mirror of
https://github.com/pjreddie/darknet.git
synced 2023-08-10 21:13:14 +03:00
im2col slightly faster
This commit is contained in:
Joseph Redmon
parent
af4e4f92dc
commit
1c0fd9bb47
13
src/axpy.c
13
src/axpy.c
@ -64,6 +64,11 @@ cl_kernel get_scal_kernel()
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void axpy_ongpu(int N, float ALPHA, cl_mem X, int INCX, cl_mem Y, int INCY)
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{
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axpy_ongpu_offset(N,ALPHA,X,0,INCX,Y,0,INCY);
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}
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void axpy_ongpu_offset(int N, float ALPHA, cl_mem X, int OFFX, int INCX, cl_mem Y, int OFFY, int INCY)
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{
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cl_setup();
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cl_kernel kernel = get_axpy_kernel();
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@ -73,8 +78,10 @@ void axpy_ongpu(int N, float ALPHA, cl_mem X, int INCX, cl_mem Y, int INCY)
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cl.error = clSetKernelArg(kernel, i++, sizeof(N), (void*) &N);
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cl.error = clSetKernelArg(kernel, i++, sizeof(ALPHA), (void*) &ALPHA);
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cl.error = clSetKernelArg(kernel, i++, sizeof(X), (void*) &X);
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cl.error = clSetKernelArg(kernel, i++, sizeof(OFFX), (void*) &OFFX);
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cl.error = clSetKernelArg(kernel, i++, sizeof(INCX), (void*) &INCX);
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cl.error = clSetKernelArg(kernel, i++, sizeof(Y), (void*) &Y);
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cl.error = clSetKernelArg(kernel, i++, sizeof(OFFY), (void*) &OFFY);
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cl.error = clSetKernelArg(kernel, i++, sizeof(INCY), (void*) &INCY);
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check_error(cl);
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@ -85,6 +92,10 @@ void axpy_ongpu(int N, float ALPHA, cl_mem X, int INCX, cl_mem Y, int INCY)
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}
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void copy_ongpu(int N, cl_mem X, int INCX, cl_mem Y, int INCY)
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{
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copy_ongpu_offset(N,X,0,INCX,Y,0,INCY);
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}
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void copy_ongpu_offset(int N, cl_mem X, int OFFX, int INCX, cl_mem Y, int OFFY, int INCY)
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{
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cl_setup();
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cl_kernel kernel = get_copy_kernel();
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@ -93,8 +104,10 @@ void copy_ongpu(int N, cl_mem X, int INCX, cl_mem Y, int INCY)
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cl_uint i = 0;
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cl.error = clSetKernelArg(kernel, i++, sizeof(N), (void*) &N);
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cl.error = clSetKernelArg(kernel, i++, sizeof(X), (void*) &X);
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cl.error = clSetKernelArg(kernel, i++, sizeof(OFFX), (void*) &OFFX);
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cl.error = clSetKernelArg(kernel, i++, sizeof(INCX), (void*) &INCX);
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cl.error = clSetKernelArg(kernel, i++, sizeof(Y), (void*) &Y);
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cl.error = clSetKernelArg(kernel, i++, sizeof(OFFY), (void*) &OFFY);
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cl.error = clSetKernelArg(kernel, i++, sizeof(INCY), (void*) &INCY);
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check_error(cl);
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@ -1,7 +1,7 @@
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__kernel void axpy(int N, float ALPHA, __global float *X, int INCX, __global float *Y, int INCY)
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__kernel void axpy(int N, float ALPHA, __global float *X, int OFFX, int INCX, __global float *Y, int OFFY, int INCY)
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{
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int i = get_global_id(0);
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Y[i*INCY] += ALPHA*X[i*INCX];
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Y[OFFY+i*INCY] += ALPHA*X[OFFX+i*INCX];
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}
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__kernel void scal(int N, float ALPHA, __global float *X, int INCX)
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@ -10,9 +10,9 @@ __kernel void scal(int N, float ALPHA, __global float *X, int INCX)
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X[i*INCX] *= ALPHA;
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}
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__kernel void copy(int N, __global float *X, int INCX, __global float *Y, int INCY)
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__kernel void copy(int N, __global float *X, int OFFX, int INCX, __global float *Y, int OFFY, int INCY)
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{
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int i = get_global_id(0);
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Y[i*INCY] = X[i*INCX];
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Y[i*INCY + OFFY] = X[i*INCX + OFFX];
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}
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23
src/cnn.c
23
src/cnn.c
@ -308,10 +308,10 @@ void train_assira()
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void train_imagenet()
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{
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network net = parse_network_cfg("/home/pjreddie/imagenet_backup/imagenet_backup_slower_larger_870.cfg");
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network net = parse_network_cfg("cfg/imagenet_backup_slowest_2340.cfg");
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printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
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int imgs = 1000/net.batch+1;
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srand(986987);
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srand(6472345);
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int i = 0;
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char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list");
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list *plist = get_paths("/data/imagenet/cls.train.list");
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@ -332,7 +332,7 @@ void train_imagenet()
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free_data(train);
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if(i%10==0){
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char buff[256];
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sprintf(buff, "/home/pjreddie/imagenet_backup/imagenet_backup_larger_%d.cfg", i);
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sprintf(buff, "/home/pjreddie/imagenet_backup/imagenet_small_%d.cfg", i);
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save_network(net, buff);
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}
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}
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@ -397,7 +397,7 @@ void test_imagenet()
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void test_visualize()
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{
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network net = parse_network_cfg("cfg/imagenet_test.cfg");
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network net = parse_network_cfg("cfg/imagenet.cfg");
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visualize_network(net);
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cvWaitKey(0);
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}
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@ -991,7 +991,7 @@ void test_gpu_net()
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translate_data_rows(train, -144);
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translate_data_rows(test, -144);
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int count = 0;
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int iters = 10000/net.batch;
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int iters = 1000/net.batch;
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while(++count <= 5){
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clock_t start = clock(), end;
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float loss = train_network_sgd(net, train, iters);
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@ -999,6 +999,7 @@ void test_gpu_net()
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float test_acc = network_accuracy(net, test);
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printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC, net.learning_rate, net.momentum, net.decay);
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}
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#ifdef GPU
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count = 0;
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srand(222222);
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net = parse_network_cfg("cfg/nist.cfg");
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@ -1009,6 +1010,7 @@ void test_gpu_net()
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float test_acc = network_accuracy(net, test);
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printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC, net.learning_rate, net.momentum, net.decay);
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}
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#endif
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}
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@ -1020,13 +1022,12 @@ int main(int argc, char *argv[])
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}
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if(0==strcmp(argv[1], "train")) train_imagenet();
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else if(0==strcmp(argv[1], "train_small")) train_imagenet_small();
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else if(0==strcmp(argv[1], "test_correct")) test_gpu_net();
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else if(0==strcmp(argv[1], "test")) test_imagenet();
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else if(0==strcmp(argv[1], "visualize")) test_visualize();
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#ifdef GPU
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else if(0==strcmp(argv[1], "test_gpu")) test_gpu_blas();
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else if(0==strcmp(argv[1], "test")) test_gpu_net();
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//test_gpu_blas();
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//train_imagenet_small();
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//test_imagenet();
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//train_nist();
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//test_visualize();
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#endif
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fprintf(stderr, "Success!\n");
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return 0;
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}
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@ -135,9 +135,7 @@ void forward_connected_layer_gpu(connected_layer layer, cl_mem input)
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{
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int i;
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for(i = 0; i < layer.batch; ++i){
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cl_mem sub = cl_sub_array(layer.output_cl, i*layer.outputs, layer.outputs);
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copy_ongpu(layer.outputs, layer.biases_cl, 1, sub, 1);
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clReleaseMemObject(sub);
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copy_ongpu_offset(layer.outputs, layer.biases_cl, 0, 1, layer.output_cl, i*layer.outputs, 1);
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}
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int m = layer.batch;
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int k = layer.inputs;
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@ -154,9 +152,7 @@ void backward_connected_layer_gpu(connected_layer layer, cl_mem input, cl_mem de
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int i;
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gradient_array_ongpu(layer.output_cl, layer.outputs*layer.batch, layer.activation, layer.delta_cl);
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for(i = 0; i < layer.batch; ++i){
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cl_mem sub = cl_sub_array(layer.delta_cl, i*layer.outputs, layer.outputs);
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axpy_ongpu(layer.outputs, 1, sub, 1, layer.bias_updates_cl, 1);
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clReleaseMemObject(sub);
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axpy_ongpu_offset(layer.outputs, 1, layer.delta_cl, i*layer.outputs, 1, layer.bias_updates_cl, 0, 1);
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}
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int m = layer.inputs;
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int k = layer.batch;
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47
src/im2col.c
47
src/im2col.c
@ -51,12 +51,23 @@ void im2col_cpu(float* data_im, int batch,
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#include "opencl.h"
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#include <math.h>
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cl_kernel get_im2col_kernel()
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cl_kernel get_im2col_pad_kernel()
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{
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static int init = 0;
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static cl_kernel im2col_kernel;
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if(!init){
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im2col_kernel = get_kernel("src/im2col.cl", "im2col", 0);
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im2col_kernel = get_kernel("src/im2col.cl", "im2col_pad", 0);
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init = 1;
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}
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return im2col_kernel;
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}
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cl_kernel get_im2col_nopad_kernel()
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{
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static int init = 0;
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static cl_kernel im2col_kernel;
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if(!init){
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im2col_kernel = get_kernel("src/im2col.cl", "im2col_nopad", 0);
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init = 1;
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}
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return im2col_kernel;
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@ -68,32 +79,34 @@ void im2col_ongpu(cl_mem data_im, int batch,
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int ksize, int stride, int pad, cl_mem data_col)
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{
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cl_setup();
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cl_kernel im2col_kernel = get_im2col_kernel();
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cl_command_queue queue = cl.queue;
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cl_uint i = 0;
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cl.error = clSetKernelArg(im2col_kernel, i++, sizeof(data_im), (void*) &data_im);
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cl.error = clSetKernelArg(im2col_kernel, i++, sizeof(batch), (void*) &batch);
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cl.error = clSetKernelArg(im2col_kernel, i++, sizeof(channels), (void*) &channels);
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cl.error = clSetKernelArg(im2col_kernel, i++, sizeof(height), (void*) &height);
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cl.error = clSetKernelArg(im2col_kernel, i++, sizeof(width), (void*) &width);
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cl.error = clSetKernelArg(im2col_kernel, i++, sizeof(ksize), (void*) &ksize);
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cl.error = clSetKernelArg(im2col_kernel, i++, sizeof(stride), (void*) &stride);
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cl.error = clSetKernelArg(im2col_kernel, i++, sizeof(pad), (void*) &pad);
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cl.error = clSetKernelArg(im2col_kernel, i++, sizeof(data_col), (void*) &data_col);
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check_error(cl);
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int height_col = (height - ksize) / stride + 1;
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int width_col = (width - ksize) / stride + 1;
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int channels_col = channels * ksize * ksize;
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cl_kernel kernel = get_im2col_nopad_kernel();
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if (pad){
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height_col = 1 + (height-1) / stride;
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width_col = 1 + (width-1) / stride;
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kernel = get_im2col_pad_kernel();
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}
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cl_command_queue queue = cl.queue;
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cl_uint i = 0;
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cl.error = clSetKernelArg(kernel, i++, sizeof(data_im), (void*) &data_im);
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cl.error = clSetKernelArg(kernel, i++, sizeof(batch), (void*) &batch);
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cl.error = clSetKernelArg(kernel, i++, sizeof(channels), (void*) &channels);
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cl.error = clSetKernelArg(kernel, i++, sizeof(height), (void*) &height);
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cl.error = clSetKernelArg(kernel, i++, sizeof(width), (void*) &width);
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cl.error = clSetKernelArg(kernel, i++, sizeof(ksize), (void*) &ksize);
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cl.error = clSetKernelArg(kernel, i++, sizeof(stride), (void*) &stride);
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cl.error = clSetKernelArg(kernel, i++, sizeof(data_col), (void*) &data_col);
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check_error(cl);
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size_t global_size = batch*channels_col*height_col*width_col;
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clEnqueueNDRangeKernel(queue, im2col_kernel, 1, 0,
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clEnqueueNDRangeKernel(queue, kernel, 1, 0,
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&global_size, 0, 0, 0, 0);
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check_error(cl);
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}
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@ -1,28 +1,17 @@
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float im2col_get_pixel(__global float *im, int height, int width, int channels,
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int batch, int row, int col, int channel, int pad)
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{
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row -= pad;
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col -= pad;
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if (row < 0 || col < 0 || row >= height || col >= width) return 0;
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int index = col + width*(row + height*(channel+batch*channels));
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return im[index];
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}
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__kernel void im2col(__global float *data_im, int batch,
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__kernel void im2col_pad(__global float *im, int batch,
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int channels, int height, int width,
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int ksize, int stride, int pad, __global float *data_col)
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int ksize, int stride, __global float *data_col)
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{
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int c,h,w,b;
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int height_col = (height - ksize) / stride + 1;
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int width_col = (width - ksize) / stride + 1;
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int height_col = 1 + (height-1) / stride;
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int width_col = 1 + (width-1) / stride;
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int channels_col = channels * ksize * ksize;
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if (pad){
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height_col = 1 + (height-1) / stride;
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width_col = 1 + (width-1) / stride;
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pad = ksize/2;
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}
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int pad = ksize/2;
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int id = get_global_id(0);
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int col_index = id;
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w = id % width_col;
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id /= width_col;
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h = id % height_col;
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@ -35,9 +24,45 @@ __kernel void im2col(__global float *data_im, int batch,
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int col_size = height_col*width_col*channels_col;
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int w_offset = c % ksize;
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int h_offset = (c / ksize) % ksize;
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int c_im = c / ksize / ksize;
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int im_channel = c / ksize / ksize;
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int im_row = h_offset + h * stride - pad;
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int im_col = w_offset + w * stride - pad;
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int im_index = im_col + width*(im_row + height*(im_channel+batch*channels));
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float val = (im_row < 0 || im_col < 0 || im_row >= height || im_col >= width) ? 0 : im[im_index];
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data_col[col_index] = val;
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}
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__kernel void im2col_nopad(__global float *im, int batch,
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int channels, int height, int width,
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int ksize, int stride, __global float *data_col)
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{
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int c,h,w,b;
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int height_col = (height - ksize) / stride + 1;
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int width_col = (width - ksize) / stride + 1;
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int channels_col = channels * ksize * ksize;
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int id = get_global_id(0);
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int col_index = id;
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w = id % width_col;
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id /= width_col;
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h = id % height_col;
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id /= height_col;
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c = id % channels_col;
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id /= channels_col;
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b = id % batch;
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id /= batch;
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int col_size = height_col*width_col*channels_col;
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int w_offset = c % ksize;
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int h_offset = (c / ksize) % ksize;
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int im_channel = c / ksize / ksize;
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int im_row = h_offset + h * stride;
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int im_col = w_offset + w * stride;
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int col_index = (c * height_col + h) * width_col + w + b*col_size;
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data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, b, im_row, im_col, c_im, pad);
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int im_index = im_col + width*(im_row + height*(im_channel+batch*channels));
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float val = (im_row < 0 || im_col < 0 || im_row >= height || im_col >= width) ? 0 : im[im_index];
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data_col[col_index] = val;
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}
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@ -738,7 +738,7 @@ image collapse_images_horz(image *ims, int n)
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void show_images(image *ims, int n, char *window)
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{
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image m = collapse_images_vert(ims, n);
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//save_image(m, window);
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save_image(m, window);
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show_image(m, window);
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free_image(m);
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}
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@ -11,7 +11,9 @@ void time_random_matrix(int TA, int TB, int m, int k, int n);
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#ifdef GPU
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void axpy_ongpu(int N, float ALPHA, cl_mem X, int INCX, cl_mem Y, int INCY);
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void axpy_ongpu_offset(int N, float ALPHA, cl_mem X, int OFFX, int INCX, cl_mem Y, int OFFY, int INCY);
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void copy_ongpu(int N, cl_mem X, int INCX, cl_mem Y, int INCY);
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void copy_ongpu_offset(int N, cl_mem X, int OFFX, int INCX, cl_mem Y, int OFFY, int INCY);
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void scal_ongpu(int N, float ALPHA, cl_mem X, int INCX);
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void im2col_ongpu(cl_mem data_im, int batch,
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int channels, int height, int width,
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@ -38,7 +38,7 @@ void forward_network_gpu(network net, cl_mem input, cl_mem truth, int train)
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//printf("start\n");
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int i;
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for(i = 0; i < net.n; ++i){
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//clock_t time = clock();
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clock_t time = clock();
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if(net.types[i] == CONVOLUTIONAL){
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convolutional_layer layer = *(convolutional_layer *)net.layers[i];
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forward_convolutional_layer_gpu(layer, input);
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@ -63,7 +63,7 @@ void forward_network_gpu(network net, cl_mem input, cl_mem truth, int train)
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forward_softmax_layer_gpu(layer, input);
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input = layer.output_cl;
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}
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//printf("%d %f\n", i, sec(clock()-time));
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printf("%d %f\n", i, sec(clock()-time));
|
||||
/*
|
||||
else if(net.types[i] == CROP){
|
||||
crop_layer layer = *(crop_layer *)net.layers[i];
|
||||
@ -85,6 +85,7 @@ void backward_network_gpu(network net, cl_mem input)
|
||||
cl_mem prev_input;
|
||||
cl_mem prev_delta;
|
||||
for(i = net.n-1; i >= 0; --i){
|
||||
clock_t time = clock();
|
||||
if(i == 0){
|
||||
prev_input = input;
|
||||
prev_delta = 0;
|
||||
@ -112,6 +113,7 @@ void backward_network_gpu(network net, cl_mem input)
|
||||
softmax_layer layer = *(softmax_layer *)net.layers[i];
|
||||
backward_softmax_layer_gpu(layer, prev_delta);
|
||||
}
|
||||
printf("back: %d %f\n", i, sec(clock()-time));
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user