mirror/darknet
1
0
Fork 0
mirror of https://github.com/pjreddie/darknet.git synced 2023-08-10 21:13:14 +03:00
Code Issues Projects Releases Wiki Activity

XNOR-net tiny-yolo_xnor.cfg ~2x faster than cuDNN on CUDA (nVidia GPU Maxwell)

Browse Source
This commit is contained in:
AlexeyAB
2018-09-22 02:01:14 +03:00
parent 0224ba3d0d
commit 7dd97537fb
15 changed files with 511 additions and 42 deletions
Download Patch File Download Diff File Expand all files Collapse all files

47
src/convolutional_kernels.cu
View File

@ -119,18 +119,15 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
if(l.xnor){
if (!l.align_bit_weights_gpu || state.train) {
binarize_weights_gpu(l.weights_gpu, l.n, l.c*l.size*l.size, l.binary_weights_gpu);
swap_binary(&l);
binarize_gpu(state.input, l.c*l.h*l.w*l.batch, l.binary_input_gpu);
state.input = l.binary_input_gpu;
}
//swap_binary(&l);
//binarize_gpu(state.input, l.c*l.h*l.w*l.batch, l.binary_input_gpu);
//state.input = l.binary_input_gpu;
//cudaDeviceSynchronize();
if (l.align_bit_weights_gpu && !state.train)
if (l.align_bit_weights_gpu && !state.train && l.c >= 256 && l.size > 1)
{
//return;
cudaError_t status = cudaSuccess;
int input_size = l.c*l.h*l.w*l.batch;
@ -146,15 +143,25 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
//if(0)
{
//cudaDeviceSynchronize();
int i = 0;
im2col_align_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, l.align_workspace_gpu, l.bit_align);
//cudaDeviceSynchronize();
if (l.stride == 1 && l.c >= 256 && l.w > 13 && l.size > 1 && 0) // disabled
{
// stride=1 only
im2col_align_bin_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, state.workspace, l.bit_align);
//cudaDeviceSynchronize();
}
else
{
im2col_align_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, l.align_workspace_gpu, l.bit_align);
//cudaDeviceSynchronize();
//getchar();
// should be optimized
float_to_bit_gpu(l.align_workspace_gpu, (unsigned char *)state.workspace, l.align_workspace_size);
//cudaDeviceSynchronize();
//im2col_align_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, state.workspace, l.bit_align);
// should be optimized
float_to_bit_gpu(l.align_workspace_gpu, (unsigned char *)state.workspace, l.align_workspace_size);
//cudaDeviceSynchronize();
}
transpose_bin_gpu((unsigned char *)state.workspace, (unsigned char *)l.transposed_align_workspace_gpu, k, n, l.bit_align, new_ldb, 8);
//cudaDeviceSynchronize();
@ -197,7 +204,13 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
}
}
fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1);
if (l.xnor) {
swap_binary(&l);
binarize_gpu(state.input, l.c*l.h*l.w*l.batch, l.binary_input_gpu);
state.input = l.binary_input_gpu;
}
//fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1);
#ifdef CUDNN
float one = 1; // alpha[0], beta[0] is float for HALF and FLOAT
@ -294,7 +307,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
#else
cudnnConvolutionForward(cudnn_handle(),
&one,
&alpha, //&one,
l.srcTensorDesc,
state.input,
l.weightDesc,
@ -303,9 +316,11 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
l.fw_algo,
state.workspace,
l.workspace_size,
&one,
&beta, //&one,
l.dstTensorDesc,
l.output_gpu);
//cudaDeviceSynchronize();
#endif // CUDNN_HALF
@ -338,7 +353,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
}
#endif // no CUDNN_HALF
activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation);
if (l.activation != LINEAR) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation);
//if(l.dot > 0) dot_error_gpu(l);
if(l.binary || l.xnor) swap_binary(&l);
//cudaDeviceSynchronize(); // for correct profiling of performance