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Test for XNOR-conv on CUDA

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AlexeyAB
2018-09-07 23:04:46 +03:00
parent 57e878b4f9
commit c0e01fd63c
5 changed files with 378 additions and 79 deletions
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41
src/convolutional_kernels.cu
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@ -141,7 +141,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
size_t t_intput_size = new_ldb * n;
size_t t_bit_input_size = t_intput_size / 8;// +1;
/*
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();
@ -160,23 +160,46 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
(unsigned char *)l.align_bit_weights_gpu, new_ldb, (unsigned char *)l.transposed_align_workspace_gpu, new_ldb, l.output_gpu, n, l.mean_arr_gpu);
//cudaDeviceSynchronize();
//check_error(status);
*/
{
//float_to_bit_gpu(state.input, (unsigned char *)l.align_workspace_gpu, input_size);
//
/*
float *input_cpu = (float *)calloc(input_size, sizeof(float));
status = cudaMemcpy(input_cpu, state.input, input_size* sizeof(float), cudaMemcpyDeviceToHost);
check_error(status);
convolve_bin_cpu(input_cpu, l.weights, l.output, l.w, l.h, l.c, l.n, l.size, l.pad); // CPU
// swaped(binary_weights <-> l.weights)
convolve_cpu(input_cpu, l.weights, l.output, l.w, l.h, l.c, l.n, l.size, l.pad); // CPU
status = cudaMemcpy(l.output_gpu, l.output, l.outputs * sizeof(float), cudaMemcpyHostToDevice);
check_error(status);
free(input_cpu);
*/
//convolve_bin_gpu(state.input, l.weights_gpu, l.output_gpu, l.w, l.h, l.c, l.n, l.size, l.pad);
/*
float *input_cpu = (float *)calloc(input_size, sizeof(float));
float *input_bin_cpu = (float *)calloc(input_size, sizeof(char));
//float *weights_bin_cpu = (float *)calloc(l.n*l.c*l.size*l.size, sizeof(char));
status = cudaMemcpy(input_cpu, state.input, input_size * sizeof(float), cudaMemcpyDeviceToHost);
check_error(status);
float_to_bit(input_cpu, (unsigned char *)input_bin_cpu, input_size);
//float_to_bit(l.weights, (unsigned char *)weights_bin_cpu, l.n*l.c*l.size*l.size); // l.align_bit_weights
convolve_bin_cpu(input_bin_cpu, (float *)l.align_bit_weights, l.output, l.w, l.h, l.c, l.n, l.size, l.pad, l.new_lda, l.mean_arr); // CPU
status = cudaMemcpy(l.output_gpu, l.output, l.outputs * sizeof(float), cudaMemcpyHostToDevice);
check_error(status);
//free(weights_bin_cpu);
free(input_bin_cpu);
free(input_cpu);
*/
float_to_bit_gpu(state.input, (unsigned char *)l.align_workspace_gpu, input_size);
convolve_bin_gpu(l.align_workspace_gpu, (float *)l.align_bit_weights_gpu, l.output_gpu, l.w, l.h, l.c, l.n, l.size, l.pad, l.new_lda, l.mean_arr_gpu);
//convolve_gpu(state.input, l.weights_gpu, l.output_gpu, l.w, l.h, l.c, l.n, l.size, l.pad);
//cudaDeviceSynchronize();
//check_error(status);
@ -309,10 +332,16 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
int k = l.size*l.size*l.c;
int n = l.out_w*l.out_h;
for(i = 0; i < l.batch; ++i){
im2col_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, state.workspace);
float *im = state.input + i*l.c*l.h*l.w;
float * a = l.weights_gpu;
float * b = state.workspace;
float * c = l.output_gpu;
if (l.size == 1) {
b = im;
}
else {
im2col_ongpu(im, l.c, l.h, l.w, l.size, l.stride, l.pad, state.workspace);
}
gemm_ongpu(0,0,m,n,k,1.,a,k,b,n,1.,c+i*m*n,n);
}
#endif