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https://github.com/pjreddie/darknet.git
synced 2023-08-10 21:13:14 +03:00
softmax does cost now, special case 1x1 convs
This commit is contained in:
Joseph Redmon
parent
508381b37f
commit
e7405b513d
4
Makefile
4
Makefile
@ -68,8 +68,8 @@ EXECOBJ = $(addprefix $(OBJDIR), $(EXECOBJA))
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OBJS = $(addprefix $(OBJDIR), $(OBJ))
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DEPS = $(wildcard src/*.h) Makefile include/darknet.h
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#all: obj backup results $(SLIB) $(ALIB) $(EXEC)
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all: obj results $(SLIB) $(ALIB) $(EXEC)
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all: obj backup results $(SLIB) $(ALIB) $(EXEC)
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#all: obj results $(SLIB) $(ALIB) $(EXEC)
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$(EXEC): $(EXECOBJ) $(ALIB)
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@ -90,6 +90,3 @@ activation=linear
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[softmax]
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groups=1
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[cost]
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type=sse
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@ -119,6 +119,3 @@ activation=leaky
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[softmax]
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groups=1
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[cost]
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@ -115,5 +115,3 @@ activation=leaky
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groups=1
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temperature=3
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[cost]
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@ -203,6 +203,3 @@ activation=linear
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[softmax]
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groups=1
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[cost]
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type=sse
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@ -195,6 +195,3 @@ activation=linear
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[softmax]
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groups=1
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[cost]
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type=sse
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@ -1949,6 +1949,3 @@ activation=linear
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[softmax]
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groups=1
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[cost]
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type=sse
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@ -201,6 +201,3 @@ activation=leaky
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[softmax]
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groups=1
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[cost]
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type=sse
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@ -204,6 +204,3 @@ activation=leaky
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[softmax]
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groups=1
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[cost]
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type=sse
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@ -130,6 +130,3 @@ stride=1
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[softmax]
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[cost]
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type=sse
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@ -129,6 +129,4 @@ stride=1
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[softmax]
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[cost]
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type=sse
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@ -27,6 +27,4 @@ activation=linear
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[softmax]
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[cost]
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type=sse
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@ -1458,6 +1458,3 @@ activation=linear
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[softmax]
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groups=1
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[cost]
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type=sse
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@ -506,6 +506,4 @@ activation=linear
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[softmax]
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groups=1
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[cost]
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type=sse
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@ -35,6 +35,4 @@ activation=leaky
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[softmax]
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[cost]
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type=sse
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@ -35,6 +35,4 @@ activation=leaky
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[softmax]
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[cost]
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type=sse
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@ -180,6 +180,3 @@ activation=ramp
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[softmax]
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[cost]
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type=sse
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@ -171,6 +171,4 @@ activation=linear
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[softmax]
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groups=1
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[cost]
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type=sse
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@ -148,6 +148,4 @@ activation=linear
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[softmax]
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groups=1
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[cost]
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type=sse
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@ -111,9 +111,13 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network net)
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float *a = l.weights_gpu + j*l.nweights/l.groups;
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float *b = net.workspace;
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float *c = l.output_gpu + (i*l.groups + j)*n*m;
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float *im = net.input_gpu + (i*l.groups + j)*l.c/l.groups*l.h*l.w;
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im2col_gpu(net.input_gpu + (i*l.groups + j)*l.c/l.groups*l.h*l.w,
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l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, b);
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if (l.size == 1){
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b = im;
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} else {
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im2col_gpu(im, l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, b);
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}
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gemm_gpu(0,0,m,n,k,1,a,k,b,n,1,c,n);
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}
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}
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@ -237,21 +241,25 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network net)
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float *c = l.weight_updates_gpu + j*l.nweights/l.groups;
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float *im = net.input_gpu+(i*l.groups + j)*l.c/l.groups*l.h*l.w;
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float *imd = net.delta_gpu+(i*l.groups + j)*l.c/l.groups*l.h*l.w;
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im2col_gpu(im, l.c/l.groups, l.h, l.w,
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l.size, l.stride, l.pad, b);
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im2col_gpu(im, l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, b);
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gemm_gpu(0,1,m,n,k,1,a,k,b,k,1,c,n);
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if(net.delta_gpu){
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if(l.binary || l.xnor) swap_binary(&l);
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if (net.delta_gpu) {
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if (l.binary || l.xnor) swap_binary(&l);
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a = l.weights_gpu + j*l.nweights/l.groups;
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b = l.delta_gpu + (i*l.groups + j)*m*k;
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c = net.workspace;
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if (l.size == 1) {
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c = imd;
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}
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gemm_gpu(1,0,n,k,m,1,a,n,b,k,0,c,k);
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col2im_gpu(net.workspace, l.c/l.groups, l.h, l.w, l.size, l.stride,
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l.pad, net.delta_gpu + (i*l.groups + j)*l.c/l.groups*l.h*l.w);
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if (l.size != 1) {
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col2im_gpu(net.workspace, l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, imd);
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}
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if(l.binary || l.xnor) {
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swap_binary(&l);
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}
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@ -463,9 +463,13 @@ void forward_convolutional_layer(convolutional_layer l, network net)
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float *a = l.weights + j*l.nweights/l.groups;
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float *b = net.workspace;
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float *c = l.output + (i*l.groups + j)*n*m;
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float *im = net.input + (i*l.groups + j)*l.c/l.groups*l.h*l.w;
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im2col_cpu(net.input + (i*l.groups + j)*l.c/l.groups*l.h*l.w,
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l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, b);
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if (l.size == 1) {
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b = im;
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} else {
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im2col_cpu(im, l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, b);
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}
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gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
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}
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}
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@ -501,21 +505,31 @@ void backward_convolutional_layer(convolutional_layer l, network net)
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float *b = net.workspace;
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float *c = l.weight_updates + j*l.nweights/l.groups;
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float *im = net.input+(i*l.groups + j)*l.c/l.groups*l.h*l.w;
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float *im = net.input + (i*l.groups + j)*l.c/l.groups*l.h*l.w;
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float *imd = net.delta + (i*l.groups + j)*l.c/l.groups*l.h*l.w;
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if(l.size == 1){
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b = im;
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} else {
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im2col_cpu(im, l.c/l.groups, l.h, l.w,
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l.size, l.stride, l.pad, b);
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}
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gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
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if(net.delta){
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if (net.delta) {
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a = l.weights + j*l.nweights/l.groups;
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b = l.delta + (i*l.groups + j)*m*k;
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c = net.workspace;
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if (l.size == 1) {
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c = imd;
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}
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gemm(1,0,n,k,m,1,a,n,b,k,0,c,k);
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col2im_cpu(net.workspace, l.c/l.groups, l.h, l.w, l.size, l.stride,
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l.pad, net.delta + (i*l.groups + j)*l.c/l.groups*l.h*l.w);
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if (l.size != 1) {
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col2im_cpu(net.workspace, l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, imd);
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}
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}
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}
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}
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