From fc9b867dd9c9a6d38d7fe478217060e11b9e7e1b Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Wed, 16 Nov 2016 08:15:46 +0000
Subject: [PATCH] :fire: :fire: :dragonite:
---
src/cost_layer.c | 2 +-
src/convolutional_layer.c | 2 +-
src/connected_layer.c | 2 +-
src/parser.c | 3 ++-
src/detector.c | 6 +++++-
src/softmax_layer.c | 2 +-
src/avgpool_layer.c | 2 +-
src/dropout_layer.c | 2 +-
src/maxpool_layer.c | 2 +-
9 files changed, 14 insertions(+), 9 deletions(-)
diff --git a/src/avgpool_layer.c b/src/avgpool_layer.c
index c6db477..b6932fe 100644
--- a/src/avgpool_layer.c
+++ b/src/avgpool_layer.c
@@ -4,7 +4,7 @@
avgpool_layer make_avgpool_layer(int batch, int w, int h, int c)
{
- fprintf(stderr, "Avgpool Layer: %d x %d x %d image\n", w,h,c);
+ fprintf(stderr, "avg %4d x%4d x%4d -> %4d\n", w, h, c, c);
avgpool_layer l = {0};
l.type = AVGPOOL;
l.batch = batch;
diff --git a/src/connected_layer.c b/src/connected_layer.c
index 2694229..b678ed0 100644
--- a/src/connected_layer.c
+++ b/src/connected_layer.c
@@ -100,7 +100,7 @@
}
#endif
l.activation = activation;
- fprintf(stderr, "Connected Layer: %d inputs, %d outputs\n", inputs, outputs);
+ fprintf(stderr, "connected %4d -> %4d\n", inputs, outputs);
return l;
}
diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c
index 86285e0..3864c1b 100644
--- a/src/convolutional_layer.c
+++ b/src/convolutional_layer.c
@@ -300,7 +300,7 @@
l.workspace_size = get_workspace_size(l);
l.activation = activation;
- fprintf(stderr, "Convolutional Layer: %d x %d x %d image, %d filters -> %d x %d x %d image\n", h,w,c,n, out_h, out_w, n);
+ fprintf(stderr, "conv %5d %2d x%2d /%2d %4d x%4d x%4d -> %4d x%4d x%4d\n", n, size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c);
return l;
}
diff --git a/src/cost_layer.c b/src/cost_layer.c
index f266c6a..39d2398 100644
--- a/src/cost_layer.c
+++ b/src/cost_layer.c
@@ -31,7 +31,7 @@
cost_layer make_cost_layer(int batch, int inputs, COST_TYPE cost_type, float scale)
{
- fprintf(stderr, "Cost Layer: %d inputs\n", inputs);
+ fprintf(stderr, "cost %4d\n", inputs);
cost_layer l = {0};
l.type = COST;
diff --git a/src/detector.c b/src/detector.c
index 3853ebb..a513816 100644
--- a/src/detector.c
+++ b/src/detector.c
@@ -136,15 +136,19 @@
i = get_current_batch(net);
printf("%d: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs);
- if(i%100==0 || (i < 1000 && i%100 == 0)){
+ if(i%1000==0 || (i < 1000 && i%100 == 0)){
+#ifdef GPU
if(ngpus != 1) sync_nets(nets, ngpus, 0);
+#endif
char buff[256];
sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
save_weights(net, buff);
}
free_data(train);
}
+#ifdef GPU
if(ngpus != 1) sync_nets(nets, ngpus, 0);
+#endif
char buff[256];
sprintf(buff, "%s/%s_final.weights", backup_directory, base);
save_weights(net, buff);
diff --git a/src/dropout_layer.c b/src/dropout_layer.c
index 82be64b..b1381e6 100644
--- a/src/dropout_layer.c
+++ b/src/dropout_layer.c
@@ -6,7 +6,6 @@
dropout_layer make_dropout_layer(int batch, int inputs, float probability)
{
- fprintf(stderr, "Dropout Layer: %d inputs, %f probability\n", inputs, probability);
dropout_layer l = {0};
l.type = DROPOUT;
l.probability = probability;
@@ -22,6 +21,7 @@
l.backward_gpu = backward_dropout_layer_gpu;
l.rand_gpu = cuda_make_array(l.rand, inputs*batch);
#endif
+ fprintf(stderr, "dropout p = %.2f %4d -> %4d\n", probability, inputs, inputs);
return l;
}
diff --git a/src/maxpool_layer.c b/src/maxpool_layer.c
index 49cfeaf..d1fbacb 100644
--- a/src/maxpool_layer.c
+++ b/src/maxpool_layer.c
@@ -20,7 +20,6 @@
maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int stride, int padding)
{
- fprintf(stderr, "Maxpool Layer: %d x %d x %d image, %d size, %d stride\n", h,w,c,size,stride);
maxpool_layer l = {0};
l.type = MAXPOOL;
l.batch = batch;
@@ -48,6 +47,7 @@
l.output_gpu = cuda_make_array(l.output, output_size);
l.delta_gpu = cuda_make_array(l.delta, output_size);
#endif
+ fprintf(stderr, "max %d x %d / %d %4d x%4d x%4d -> %4d x%4d x%4d\n", size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c);
return l;
}
diff --git a/src/parser.c b/src/parser.c
index db4cf36..cde06b4 100644
--- a/src/parser.c
+++ b/src/parser.c
@@ -610,9 +610,10 @@
n = n->next;
int count = 0;
free_section(s);
+ fprintf(stderr, "layer filters size input output\n");
while(n){
params.index = count;
- fprintf(stderr, "%d: ", count);
+ fprintf(stderr, "%5d ", count);
s = (section *)n->val;
options = s->options;
layer l = {0};
diff --git a/src/softmax_layer.c b/src/softmax_layer.c
index 0aa9047..5d15314 100644
--- a/src/softmax_layer.c
+++ b/src/softmax_layer.c
@@ -10,7 +10,7 @@
softmax_layer make_softmax_layer(int batch, int inputs, int groups)
{
assert(inputs%groups == 0);
- fprintf(stderr, "Softmax Layer: %d inputs\n", inputs);
+ fprintf(stderr, "softmax %4d\n", inputs);
softmax_layer l = {0};
l.type = SOFTMAX;
l.batch = batch;
--
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