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hate warnings

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This commit is contained in:
Joseph Redmon 2016-06-14 11:30:28 -07:00
parent 729ce43e6e
commit 8322a58cf6
9 changed files with 89 additions and 94 deletions
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2
src/art.c
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@ -53,7 +53,7 @@ void demo_art(char *cfgfile, char *weightfile, int cam_index)
printf("[");
int upper = 30;
for(i = 0; i < upper; ++i){
printf("%s", ((i+.5) < score*upper) ? "\u2588" : " ");
printf("%c", ((i+.5) < score*upper) ? 219 : ' ');
}
printf("]\n");

4
src/classifier.c
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@ -51,7 +51,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int clear)
}
if(clear) *net.seen = 0;
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = net.batch;
int imgs = net.batch*net.subdivisions;
list *options = read_data_cfg(datacfg);
@ -338,10 +338,10 @@ void validate_classifier_single(char *datacfg, char *filename, char *weightfile)
{
int i, j;
network net = parse_network_cfg(filename);
set_batch_network(&net, 1);
if(weightfile){
load_weights(&net, weightfile);
}
set_batch_network(&net, 1);
srand(time(0));
list *options = read_data_cfg(datacfg);

24
src/convolutional_kernels.cu
View File

@ -72,10 +72,6 @@ void binarize_filters_gpu(float *filters, int n, int size, float *binary)
void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
{
int i;
int m = l.n;
int k = l.size*l.size*l.c;
int n = convolutional_out_height(l)*
convolutional_out_width(l);
fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1);
if(l.binary){
@ -109,6 +105,9 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
l.output_gpu);
#else
int m = l.n;
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 * a = l.filters_gpu;
@ -121,23 +120,18 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
if (l.batch_normalize) {
forward_batchnorm_layer_gpu(l, state);
}
add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, n);
add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h);
activate_array_ongpu(l.output_gpu, m*n*l.batch, l.activation);
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);
}
void backward_convolutional_layer_gpu(convolutional_layer l, network_state state)
{
int m = l.n;
int n = l.size*l.size*l.c;
int k = convolutional_out_height(l)*
convolutional_out_width(l);
gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu);
gradient_array_ongpu(l.output_gpu, m*k*l.batch, l.activation, l.delta_gpu);
backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, k);
backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h);
if(l.batch_normalize){
backward_batchnorm_layer_gpu(l, state);
@ -181,6 +175,10 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state
}
#else
int m = l.n;
int n = l.size*l.size*l.c;
int k = l.out_w*l.out_h;
int i;
for(i = 0; i < l.batch; ++i){
float * a = l.delta_gpu;

110
src/convolutional_layer.c
View File

@ -14,6 +14,7 @@
#ifndef AI2
#define AI2 0
void forward_xnor_layer(layer l, network_state state);
#endif
void swap_binary(convolutional_layer *l)
@ -127,6 +128,47 @@ size_t get_workspace_size(layer l){
#endif
}
#ifdef GPU
#ifdef CUDNN
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->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
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;
@ -231,39 +273,7 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int
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);
cudnn_convolutional_setup(&l);
#endif
#endif
l.workspace_size = get_workspace_size(l);
@ -335,39 +345,7 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h)
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);
cudnn_convolutional_setup(l);
#endif
#endif
l->workspace_size = get_workspace_size(*l);

3
src/convolutional_layer.h
View File

@ -19,6 +19,9 @@ void pull_convolutional_layer(convolutional_layer layer);
void add_bias_gpu(float *output, float *biases, int batch, int n, int size);
void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size);
#ifdef CUDNN
void cudnn_convolutional_setup(layer *l);
#endif
#endif
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_normalization, int binary, int xnor);

7
src/detection_layer.c
View File

@ -133,6 +133,9 @@ void forward_detection_layer(const detection_layer l, network_state state)
best_index = 0;
}
}
if(1 && *(state.net.seen) < 100000){
best_index = rand()%l.n;
}
int box_index = index + locations*(l.classes + l.n) + (i*l.n + best_index) * l.coords;
int tbox_index = truth_index + 1 + l.classes;
@ -181,7 +184,6 @@ void forward_detection_layer(const detection_layer l, network_state state)
for (b = 0; b < l.batch; ++b) {
int index = b*l.inputs;
for (i = 0; i < locations; ++i) {
int truth_index = (b*locations + i)*(1+l.coords+l.classes);
for (j = 0; j < l.n; ++j) {
int p_index = index + locations*l.classes + i*l.n + j;
costs[b*locations*l.n + i*l.n + j] = l.delta[p_index]*l.delta[p_index];
@ -194,7 +196,6 @@ void forward_detection_layer(const detection_layer l, network_state state)
for (b = 0; b < l.batch; ++b) {
int index = b*l.inputs;
for (i = 0; i < locations; ++i) {
int truth_index = (b*locations + i)*(1+l.coords+l.classes);
for (j = 0; j < l.n; ++j) {
int p_index = index + locations*l.classes + i*l.n + j;
if (l.delta[p_index]*l.delta[p_index] < cutoff) l.delta[p_index] = 0;
@ -233,7 +234,7 @@ void forward_detection_layer_gpu(const detection_layer l, network_state state)
cuda_pull_array(state.truth, truth_cpu, num_truth);
}
cuda_pull_array(state.input, in_cpu, l.batch*l.inputs);
network_state cpu_state;
network_state cpu_state = state;
cpu_state.train = state.train;
cpu_state.truth = truth_cpu;
cpu_state.input = in_cpu;

21
src/go.c
View File

@ -217,7 +217,7 @@ void print_board(float *board, int swap, int *indexes)
}
fprintf(stream, "\n");
for(j = 0; j < 19; ++j){
fprintf(stream, "%2d ", (inverted) ? 19-j : j+1);
fprintf(stream, "%2d", (inverted) ? 19-j : j+1);
for(i = 0; i < 19; ++i){
int index = j*19 + i;
if(indexes){
@ -225,17 +225,26 @@ void print_board(float *board, int swap, int *indexes)
for(n = 0; n < nind; ++n){
if(index == indexes[n]){
found = 1;
/*
if(n == 0) fprintf(stream, "\uff11");
else if(n == 1) fprintf(stream, "\uff12");
else if(n == 2) fprintf(stream, "\uff13");
else if(n == 3) fprintf(stream, "\uff14");
else if(n == 4) fprintf(stream, "\uff15");
*/
if(n == 0) fprintf(stream, " 1");
else if(n == 1) fprintf(stream, " 2");
else if(n == 2) fprintf(stream, " 3");
else if(n == 3) fprintf(stream, " 4");
else if(n == 4) fprintf(stream, " 5");
}
}
if(found) continue;
}
if(board[index]*-swap > 0) fprintf(stream, "\u25C9 ");
else if(board[index]*-swap < 0) fprintf(stream, "\u25EF ");
//if(board[index]*-swap > 0) fprintf(stream, "\u25C9 ");
//else if(board[index]*-swap < 0) fprintf(stream, "\u25EF ");
if(board[index]*-swap > 0) fprintf(stream, " O");
else if(board[index]*-swap < 0) fprintf(stream, " X");
else fprintf(stream, " ");
}
fprintf(stream, "\n");
@ -640,8 +649,10 @@ void test_go(char *cfg, char *weights, int multi)
col = index % 19;
printf("%d: %c %d, %.2f%%\n", i+1, col + 'A' + 1*(col > 7 && noi), (inverted)?19 - row : row+1, move[index]*100);
}
if(color == 1) printf("\u25EF Enter move: ");
else printf("\u25C9 Enter move: ");
//if(color == 1) printf("\u25EF Enter move: ");
//else printf("\u25C9 Enter move: ");
if(color == 1) printf("X Enter move: ");
else printf("O Enter move: ");
char c;
char *line = fgetl(stdin);

5
src/network.c
View File

@ -392,6 +392,11 @@ void set_batch_network(network *net, int b)
int i;
for(i = 0; i < net->n; ++i){
net->layers[i].batch = b;
#ifdef CUDNN
if(net->layers[i].type == CONVOLUTIONAL){
cudnn_convolutional_setup(net->layers + i);
}
#endif
}
}

7
src/rnn.c
View File

@ -280,7 +280,7 @@ void test_char_rnn(char *cfgfile, char *weightfile, int num, char *seed, float t
printf("\n");
}
void test_tactic_rnn(char *cfgfile, char *weightfile, int num, char *seed, float temp, int rseed, char *token_file)
void test_tactic_rnn(char *cfgfile, char *weightfile, int num, float temp, int rseed, char *token_file)
{
char **tokens = 0;
if(token_file){
@ -301,9 +301,8 @@ void test_tactic_rnn(char *cfgfile, char *weightfile, int num, char *seed, float
int i, j;
for(i = 0; i < net.n; ++i) net.layers[i].temperature = temp;
int c = 0;
int len = strlen(seed);
float *input = calloc(inputs, sizeof(float));
float *out;
float *out = 0;
while((c = getc(stdin)) != EOF){
input[c] = 1;
@ -490,5 +489,5 @@ void run_char_rnn(int argc, char **argv)
else if(0==strcmp(argv[2], "validtactic")) valid_tactic_rnn(cfg, weights, seed);
else if(0==strcmp(argv[2], "vec")) vec_char_rnn(cfg, weights, seed);
else if(0==strcmp(argv[2], "generate")) test_char_rnn(cfg, weights, len, seed, temp, rseed, tokens);
else if(0==strcmp(argv[2], "generatetactic")) test_tactic_rnn(cfg, weights, len, seed, temp, rseed, tokens);
else if(0==strcmp(argv[2], "generatetactic")) test_tactic_rnn(cfg, weights, len, temp, rseed, tokens);
}