mirror/darknet
1
0
Fork 0
mirror of https://github.com/pjreddie/darknet.git synced 2023-08-10 21:13:14 +03:00
Code Issues Projects Releases Wiki Activity

resnet that works

Browse Source
This commit is contained in:
Joseph Redmon
2017-10-02 15:17:48 -07:00
parent 1b001a7f58
commit 62b781af4d
18 changed files with 294 additions and 1311 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
Makefile
View File

@ -4,11 +4,11 @@ OPENCV=0
OPENMP=0 OPENMP=0
DEBUG=0 DEBUG=0
ARCH= -gencode arch=compute_20,code=[sm_20,sm_21] \ ARCH= -gencode arch=compute_30,code=sm_30 \
-gencode arch=compute_30,code=sm_30 \
-gencode arch=compute_35,code=sm_35 \ -gencode arch=compute_35,code=sm_35 \
-gencode arch=compute_50,code=[sm_50,compute_50] \ -gencode arch=compute_50,code=[sm_50,compute_50] \
-gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_52,code=[sm_52,compute_52]
# -gencode arch=compute_20,code=[sm_20,sm_21] \ This one is deprecated?
# This is what I use, uncomment if you know your arch and want to specify # This is what I use, uncomment if you know your arch and want to specify
# ARCH= -gencode arch=compute_52,code=compute_52 # ARCH= -gencode arch=compute_52,code=compute_52

366
cfg/msr_34.cfg
View File

@ -1,366 +0,0 @@
[net]
batch=128
subdivisions=1
height=256
width=256
channels=3
momentum=0.9
decay=0.0005
learning_rate=0.1
policy=poly
power=4
max_batches=500000
[crop]
crop_height=224
crop_width=224
flip=1
saturation=1
exposure=1
angle=0
[convolutional]
batch_normalize=1
filters=64
size=7
stride=2
pad=1
activation=leaky
[maxpool]
size=3
stride=2
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from = -3
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from = -3
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from = -3
[convolutional]
batch_normalize=1
filters=128
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from = -3
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from = -3
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from = -3
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from = -3
[convolutional]
batch_normalize=1
filters=256
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from = -3
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from = -3
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from = -3
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from = -3
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from = -3
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from = -3
[convolutional]
batch_normalize=1
filters=512
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from = -3
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from = -3
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from = -3
[avgpool]
[connected]
output=1000
activation=leaky
[softmax]
groups=1
[cost]
type=sse

558
cfg/msr_50.cfg
View File

@ -1,558 +0,0 @@
[net]
batch=128
subdivisions=8
height=256
width=256
channels=3
momentum=0.9
decay=0.0001
learning_rate=0.05
policy=poly
power=4
max_batches=500000
[crop]
crop_height=224
crop_width=224
flip=1
saturation=1
exposure=1
angle=0
##### Conv 1 #####
[convolutional]
batch_normalize=1
filters=64
size=7
stride=2
pad=1
activation=leaky
[maxpool]
size=3
stride=2
##### Conv 2_x #####
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=linear
[route]
layers=-4
[convolutional]
batch_normalize=1
size=1
stride=1
pad=1
activation=linear
filters=256
[shortcut]
from = -3
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=linear
[shortcut]
from = -4
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=linear
[shortcut]
from = -4
activation=leaky
##### Conv 3_x #####
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=linear
[route]
layers=-4
[convolutional]
batch_normalize=1
size=1
stride=2
pad=1
activation=linear
filters=512
[shortcut]
from = -3
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=linear
[shortcut]
from = -4
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=linear
[shortcut]
from = -4
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=linear
[shortcut]
from = -4
activation=leaky
##### Conv 4_x #####
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[route]
layers=-4
[convolutional]
batch_normalize=1
size=1
stride=2
pad=1
activation=linear
filters=1024
[shortcut]
from = -3
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from = -4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from = -4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from = -4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from = -4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from = -4
activation=leaky
##### Conv 5_x #####
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=2048
size=1
stride=1
pad=1
activation=linear
[route]
layers=-4
[convolutional]
batch_normalize=1
size=1
stride=2
pad=1
activation=linear
filters=2048
[shortcut]
from = -3
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=2048
size=1
stride=1
pad=1
activation=linear
[shortcut]
from = -4
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=2048
size=1
stride=1
pad=1
activation=linear
[shortcut]
from = -4
activation=leaky
[avgpool]
[connected]
output=1000
activation=leaky
[softmax]
groups=1
[cost]
type=sse

353
cfg/msr_152.cfg → cfg/resnet152.cfg
View File

@ -1,26 +1,30 @@
[net] [net]
batch=128 # Training
subdivisions=8 # batch=128
# subdivisions=8
# Testing
batch=1
subdivisions=1
height=256 height=256
width=256 width=256
max_crop=448
channels=3 channels=3
momentum=0.9 momentum=0.9
decay=0.0001 decay=0.0005
burn_in=1000
learning_rate=0.1 learning_rate=0.1
policy=poly policy=poly
power=4 power=4
max_batches=500000 max_batches=1600000
[crop] angle=7
crop_height=224 hue=.1
crop_width=224 saturation=.75
flip=1 exposure=.75
saturation=1 aspect=.75
exposure=1
angle=0
##### Conv 1 #####
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
@ -31,13 +35,9 @@ pad=1
activation=leaky activation=leaky
[maxpool] [maxpool]
size=3 size=2
stride=2 stride=2
##### Conv 2_x #####
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=64 filters=64
@ -62,19 +62,8 @@ stride=1
pad=1 pad=1
activation=linear activation=linear
[route]
layers=-4
[convolutional]
batch_normalize=1
size=1
stride=1
pad=1
activation=linear
filters=256
[shortcut] [shortcut]
from = -3 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
@ -102,8 +91,7 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
@ -131,13 +119,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
##### Conv 3_x #####
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=128 filters=128
@ -162,23 +146,10 @@ stride=1
pad=1 pad=1
activation=linear activation=linear
[route]
layers=-4
[convolutional]
batch_normalize=1
size=1
stride=2
pad=1
activation=linear
filters=512
[shortcut] [shortcut]
from = -3 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=128 filters=128
@ -204,11 +175,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=128 filters=128
@ -234,11 +203,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=128 filters=128
@ -264,11 +231,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=128 filters=128
@ -294,11 +259,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=128 filters=128
@ -324,11 +287,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=128 filters=128
@ -354,11 +315,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=128 filters=128
@ -384,14 +343,11 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
# Conv 4
##### Conv 4_x #####
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=256 filters=256
@ -416,23 +372,10 @@ stride=1
pad=1 pad=1
activation=linear activation=linear
[route]
layers=-4
[convolutional]
batch_normalize=1
size=1
stride=2
pad=1
activation=linear
filters=1024
[shortcut] [shortcut]
from = -3 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=256 filters=256
@ -458,11 +401,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=256 filters=256
@ -488,11 +429,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=256 filters=256
@ -518,11 +457,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=256 filters=256
@ -548,11 +485,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=256 filters=256
@ -578,11 +513,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=256 filters=256
@ -608,11 +541,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=256 filters=256
@ -638,11 +569,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=256 filters=256
@ -668,11 +597,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=256 filters=256
@ -698,11 +625,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=256 filters=256
@ -728,11 +653,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=256 filters=256
@ -758,11 +681,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=256 filters=256
@ -788,11 +709,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=256 filters=256
@ -818,11 +737,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=256 filters=256
@ -848,11 +765,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=256 filters=256
@ -878,11 +793,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=256 filters=256
@ -908,11 +821,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=256 filters=256
@ -938,11 +849,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=256 filters=256
@ -968,11 +877,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=256 filters=256
@ -998,11 +905,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=256 filters=256
@ -1028,11 +933,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=256 filters=256
@ -1058,11 +961,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=256 filters=256
@ -1088,11 +989,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=256 filters=256
@ -1118,11 +1017,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=256 filters=256
@ -1148,11 +1045,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=256 filters=256
@ -1178,11 +1073,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=256 filters=256
@ -1208,11 +1101,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=256 filters=256
@ -1238,11 +1129,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=256 filters=256
@ -1268,11 +1157,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=256 filters=256
@ -1298,11 +1185,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=256 filters=256
@ -1328,11 +1213,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=256 filters=256
@ -1358,11 +1241,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=256 filters=256
@ -1388,11 +1269,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=256 filters=256
@ -1418,11 +1297,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=256 filters=256
@ -1448,11 +1325,9 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=256 filters=256
@ -1478,13 +1353,10 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
#Conv 5
##### Conv 5_x #####
[convolutional] [convolutional]
batch_normalize=1 batch_normalize=1
filters=512 filters=512
@ -1509,50 +1381,8 @@ stride=1
pad=1 pad=1
activation=linear activation=linear
[route]
layers=-4
[convolutional]
batch_normalize=1
size=1
stride=2
pad=1
activation=linear
filters=2048
[shortcut] [shortcut]
from = -3 from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=2048
size=1
stride=1
pad=1
activation=linear
[shortcut]
from = -4
activation=leaky activation=leaky
[convolutional] [convolutional]
@ -1580,16 +1410,51 @@ pad=1
activation=linear activation=linear
[shortcut] [shortcut]
from = -4 from=-4
activation=leaky activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=2048
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
filters=1000
size=1
stride=1
pad=1
activation=linear
[avgpool] [avgpool]
[connected]
output=1000
activation=leaky
[softmax] [softmax]
groups=1 groups=1

3
examples/classifier.c
View File

@ -58,7 +58,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus,
load_args args = {0}; load_args args = {0};
args.w = net.w; args.w = net.w;
args.h = net.h; args.h = net.h;
args.threads = 64; args.threads = 32;
args.hierarchy = net.hierarchy; args.hierarchy = net.hierarchy;
args.min = net.min_crop; args.min = net.min_crop;
@ -123,6 +123,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus,
char buff[256]; char buff[256];
sprintf(buff, "%s/%s.weights", backup_directory, base); sprintf(buff, "%s/%s.weights", backup_directory, base);
save_weights(net, buff); save_weights(net, buff);
pthread_join(load_thread, 0);
free_network(net); free_network(net);
free_ptrs((void**)labels, classes); free_ptrs((void**)labels, classes);

49
examples/darknet.c
View File

@ -83,27 +83,8 @@ void average(int argc, char *argv[])
save_weights(sum, outfile); save_weights(sum, outfile);
} }
void speed(char *cfgfile, int tics) long numops(network net)
{ {
if (tics == 0) tics = 1000;
network net = parse_network_cfg(cfgfile);
set_batch_network(&net, 1);
int i;
double time=what_time_is_it_now();
image im = make_image(net.w, net.h, net.c*net.batch);
for(i = 0; i < tics; ++i){
network_predict(net, im.data);
}
double t = what_time_is_it_now() - time;
printf("\n%d evals, %f Seconds\n", tics, t);
printf("Speed: %f sec/eval\n", t/tics);
printf("Speed: %f Hz\n", tics/t);
}
void operations(char *cfgfile)
{
gpu_index = -1;
network net = parse_network_cfg(cfgfile);
int i; int i;
long ops = 0; long ops = 0;
for(i = 0; i < net.n; ++i){ for(i = 0; i < net.n; ++i){
@ -134,6 +115,34 @@ void operations(char *cfgfile)
ops += 2l * l.wo->inputs * l.wo->outputs; ops += 2l * l.wo->inputs * l.wo->outputs;
} }
} }
return ops;
}
void speed(char *cfgfile, int tics)
{
if (tics == 0) tics = 1000;
network net = parse_network_cfg(cfgfile);
set_batch_network(&net, 1);
int i;
double time=what_time_is_it_now();
image im = make_image(net.w, net.h, net.c*net.batch);
for(i = 0; i < tics; ++i){
network_predict(net, im.data);
}
double t = what_time_is_it_now() - time;
long ops = numops(net);
printf("\n%d evals, %f Seconds\n", tics, t);
printf("Floating Point Operations: %.2f Bn\n", (float)ops/1000000000.);
printf("FLOPS: %.2f Bn\n", (float)ops/1000000000.*tics/t);
printf("Speed: %f sec/eval\n", t/tics);
printf("Speed: %f Hz\n", tics/t);
}
void operations(char *cfgfile)
{
gpu_index = -1;
network net = parse_network_cfg(cfgfile);
long ops = numops(net);
printf("Floating Point Operations: %ld\n", ops); printf("Floating Point Operations: %ld\n", ops);
printf("Floating Point Operations: %.2f Bn\n", (float)ops/1000000000.); printf("Floating Point Operations: %.2f Bn\n", (float)ops/1000000000.);
} }

20
examples/detector.c
View File

@ -52,10 +52,10 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
args.d = &buffer; args.d = &buffer;
args.type = DETECTION_DATA; args.type = DETECTION_DATA;
//args.type = INSTANCE_DATA; //args.type = INSTANCE_DATA;
args.threads = 8; args.threads = 64;
pthread_t load_thread = load_data(args); pthread_t load_thread = load_data(args);
clock_t time; double time;
int count = 0; int count = 0;
//while(i*imgs < N*120){ //while(i*imgs < N*120){
while(get_current_batch(net) < net.max_batches){ while(get_current_batch(net) < net.max_batches){
@ -78,7 +78,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
} }
net = nets[0]; net = nets[0];
} }
time=clock(); time=what_time_is_it_now();
pthread_join(load_thread, 0); pthread_join(load_thread, 0);
train = buffer; train = buffer;
load_thread = load_data(args); load_thread = load_data(args);
@ -107,9 +107,9 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
} }
*/ */
printf("Loaded: %lf seconds\n", sec(clock()-time)); printf("Loaded: %lf seconds\n", what_time_is_it_now()-time);
time=clock(); time=what_time_is_it_now();
float loss = 0; float loss = 0;
#ifdef GPU #ifdef GPU
if(ngpus == 1){ if(ngpus == 1){
@ -124,7 +124,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
avg_loss = avg_loss*.9 + loss*.1; avg_loss = avg_loss*.9 + loss*.1;
i = get_current_batch(net); i = get_current_batch(net);
printf("%ld: %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); printf("%ld: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), what_time_is_it_now()-time, i*imgs);
if(i%100==0){ if(i%100==0){
#ifdef GPU #ifdef GPU
if(ngpus != 1) sync_nets(nets, ngpus, 0); if(ngpus != 1) sync_nets(nets, ngpus, 0);
@ -313,7 +313,7 @@ void validate_detector_flip(char *datacfg, char *cfgfile, char *weightfile, char
args.resized = &buf_resized[t]; args.resized = &buf_resized[t];
thr[t] = load_data_in_thread(args); thr[t] = load_data_in_thread(args);
} }
time_t start = time(0); double start = what_time_is_it_now();
for(i = nthreads; i < m+nthreads; i += nthreads){ for(i = nthreads; i < m+nthreads; i += nthreads){
fprintf(stderr, "%d\n", i); fprintf(stderr, "%d\n", i);
for(t = 0; t < nthreads && i+t-nthreads < m; ++t){ for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
@ -359,7 +359,7 @@ void validate_detector_flip(char *datacfg, char *cfgfile, char *weightfile, char
fprintf(fp, "\n]\n"); fprintf(fp, "\n]\n");
fclose(fp); fclose(fp);
} }
fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start)); fprintf(stderr, "Total Detection Time: %f Seconds\n", what_time_is_it_now() - start);
} }
@ -447,7 +447,7 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out
args.resized = &buf_resized[t]; args.resized = &buf_resized[t];
thr[t] = load_data_in_thread(args); thr[t] = load_data_in_thread(args);
} }
time_t start = time(0); double start = what_time_is_it_now();
for(i = nthreads; i < m+nthreads; i += nthreads){ for(i = nthreads; i < m+nthreads; i += nthreads){
fprintf(stderr, "%d\n", i); fprintf(stderr, "%d\n", i);
for(t = 0; t < nthreads && i+t-nthreads < m; ++t){ for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
@ -490,7 +490,7 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out
fprintf(fp, "\n]\n"); fprintf(fp, "\n]\n");
fclose(fp); fclose(fp);
} }
fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start)); fprintf(stderr, "Total Detection Time: %f Seconds\n", what_time_is_it_now() - start);
} }
void validate_detector_recall(char *cfgfile, char *weightfile) void validate_detector_recall(char *cfgfile, char *weightfile)

30
examples/rnn.c
View File

@ -52,6 +52,7 @@ char **read_tokens(char *filename, size_t *read)
return d; return d;
} }
float_pair get_rnn_token_data(int *tokens, size_t *offsets, int characters, size_t len, int batch, int steps) float_pair get_rnn_token_data(int *tokens, size_t *offsets, int characters, size_t len, int batch, int steps)
{ {
float *x = calloc(batch * steps * characters, sizeof(float)); float *x = calloc(batch * steps * characters, sizeof(float));
@ -78,6 +79,35 @@ float_pair get_rnn_token_data(int *tokens, size_t *offsets, int characters, size
return p; return p;
} }
float_pair get_seq2seq_data(char **source, char **dest, int n, int characters, size_t len, int batch, int steps)
{
int i,j;
float *x = calloc(batch * steps * characters, sizeof(float));
float *y = calloc(batch * steps * characters, sizeof(float));
for(i = 0; i < batch; ++i){
int index = rand()%n;
for(j = 0; j < steps; ++j){
unsigned char curr = source[index][j];
unsigned char next = dest[index][j];
x[(j*batch + i)*characters + curr] = 1;
y[(j*batch + i)*characters + next] = 1;
if(curr > 255 || curr <= 0 || next > 255 || next <= 0){
/*text[(index+j+2)%len] = 0;
printf("%ld %d %d %d %d\n", index, j, len, (int)text[index+j], (int)text[index+j+1]);
printf("%s", text+index);
*/
error("Bad char");
}
}
}
float_pair p;
p.x = x;
p.y = y;
return p;
}
float_pair get_rnn_data(unsigned char *text, size_t *offsets, int characters, size_t len, int batch, int steps) float_pair get_rnn_data(unsigned char *text, size_t *offsets, int characters, size_t len, int batch, int steps)
{ {
float *x = calloc(batch * steps * characters, sizeof(float)); float *x = calloc(batch * steps * characters, sizeof(float));

2
examples/segmenter.c
View File

@ -211,7 +211,7 @@ void demo_segmenter(char *datacfg, char *cfgfile, char *weightfile, int cam_inde
image in = get_image_from_stream(cap); image in = get_image_from_stream(cap);
image in_s = letterbox_image(in, net.w, net.h); image in_s = letterbox_image(in, net.w, net.h);
float *predictions = network_predict(net, in_s.data); network_predict(net, in_s.data);
printf("\033[2J"); printf("\033[2J");
printf("\033[1;1H"); printf("\033[1;1H");

42
src/activation_kernels.cu
View File

@ -10,8 +10,8 @@ extern "C" {
__device__ float lhtan_activate_kernel(float x) __device__ float lhtan_activate_kernel(float x)
{ {
if(x < 0) return .001*x; if(x < 0) return .001f*x;
if(x > 1) return .001*(x-1) + 1; if(x > 1) return .001f*(x-1.f) + 1.f;
return x; return x;
} }
__device__ float lhtan_gradient_kernel(float x) __device__ float lhtan_gradient_kernel(float x)
@ -27,25 +27,25 @@ __device__ float hardtan_activate_kernel(float x)
return x; return x;
} }
__device__ float linear_activate_kernel(float x){return x;} __device__ float linear_activate_kernel(float x){return x;}
__device__ float logistic_activate_kernel(float x){return 1./(1. + exp(-x));} __device__ float logistic_activate_kernel(float x){return 1.f/(1.f + expf(-x));}
__device__ float loggy_activate_kernel(float x){return 2./(1. + exp(-x)) - 1;} __device__ float loggy_activate_kernel(float x){return 2.f/(1.f + expf(-x)) - 1;}
__device__ float relu_activate_kernel(float x){return x*(x>0);} __device__ float relu_activate_kernel(float x){return x*(x>0);}
__device__ float elu_activate_kernel(float x){return (x >= 0)*x + (x < 0)*(exp(x)-1);} __device__ float elu_activate_kernel(float x){return (x >= 0)*x + (x < 0)*(expf(x)-1);}
__device__ float relie_activate_kernel(float x){return (x>0) ? x : .01*x;} __device__ float relie_activate_kernel(float x){return (x>0) ? x : .01f*x;}
__device__ float ramp_activate_kernel(float x){return x*(x>0)+.1*x;} __device__ float ramp_activate_kernel(float x){return x*(x>0)+.1f*x;}
__device__ float leaky_activate_kernel(float x){return (x>0) ? x : .1*x;} __device__ float leaky_activate_kernel(float x){return (x>0) ? x : .1f*x;}
__device__ float tanh_activate_kernel(float x){return (2/(1 + exp(-2*x)) - 1);} __device__ float tanh_activate_kernel(float x){return (2.f/(1 + expf(-2*x)) - 1);}
__device__ float plse_activate_kernel(float x) __device__ float plse_activate_kernel(float x)
{ {
if(x < -4) return .01 * (x + 4); if(x < -4) return .01f * (x + 4);
if(x > 4) return .01 * (x - 4) + 1; if(x > 4) return .01f * (x - 4) + 1;
return .125*x + .5; return .125f*x + .5f;
} }
__device__ float stair_activate_kernel(float x) __device__ float stair_activate_kernel(float x)
{ {
int n = floor(x); int n = floorf(x);
if (n%2 == 0) return floor(x/2.); if (n%2 == 0) return floorf(x/2);
else return (x - n) + floor(x/2.); else return (x - n) + floorf(x/2);
} }
@ -58,19 +58,19 @@ __device__ float linear_gradient_kernel(float x){return 1;}
__device__ float logistic_gradient_kernel(float x){return (1-x)*x;} __device__ float logistic_gradient_kernel(float x){return (1-x)*x;}
__device__ float loggy_gradient_kernel(float x) __device__ float loggy_gradient_kernel(float x)
{ {
float y = (x+1.)/2.; float y = (x+1)/2;
return 2*(1-y)*y; return 2*(1-y)*y;
} }
__device__ float relu_gradient_kernel(float x){return (x>0);} __device__ float relu_gradient_kernel(float x){return (x>0);}
__device__ float elu_gradient_kernel(float x){return (x >= 0) + (x < 0)*(x + 1);} __device__ float elu_gradient_kernel(float x){return (x >= 0) + (x < 0)*(x + 1);}
__device__ float relie_gradient_kernel(float x){return (x>0) ? 1 : .01;} __device__ float relie_gradient_kernel(float x){return (x>0) ? 1 : .01f;}
__device__ float ramp_gradient_kernel(float x){return (x>0)+.1;} __device__ float ramp_gradient_kernel(float x){return (x>0)+.1f;}
__device__ float leaky_gradient_kernel(float x){return (x>0) ? 1 : .1;} __device__ float leaky_gradient_kernel(float x){return (x>0) ? 1 : .1f;}
__device__ float tanh_gradient_kernel(float x){return 1-x*x;} __device__ float tanh_gradient_kernel(float x){return 1-x*x;}
__device__ float plse_gradient_kernel(float x){return (x < 0 || x > 1) ? .01 : .125;} __device__ float plse_gradient_kernel(float x){return (x < 0 || x > 1) ? .01f : .125f;}
__device__ float stair_gradient_kernel(float x) __device__ float stair_gradient_kernel(float x)
{ {
if (floor(x) == x) return 0; if (floorf(x) == x) return 0;
return 1; return 1;
} }

26
src/blas_kernels.cu
View File

@ -165,7 +165,7 @@ __global__ void adam_kernel(int N, float *x, float *m, float *v, float B1, float
int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
if (index >= N) return; if (index >= N) return;
x[index] = x[index] + (rate * sqrt(1.-pow(B2, t)) / (1.-pow(B1, t)) * m[index] / (sqrt(v[index]) + eps)); x[index] = x[index] + (rate * sqrtf(1.f-powf(B2, t)) / (1.f-powf(B1, t)) * m[index] / (sqrtf(v[index]) + eps));
} }
extern "C" void adam_gpu(int n, float *x, float *m, float *v, float B1, float B2, float rate, float eps, int t) extern "C" void adam_gpu(int n, float *x, float *m, float *v, float B1, float B2, float rate, float eps, int t)
@ -194,7 +194,7 @@ __global__ void normalize_kernel(int N, float *x, float *mean, float *variance,
if (index >= N) return; if (index >= N) return;
int f = (index/spatial)%filters; int f = (index/spatial)%filters;
x[index] = (x[index] - mean[f])/(sqrt(variance[f] + .00001f)); x[index] = (x[index] - mean[f])/(sqrtf(variance[f] + .00001f));
} }
__global__ void normalize_delta_kernel(int N, float *x, float *mean, float *variance, float *mean_delta, float *variance_delta, int batch, int filters, int spatial, float *delta) __global__ void normalize_delta_kernel(int N, float *x, float *mean, float *variance, float *mean_delta, float *variance_delta, int batch, int filters, int spatial, float *delta)
@ -203,7 +203,7 @@ __global__ void normalize_delta_kernel(int N, float *x, float *mean, float *vari
if (index >= N) return; if (index >= N) return;
int f = (index/spatial)%filters; int f = (index/spatial)%filters;
delta[index] = delta[index] * 1./(sqrt(variance[f] + .00001f)) + variance_delta[f] * 2. * (x[index] - mean[f]) / (spatial * batch) + mean_delta[f]/(spatial*batch); delta[index] = delta[index] * 1.f/(sqrtf(variance[f] + .00001f)) + variance_delta[f] * 2.f * (x[index] - mean[f]) / (spatial * batch) + mean_delta[f]/(spatial*batch);
} }
extern "C" void normalize_delta_gpu(float *x, float *mean, float *variance, float *mean_delta, float *variance_delta, int batch, int filters, int spatial, float *delta) extern "C" void normalize_delta_gpu(float *x, float *mean, float *variance, float *mean_delta, float *variance_delta, int batch, int filters, int spatial, float *delta)
@ -225,7 +225,7 @@ __global__ void variance_delta_kernel(float *x, float *delta, float *mean, floa
variance_delta[i] += delta[index]*(x[index] - mean[i]); variance_delta[i] += delta[index]*(x[index] - mean[i]);
} }
} }
variance_delta[i] *= -.5 * pow(variance[i] + .00001f, (float)(-3./2.)); variance_delta[i] *= -.5f * powf(variance[i] + .00001f, (float)(-3.f/2.f));
} }
__global__ void accumulate_kernel(float *x, int n, int groups, float *sum) __global__ void accumulate_kernel(float *x, int n, int groups, float *sum)
@ -264,7 +264,7 @@ __global__ void fast_mean_delta_kernel(float *delta, float *variance, int batch,
for(i = 0; i < threads; ++i){ for(i = 0; i < threads; ++i){
mean_delta[filter] += local[i]; mean_delta[filter] += local[i];
} }
mean_delta[filter] *= (-1./sqrt(variance[filter] + .00001f)); mean_delta[filter] *= (-1.f/sqrtf(variance[filter] + .00001f));
} }
} }
@ -294,7 +294,7 @@ __global__ void fast_variance_delta_kernel(float *x, float *delta, float *mean,
for(i = 0; i < threads; ++i){ for(i = 0; i < threads; ++i){
variance_delta[filter] += local[i]; variance_delta[filter] += local[i];
} }
variance_delta[filter] *= -.5 * pow(variance[filter] + .00001f, (float)(-3./2.)); variance_delta[filter] *= -.5f * powf(variance[filter] + .00001f, (float)(-3.f/2.f));
} }
} }
@ -311,7 +311,7 @@ __global__ void mean_delta_kernel(float *delta, float *variance, int batch, int
mean_delta[i] += delta[index]; mean_delta[i] += delta[index];
} }
} }
mean_delta[i] *= (-1./sqrt(variance[i] + .00001f)); mean_delta[i] *= (-1.f/sqrtf(variance[i] + .00001f));
} }
extern "C" void mean_delta_gpu(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta) extern "C" void mean_delta_gpu(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta)
@ -334,7 +334,7 @@ extern "C" void fast_variance_delta_gpu(float *x, float *delta, float *mean, flo
__global__ void mean_kernel(float *x, int batch, int filters, int spatial, float *mean) __global__ void mean_kernel(float *x, int batch, int filters, int spatial, float *mean)
{ {
float scale = 1./(batch * spatial); float scale = 1.f/(batch * spatial);
int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
if (i >= filters) return; if (i >= filters) return;
int j,k; int j,k;
@ -350,7 +350,7 @@ __global__ void mean_kernel(float *x, int batch, int filters, int spatial, floa
__global__ void variance_kernel(float *x, float *mean, int batch, int filters, int spatial, float *variance) __global__ void variance_kernel(float *x, float *mean, int batch, int filters, int spatial, float *variance)
{ {
float scale = 1./(batch * spatial - 1); float scale = 1.f/(batch * spatial - 1);
int j,k; int j,k;
int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
if (i >= filters) return; if (i >= filters) return;
@ -358,7 +358,7 @@ __global__ void variance_kernel(float *x, float *mean, int batch, int filters, i
for(j = 0; j < batch; ++j){ for(j = 0; j < batch; ++j){
for(k = 0; k < spatial; ++k){ for(k = 0; k < spatial; ++k){
int index = j*filters*spatial + i*spatial + k; int index = j*filters*spatial + i*spatial + k;
variance[i] += pow((x[index] - mean[i]), 2); variance[i] += powf((x[index] - mean[i]), 2);
} }
} }
variance[i] *= scale; variance[i] *= scale;
@ -516,7 +516,7 @@ __global__ void fast_variance_kernel(float *x, float *mean, int batch, int filt
for(i = 0; i < spatial; i += threads){ for(i = 0; i < spatial; i += threads){
int index = j*spatial*filters + filter*spatial + i + id; int index = j*spatial*filters + filter*spatial + i + id;
local[id] += (i+id < spatial) ? pow((x[index] - mean[filter]), 2) : 0; local[id] += (i+id < spatial) ? powf((x[index] - mean[filter]), 2) : 0;
} }
} }
@ -716,7 +716,7 @@ __global__ void smooth_l1_kernel(int n, float *pred, float *truth, float *delta,
int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
if(i < n){ if(i < n){
float diff = truth[i] - pred[i]; float diff = truth[i] - pred[i];
float abs_val = abs(diff); float abs_val = fabsf(diff);
if(abs_val < 1) { if(abs_val < 1) {
error[i] = diff * diff; error[i] = diff * diff;
delta[i] = diff; delta[i] = diff;
@ -864,7 +864,7 @@ __device__ void softmax_device(float *input, int n, float temp, int stride, floa
largest = (val>largest) ? val : largest; largest = (val>largest) ? val : largest;
} }
for(i = 0; i < n; ++i){ for(i = 0; i < n; ++i){
float e = exp(input[i*stride]/temp - largest/temp); float e = expf(input[i*stride]/temp - largest/temp);
sum += e; sum += e;
output[i*stride] = e; output[i*stride] = e;
} }

8
src/convolutional_kernels.cu
View File

@ -33,7 +33,7 @@ __global__ void binarize_input_kernel(float *input, int n, int size, float *bina
int i = 0; int i = 0;
float mean = 0; float mean = 0;
for(i = 0; i < n; ++i){ for(i = 0; i < n; ++i){
mean += abs(input[i*size + s]); mean += fabsf(input[i*size + s]);
} }
mean = mean / n; mean = mean / n;
for(i = 0; i < n; ++i){ for(i = 0; i < n; ++i){
@ -55,7 +55,7 @@ __global__ void binarize_weights_kernel(float *weights, int n, int size, float *
int i = 0; int i = 0;
float mean = 0; float mean = 0;
for(i = 0; i < size; ++i){ for(i = 0; i < size; ++i){
mean += abs(weights[f*size + i]); mean += fabsf(weights[f*size + i]);
} }
mean = mean / size; mean = mean / size;
for(i = 0; i < size; ++i){ for(i = 0; i < size; ++i){
@ -139,8 +139,8 @@ __global__ void smooth_kernel(float *x, int n, int w, int h, int c, int size, fl
id /= c; id /= c;
int b = id; int b = id;
int w_offset = -(size/2.); int w_offset = -(size/2.f);
int h_offset = -(size/2.); int h_offset = -(size/2.f);
int out_index = j + w*(i + h*(k + c*b)); int out_index = j + w*(i + h*(k + c*b));
int l, m; int l, m;

26
src/crop_layer_kernels.cu
View File

@ -113,9 +113,9 @@ __global__ void levels_image_kernel(float *image, float *rand, int batch, int w,
float r3 = rand[8*id + 3]; float r3 = rand[8*id + 3];
saturation = r0*(saturation - 1) + 1; saturation = r0*(saturation - 1) + 1;
saturation = (r1 > .5) ? 1./saturation : saturation; saturation = (r1 > .5f) ? 1.f/saturation : saturation;
exposure = r2*(exposure - 1) + 1; exposure = r2*(exposure - 1) + 1;
exposure = (r3 > .5) ? 1./exposure : exposure; exposure = (r3 > .5f) ? 1.f/exposure : exposure;
size_t offset = id * h * w * 3; size_t offset = id * h * w * 3;
image += offset; image += offset;
@ -131,9 +131,9 @@ __global__ void levels_image_kernel(float *image, float *rand, int batch, int w,
} else { } else {
shift = 0; shift = 0;
} }
image[x + w*(y + h*0)] = rgb.x*scale + translate + (rshift - .5)*shift; image[x + w*(y + h*0)] = rgb.x*scale + translate + (rshift - .5f)*shift;
image[x + w*(y + h*1)] = rgb.y*scale + translate + (gshift - .5)*shift; image[x + w*(y + h*1)] = rgb.y*scale + translate + (gshift - .5f)*shift;
image[x + w*(y + h*2)] = rgb.z*scale + translate + (bshift - .5)*shift; image[x + w*(y + h*2)] = rgb.z*scale + translate + (bshift - .5f)*shift;
} }
__global__ void forward_crop_layer_kernel(float *input, float *rand, int size, int c, int h, int w, int crop_height, int crop_width, int train, int flip, float angle, float *output) __global__ void forward_crop_layer_kernel(float *input, float *rand, int size, int c, int h, int w, int crop_height, int crop_width, int train, int flip, float angle, float *output)
@ -141,8 +141,8 @@ __global__ void forward_crop_layer_kernel(float *input, float *rand, int size, i
int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
if(id >= size) return; if(id >= size) return;
float cx = w/2.; float cx = w/2.f;
float cy = h/2.; float cy = h/2.f;
int count = id; int count = id;
int j = id % crop_width; int j = id % crop_width;
@ -160,11 +160,11 @@ __global__ void forward_crop_layer_kernel(float *input, float *rand, int size, i
float dw = (w - crop_width)*r4; float dw = (w - crop_width)*r4;
float dh = (h - crop_height)*r5; float dh = (h - crop_height)*r5;
flip = (flip && (r6 > .5)); flip = (flip && (r6 > .5f));
angle = 2*angle*r7 - angle; angle = 2*angle*r7 - angle;
if(!train){ if(!train){
dw = (w - crop_width)/2.; dw = (w - crop_width)/2.f;
dh = (h - crop_height)/2.; dh = (h - crop_height)/2.f;
flip = 0; flip = 0;
angle = 0; angle = 0;
} }
@ -174,8 +174,8 @@ __global__ void forward_crop_layer_kernel(float *input, float *rand, int size, i
float x = (flip) ? w - dw - j - 1 : j + dw; float x = (flip) ? w - dw - j - 1 : j + dw;
float y = i + dh; float y = i + dh;
float rx = cos(angle)*(x-cx) - sin(angle)*(y-cy) + cx; float rx = cosf(angle)*(x-cx) - sinf(angle)*(y-cy) + cx;
float ry = sin(angle)*(x-cx) + cos(angle)*(y-cy) + cy; float ry = sinf(angle)*(x-cx) + cosf(angle)*(y-cy) + cy;
output[count] = bilinear_interpolate_kernel(input, w, h, rx, ry, k); output[count] = bilinear_interpolate_kernel(input, w, h, rx, ry, k);
} }
@ -184,7 +184,7 @@ extern "C" void forward_crop_layer_gpu(crop_layer layer, network net)
{ {
cuda_random(layer.rand_gpu, layer.batch*8); cuda_random(layer.rand_gpu, layer.batch*8);
float radians = layer.angle*3.14159265/180.; float radians = layer.angle*3.14159265f/180.f;
float scale = 2; float scale = 2;
float translate = -1; float translate = -1;

9
src/data.c
View File

@ -137,14 +137,18 @@ matrix load_image_augment_paths(char **paths, int n, int min, int max, int size,
box_label *read_boxes(char *filename, int *n) box_label *read_boxes(char *filename, int *n)
{ {
box_label *boxes = calloc(1, sizeof(box_label));
FILE *file = fopen(filename, "r"); FILE *file = fopen(filename, "r");
if(!file) file_error(filename); if(!file) file_error(filename);
float x, y, h, w; float x, y, h, w;
int id; int id;
int count = 0; int count = 0;
int size = 64;
box_label *boxes = calloc(size, sizeof(box_label));
while(fscanf(file, "%d %f %f %f %f", &id, &x, &y, &w, &h) == 5){ while(fscanf(file, "%d %f %f %f %f", &id, &x, &y, &w, &h) == 5){
boxes = realloc(boxes, (count+1)*sizeof(box_label)); if(count == size) {
size = size * 2;
boxes = realloc(boxes, size*sizeof(box_label));
}
boxes[count].id = id; boxes[count].id = id;
boxes[count].x = x; boxes[count].x = x;
boxes[count].y = y; boxes[count].y = y;
@ -976,6 +980,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int boxes, in
place_image(orig, nw, nh, dx, dy, sized); place_image(orig, nw, nh, dx, dy, sized);
random_distort_image(sized, hue, saturation, exposure); random_distort_image(sized, hue, saturation, exposure);
int flip = rand()%2; int flip = rand()%2;
if(flip) flip_image(sized); if(flip) flip_image(sized);
d.X.vals[i] = sized.data; d.X.vals[i] = sized.data;

4
src/demo.c
View File

@ -20,10 +20,6 @@ static int demo_classes;
static float **probs; static float **probs;
static box *boxes; static box *boxes;
static network net; static network net;
static network net2;
static float **probs2;
static box *boxes2;
static float **predictions2;
static image buff [3]; static image buff [3];
static image buff_letter[3]; static image buff_letter[3];
static int buff_index = 0; static int buff_index = 0;

90
src/image.c
View File

@ -44,6 +44,51 @@ image mask_to_rgb(image mask)
return im; return im;
} }
static float get_pixel(image m, int x, int y, int c)
{
assert(x < m.w && y < m.h && c < m.c);
return m.data[c*m.h*m.w + y*m.w + x];
}
static float get_pixel_extend(image m, int x, int y, int c)
{
if(x < 0 || x >= m.w || y < 0 || y >= m.h) return 0;
/*
if(x < 0) x = 0;
if(x >= m.w) x = m.w-1;
if(y < 0) y = 0;
if(y >= m.h) y = m.h-1;
*/
if(c < 0 || c >= m.c) return 0;
return get_pixel(m, x, y, c);
}
static void set_pixel(image m, int x, int y, int c, float val)
{
if (x < 0 || y < 0 || c < 0 || x >= m.w || y >= m.h || c >= m.c) return;
assert(x < m.w && y < m.h && c < m.c);
m.data[c*m.h*m.w + y*m.w + x] = val;
}
static void add_pixel(image m, int x, int y, int c, float val)
{
assert(x < m.w && y < m.h && c < m.c);
m.data[c*m.h*m.w + y*m.w + x] += val;
}
static float bilinear_interpolate(image im, float x, float y, int c)
{
int ix = (int) floorf(x);
int iy = (int) floorf(y);
float dx = x - ix;
float dy = y - iy;
float val = (1-dy) * (1-dx) * get_pixel_extend(im, ix, iy, c) +
dy * (1-dx) * get_pixel_extend(im, ix, iy+1, c) +
(1-dy) * dx * get_pixel_extend(im, ix+1, iy, c) +
dy * dx * get_pixel_extend(im, ix+1, iy+1, c);
return val;
}
void composite_image(image source, image dest, int dx, int dy) void composite_image(image source, image dest, int dx, int dy)
{ {
int x,y,k; int x,y,k;
@ -1255,21 +1300,6 @@ void saturate_exposure_image(image im, float sat, float exposure)
constrain_image(im); constrain_image(im);
} }
float bilinear_interpolate(image im, float x, float y, int c)
{
int ix = (int) floorf(x);
int iy = (int) floorf(y);
float dx = x - ix;
float dy = y - iy;
float val = (1-dy) * (1-dx) * get_pixel_extend(im, ix, iy, c) +
dy * (1-dx) * get_pixel_extend(im, ix, iy+1, c) +
(1-dy) * dx * get_pixel_extend(im, ix+1, iy, c) +
dy * dx * get_pixel_extend(im, ix+1, iy+1, c);
return val;
}
image resize_image(image im, int w, int h) image resize_image(image im, int w, int h)
{ {
image resized = make_image(w, h, im.c); image resized = make_image(w, h, im.c);
@ -1419,36 +1449,6 @@ image get_image_layer(image m, int l)
} }
return out; return out;
} }
float get_pixel(image m, int x, int y, int c)
{
assert(x < m.w && y < m.h && c < m.c);
return m.data[c*m.h*m.w + y*m.w + x];
}
float get_pixel_extend(image m, int x, int y, int c)
{
if(x < 0 || x >= m.w || y < 0 || y >= m.h) return 0;
/*
if(x < 0) x = 0;
if(x >= m.w) x = m.w-1;
if(y < 0) y = 0;
if(y >= m.h) y = m.h-1;
*/
if(c < 0 || c >= m.c) return 0;
return get_pixel(m, x, y, c);
}
void set_pixel(image m, int x, int y, int c, float val)
{
if (x < 0 || y < 0 || c < 0 || x >= m.w || y >= m.h || c >= m.c) return;
assert(x < m.w && y < m.h && c < m.c);
m.data[c*m.h*m.w + y*m.w + x] = val;
}
void add_pixel(image m, int x, int y, int c, float val)
{
assert(x < m.w && y < m.h && c < m.c);
m.data[c*m.h*m.w + y*m.w + x] += val;
}
void print_image(image m) void print_image(image m)
{ {
int i, j, k; int i, j, k;

6
src/image.h
View File

@ -60,12 +60,6 @@ void print_image(image m);
image make_empty_image(int w, int h, int c); image make_empty_image(int w, int h, int c);
void copy_image_into(image src, image dest); void copy_image_into(image src, image dest);
float get_pixel(image m, int x, int y, int c);
float get_pixel_extend(image m, int x, int y, int c);
void set_pixel(image m, int x, int y, int c, float val);
void add_pixel(image m, int x, int y, int c, float val);
float bilinear_interpolate(image im, float x, float y, int c);
image get_image_layer(image m, int l); image get_image_layer(image m, int l);
#endif #endif

9
src/utils.h
View File

@ -5,7 +5,14 @@
#include "darknet.h" #include "darknet.h"
#include "list.h" #include "list.h"
#define TWO_PI 6.2831853071795864769252866 #define TIME(a) \
do { \
double start = what_time_is_it_now(); \
a; \
printf("%s took: %f seconds\n", #a, what_time_is_it_now() - start); \
} while (0)
#define TWO_PI 6.2831853071795864769252866f
double what_time_is_it_now(); double what_time_is_it_now();
void shuffle(void *arr, size_t n, size_t size); void shuffle(void *arr, size_t n, size_t size);