
    }

    if(batch_normalize){
        l.scales = calloc(n, sizeof(float));
        l.scale_updates = calloc(n, sizeof(float));
        for(i = 0; i < n; ++i){
            l.scales[i] = 1;
        }

        l.mean = calloc(n, sizeof(float));
        l.variance = calloc(n, sizeof(float));

        l.mean_delta = calloc(n, sizeof(float));
        l.variance_delta = calloc(n, sizeof(float));

        l.rolling_mean = calloc(n, sizeof(float));
        l.rolling_variance = calloc(n, sizeof(float));
        l.x = calloc(l.batch*l.outputs, sizeof(float));
        l.x_norm = calloc(l.batch*l.outputs, sizeof(float));
    }
    if(adam){
        l.m = calloc(l.nweights, sizeof(float));
        l.v = calloc(l.nweights, sizeof(float));
        l.bias_m = calloc(n, sizeof(float));
        l.scale_m = calloc(n, sizeof(float));
        l.bias_v = calloc(n, sizeof(float));
        l.scale_v = calloc(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 (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);
        }

        l.weights_gpu = cuda_make_array(l.weights, l.nweights);
        l.weight_updates_gpu = cuda_make_array(l.weight_updates, l.nweights);

        l.biases_gpu = cuda_make_array(l.biases, n);
        l.bias_updates_gpu = cuda_make_array(l.bias_updates, n);

        l.delta_gpu = cuda_make_array(l.delta, l.batch*out_h*out_w*n);
        l.output_gpu = cuda_make_array(l.output, l.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.binary_input_gpu = cuda_make_array(0, l.inputs*l.batch);
        }

        if(batch_normalize){
            l.mean_gpu = cuda_make_array(l.mean, n);
            l.variance_gpu = cuda_make_array(l.variance, n);

            l.rolling_mean_gpu = cuda_make_array(l.mean, n);
            l.rolling_variance_gpu = cuda_make_array(l.variance, n);

            l.mean_delta_gpu = cuda_make_array(l.mean, n);
            l.variance_delta_gpu = cuda_make_array(l.variance, n);

            l.scales_gpu = cuda_make_array(l.scales, n);
            l.scale_updates_gpu = cuda_make_array(l.scale_updates, n);

            l.x_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
            l.x_norm_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
        }
#ifdef CUDNN
        cudnnCreateTensorDescriptor(&l.normTensorDesc);
        cudnnCreateTensorDescriptor(&l.srcTensorDesc);
        cudnnCreateTensorDescriptor(&l.dstTensorDesc);
        cudnnCreateFilterDescriptor(&l.weightDesc);
        cudnnCreateTensorDescriptor(&l.dsrcTensorDesc);
        cudnnCreateTensorDescriptor(&l.ddstTensorDesc);
        cudnnCreateFilterDescriptor(&l.dweightDesc);
        cudnnCreateConvolutionDescriptor(&l.convDesc);
        cudnn_convolutional_setup(&l);
#endif
    }
#endif
    l.workspace_size = get_workspace_size(l);
    l.activation = activation;

    //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);

    return l;
}