inline void setConvolutionDesc(cudnnConvolutionDescriptor_t* conv,
cudnnTensorDescriptor_t bottom, cudnnFilterDescriptor_t filter,
int pad_h, int pad_w, int stride_h, int stride_w) {
#if CUDNN_VERSION_MIN(6, 0, 0)
CUDNN_CHECK(cudnnSetConvolution2dDescriptor(*conv,
pad_h, pad_w, stride_h, stride_w, 1, 1, CUDNN_CROSS_CORRELATION,
dataType<Dtype>::type));
#else
CUDNN_CHECK(cudnnSetConvolution2dDescriptor(*conv,
pad_h, pad_w, stride_h, stride_w, 1, 1, CUDNN_CROSS_CORRELATION));
#endif
}
void cudnn_convolutional_setup(layer *l)
{
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);
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);
cudnnSetTensor4dDescriptor(l->normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l->out_c, 1, 1);
cudnnSetFilter4dDescriptor(l->dweightDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l->n, l->c/l->groups, l->size, l->size);
cudnnSetFilter4dDescriptor(l->weightDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l->n, l->c/l->groups, l->size, l->size);
#if CUDNN_MAJOR >= 6
cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT);
#else
cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION);
#endif
#if CUDNN_MAJOR >= 7
cudnnSetConvolutionGroupCount(l->convDesc, l->groups);
#else
if(l->groups > 1){
error("CUDNN < 7 doesn't support groups, please upgrade!");
}
#endif
cudnnGetConvolutionForwardAlgorithm(cudnn_handle(),
l->srcTensorDesc,
l->weightDesc,
l->convDesc,
l->dstTensorDesc,
CUDNN_CONVOLUTION_FWD_PREFER_FASTEST,
0,
&l->fw_algo);
cudnnGetConvolutionBackwardDataAlgorithm(cudnn_handle(),
l->weightDesc,
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->dweightDesc,
CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST,
0,
&l->bf_algo);
}
void ConvBC01CuDNN<T>::fprop(const T *imgs, const T *filters, int n_imgs,
int n_channels, int n_filters, int img_h, int img_w, int filter_h,
int filter_w, T *convout) {
bool set_conv_desc = false;
if (n_imgs != this->n_imgs || n_channels != this->n_channels ||
img_h != this->img_h || img_w != this->img_w) {
CUDNN_CHECK(cudnnSetTensor4dDescriptor(
imgs_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, n_imgs, n_channels,
img_h, img_w
));
this->n_imgs = n_imgs;
this->n_channels = n_channels;
this->img_h = img_h;
this->img_w = img_w;
set_conv_desc = true;
}
if (n_filters != this->n_filters || n_channels != this->n_channels ||
filter_h != this->filter_h || filter_w != this->filter_w) {
CUDNN_CHECK(cudnnSetFilter4dDescriptor(
filters_desc, CUDNN_DATA_FLOAT, n_filters, n_channels, filter_h,
filter_w
));
this->n_filters = n_filters;
this->n_channels = n_channels;
this->filter_h = filter_h;
this->filter_w = filter_w;
set_conv_desc = true;
}
if (set_conv_desc) {
CUDNN_CHECK(cudnnSetConvolution2dDescriptor(
conv_desc, pad_y, pad_x, stride_y, stride_x, 1, 1, CUDNN_CONVOLUTION
));
int n, c, h, w;
CUDNN_CHECK(cudnnGetConvolution2dForwardOutputDim(
conv_desc, imgs_desc, filters_desc, &n, &c, &h, &w
));
CUDNN_CHECK(cudnnSetTensor4dDescriptor(
convout_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, n, c, h, w
));
CUDNN_CHECK(cudnnGetConvolutionForwardAlgorithm(
CUDNN::handle(), imgs_desc, filters_desc, conv_desc, convout_desc,
CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT, WORKSPACE_LIMIT,
&fwd_algo
));
CUDNN_CHECK(cudnnGetConvolutionForwardWorkspaceSize(
CUDNN::handle(), imgs_desc, filters_desc, conv_desc, convout_desc,
fwd_algo, &workspace_size
));
}
void *workspace = NULL;
if (workspace_size > 0) {
workspace = CUDA::buffer(workspace_size);
}
CUDNN_CHECK(cudnnConvolutionForward(
CUDNN::handle(), &CUDNN::one, imgs_desc, imgs, filters_desc, filters,
conv_desc, fwd_algo, workspace, workspace_size, &CUDNN::zero,
convout_desc, convout
));
}
void convolution_layer_updater_cuda::updater_configured()
{
nnforge_shared_ptr<const convolution_layer> layer_derived = nnforge_dynamic_pointer_cast<const convolution_layer>(layer_schema);
window_sizes = layer_derived->window_sizes;
zero_padding = layer_derived->left_zero_padding;
for(int i = 0; i < window_sizes.size(); ++i)
{
if (zero_padding[i] != layer_derived->right_zero_padding[i])
throw neural_network_exception("cuDNN is not able to run convolution when left and right padding sizes don't match");
}
cudnn_safe_call(cudnnSetFilter4dDescriptor(
weights_desc,
CUDNN_DATA_FLOAT,
output_configuration_specific.feature_map_count,
input_configuration_specific.feature_map_count,
(window_sizes.size() > 1) ? window_sizes[1] : 1,
window_sizes[0]));
cudnn_safe_call(cudnnSetTensor4dDescriptor(
bias_desc,
CUDNN_TENSOR_NCHW,
CUDNN_DATA_FLOAT,
1,
output_configuration_specific.feature_map_count,
1,
1));
cudnn_safe_call(cudnnSetConvolution2dDescriptor(
convolution_desc,
(zero_padding.size() > 1) ? zero_padding[1] : 1,
zero_padding[0],
1,
1,
1,
1,
CUDNN_CROSS_CORRELATION));
}
void cudnn_convolutional_setup(layer *l)
{
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->dweightDesc, 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->weightDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size);
//cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION);
cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT);
cudnnGetConvolutionForwardAlgorithm(cudnn_handle(),
l->srcTensorDesc,
l->weightDesc,
l->convDesc,
l->dstTensorDesc,
CUDNN_CONVOLUTION_FWD_PREFER_FASTEST,
0,
&l->fw_algo);
cudnnGetConvolutionBackwardDataAlgorithm(cudnn_handle(),
l->weightDesc,
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->dweightDesc,
CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST,
0,
&l->bf_algo);
}
inline void createConvolutionDesc(cudnnConvolutionDescriptor_t* conv,
int pad_h, int pad_w, int stride_h, int stride_w) {
CUDNN_CHECK(cudnnCreateConvolutionDescriptor(conv));
CUDNN_CHECK(cudnnSetConvolution2dDescriptor(*conv, pad_h, pad_w, stride_h,
stride_w, 1, 1, CUDNN_CROSS_CORRELATION));
}
inline void setConvolutionDesc(cudnnConvolutionDescriptor_t* conv,
cudnnTensorDescriptor_t bottom, cudnnFilterDescriptor_t filter,
int pad_h, int pad_w, int stride_h, int stride_w) {
CUDNN_CHECK(cudnnSetConvolution2dDescriptor(*conv,
pad_h, pad_w, stride_h, stride_w, 1, 1, CUDNN_CROSS_CORRELATION));
}
void ConvBC01CuDNN<T>::fprop(const T *imgs, const T *filters, int n_imgs,
int n_channels, int n_filters, int img_h, int img_w, int filter_h,
int filter_w, T *convout) {
bool set_conv_desc = false;
if (n_imgs != this->n_imgs || n_channels != this->n_channels ||
img_h != this->img_h || img_w != this->img_w) {
CUDNN_CHECK(cudnnSetTensor4dDescriptor(
imgs_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, n_imgs, n_channels,
img_h, img_w
));
this->n_imgs = n_imgs;
this->n_channels = n_channels;
this->img_h = img_h;
this->img_w = img_w;
set_conv_desc = true;
}
if (n_filters != this->n_filters || n_channels != this->n_channels ||
filter_h != this->filter_h || filter_w != this->filter_w) {
CUDNN_CHECK(cudnnSetFilter4dDescriptor(
filters_desc, CUDNN_DATA_FLOAT, n_filters, n_channels, filter_h,
filter_w
));
this->n_filters = n_filters;
this->n_channels = n_channels;
this->filter_h = filter_h;
this->filter_w = filter_w;
set_conv_desc = true;
}
if (set_conv_desc) {
CUDNN_CHECK(cudnnSetConvolution2dDescriptor(
conv_desc, pad_y, pad_x, stride_y, stride_x, 1, 1, CUDNN_CONVOLUTION
));
int n, c, h, w;
CUDNN_CHECK(cudnnGetConvolution2dForwardOutputDim(
conv_desc, imgs_desc, filters_desc, &n, &c, &h, &w
));
CUDNN_CHECK(cudnnSetTensor4dDescriptor(
convout_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, n, c, h, w
));
const int n_requestedAlgo = 10;
int n_returnedAlgo;
cudnnConvolutionFwdAlgoPerf_t fwd_algo_perf[n_requestedAlgo];
CUDNN_CHECK(cudnnFindConvolutionForwardAlgorithm(
CUDNN::handle(), imgs_desc, filters_desc, conv_desc, convout_desc,
n_requestedAlgo, &n_returnedAlgo, fwd_algo_perf
));
if (n_returnedAlgo == 0) {
throw std::runtime_error("No cudnnConvolutionFwdAlgoPerf_t found");
}
fwd_algo = fwd_algo_perf[0].algo;
cudnnConvolutionBwdDataAlgoPerf_t bwd_data_algo_perf[n_requestedAlgo];
CUDNN_CHECK(cudnnFindConvolutionBackwardDataAlgorithm(
CUDNN::handle(), filters_desc, convout_desc, conv_desc, imgs_desc,
n_requestedAlgo, &n_returnedAlgo, bwd_data_algo_perf
));
if (n_returnedAlgo == 0) {
throw std::runtime_error("No cudnnConvolutionBwdDataAlgoPerf_t found");
}
bwd_imgs_algo = bwd_data_algo_perf[0].algo;
cudnnConvolutionBwdFilterAlgoPerf_t bwd_filters_algo_perf[n_requestedAlgo];
CUDNN_CHECK(cudnnFindConvolutionBackwardFilterAlgorithm(
CUDNN::handle(), imgs_desc, convout_desc, conv_desc, filters_desc,
n_requestedAlgo, &n_returnedAlgo, bwd_filters_algo_perf
));
if (n_returnedAlgo == 0) {
throw std::runtime_error("No cudnnConvolutionBwdFilterAlgoPerf_t found");
}
bwd_filters_algo = bwd_filters_algo_perf[0].algo;
size_t fwd_workspace_size;
size_t bwd_imgs_workspace_size;
size_t bwd_filters_workspace_size;
CUDNN_CHECK(cudnnGetConvolutionForwardWorkspaceSize(
CUDNN::handle(), imgs_desc, filters_desc, conv_desc, convout_desc,
fwd_algo, &fwd_workspace_size
));
CUDNN_CHECK(cudnnGetConvolutionBackwardDataWorkspaceSize(
CUDNN::handle(), filters_desc, convout_desc, conv_desc, imgs_desc,
bwd_imgs_algo, &bwd_imgs_workspace_size
));
CUDNN_CHECK(cudnnGetConvolutionBackwardFilterWorkspaceSize(
CUDNN::handle(), imgs_desc, convout_desc, conv_desc, filters_desc,
bwd_filters_algo, &bwd_filters_workspace_size
));
workspace_size = std::max(fwd_workspace_size, bwd_imgs_workspace_size);
workspace_size = std::max(workspace_size, bwd_filters_workspace_size);
}
void *workspace = NULL;
if (workspace_size > 0) {
workspace = CUDA::buffer(workspace_size);
}
CUDNN_CHECK(cudnnConvolutionForward(
CUDNN::handle(), &CUDNN::one, imgs_desc, imgs, filters_desc, filters,
conv_desc, fwd_algo, workspace, workspace_size, &CUDNN::zero,
convout_desc, convout
));
}
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;
}
