From 8f1b4e0962857d402f9d017fcbf387ef0eceb7c4 Mon Sep 17 00:00:00 2001 From: Joseph Redmon Date: Thu, 1 Sep 2016 16:48:41 -0700 Subject: [PATCH] updates and things --- .gitignore | 1 + Makefile | 8 +- cfg/extraction22k.cfg | 209 + cfg/go.test.cfg | 126 +- cfg/imagenet1k.dataset | 11 +- cfg/yolo.cfg | 16 +- cfg/yolo.train.cfg | 257 + data/dog.jpg | Bin 163759 -> 163759 bytes data/imagenet.labels.list | 21842 ++++++++++++++++++++++++++++++++ data/imagenet.shortnames.list | 21842 ++++++++++++++++++++++++++++++++ data/inet.labels.list | 1000 -- data/shortnames.txt | 1000 -- src/blas.h | 1 + src/blas_kernels.cu | 15 + src/classifier.c | 222 +- src/coco.c | 11 + src/col2im.c | 10 +- src/col2im_kernels.cu | 1 - src/convolutional_kernels.cu | 3 +- src/convolutional_layer.c | 17 +- src/cost_layer.c | 19 + src/darknet.c | 5 +- src/data.c | 37 +- src/data.h | 10 +- src/demo.c | 24 +- src/detection_layer.c | 4 + src/detector.c | 5 + src/im2col.c | 10 +- src/im2col_kernels.cu | 95 +- src/image.c | 491 +- src/image.h | 2 + src/imagenet.c | 237 - src/layer.h | 2 + src/maxpool_layer.c | 24 +- src/maxpool_layer.h | 2 +- src/maxpool_layer_kernels.cu | 28 +- src/network.c | 2 +- src/network.h | 1 + src/parser.c | 21 +- src/region_layer.c | 6 +- src/tag.c | 5 + src/utils.c | 7 + src/utils.h | 1 + src/voxel.c | 56 +- src/yolo.c | 5 + 45 files changed, 44944 insertions(+), 2747 deletions(-) create mode 100644 cfg/extraction22k.cfg create mode 100644 cfg/yolo.train.cfg create mode 100644 data/imagenet.labels.list create mode 100644 data/imagenet.shortnames.list delete mode 100644 data/inet.labels.list delete mode 100644 data/shortnames.txt delete mode 100644 src/imagenet.c diff --git a/.gitignore b/.gitignore index 46f4dbcf..fd44ca29 100644 --- a/.gitignore +++ b/.gitignore @@ -14,6 +14,7 @@ decaf/ submission/ cfg/ darknet +.fuse* # OS Generated # .DS_Store* diff --git a/Makefile b/Makefile index 6878eef0..b36b6b85 100644 --- a/Makefile +++ b/Makefile @@ -1,6 +1,6 @@ -GPU=1 -CUDNN=1 -OPENCV=1 +GPU=0 +CUDNN=0 +OPENCV=0 DEBUG=0 ARCH= --gpu-architecture=compute_52 --gpu-code=compute_52 @@ -41,7 +41,7 @@ CFLAGS+= -DCUDNN LDFLAGS+= -lcudnn endif -OBJ=gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o imagenet.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o super.o voxel.o +OBJ=gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o super.o voxel.o ifeq ($(GPU), 1) LDFLAGS+= -lstdc++ OBJ+=convolutional_kernels.o deconvolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o softmax_layer_kernels.o network_kernels.o avgpool_layer_kernels.o diff --git a/cfg/extraction22k.cfg b/cfg/extraction22k.cfg new file mode 100644 index 00000000..4cec6da9 --- /dev/null +++ b/cfg/extraction22k.cfg @@ -0,0 +1,209 @@ +[net] +batch=128 +subdivisions=1 +height=224 +width=224 +max_crop=320 +channels=3 +momentum=0.9 +decay=0.0005 + +learning_rate=0.01 +max_batches = 0 +policy=steps +steps=444000,590000,970000 +scales=.5,.2,.1 + +#policy=sigmoid +#gamma=.00008 +#step=100000 +#max_batches=200000 + +[convolutional] +batch_normalize=1 +filters=64 +size=7 +stride=2 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=192 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +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=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=256 +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=256 +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=256 +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=256 +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=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=3 +stride=1 +pad=1 +activation=leaky + +[avgpool] + +[connected] +output=21842 +activation=leaky + +[softmax] +groups=1 + +[cost] +type=sse + diff --git a/cfg/go.test.cfg b/cfg/go.test.cfg index 9403e82b..6b92d335 100644 --- a/cfg/go.test.cfg +++ b/cfg/go.test.cfg @@ -3,102 +3,126 @@ batch=1 subdivisions=1 height=19 width=19 -channels=8 +channels=1 momentum=0.9 decay=0.0005 learning_rate=0.1 -max_batches = 0 -policy=steps -steps=50000 -scales=.1 +policy=poly +power=4 +max_batches=400000 [convolutional] -filters=512 +filters=192 size=3 stride=1 pad=1 -activation=leaky +activation=relu batch_normalize=1 [convolutional] -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky -batch_normalize=1 - -[convolutional] -filters=512 +filters=192 size=3 stride=1 pad=1 -activation=leaky +activation=relu batch_normalize=1 [convolutional] -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky -batch_normalize=1 - -[convolutional] -filters=512 +filters=192 size=3 stride=1 pad=1 -activation=leaky +activation=relu batch_normalize=1 [convolutional] -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky -batch_normalize=1 - -[convolutional] -filters=512 +filters=192 size=3 stride=1 pad=1 -activation=leaky +activation=relu batch_normalize=1 [convolutional] -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky -batch_normalize=1 - -[convolutional] -filters=512 +filters=192 size=3 stride=1 pad=1 -activation=leaky +activation=relu batch_normalize=1 [convolutional] -filters=256 -size=1 +filters=192 +size=3 stride=1 pad=1 -activation=leaky +activation=relu batch_normalize=1 +[convolutional] +filters=192 +size=3 +stride=1 +pad=1 +activation=relu +batch_normalize=1 + +[convolutional] +filters=192 +size=3 +stride=1 +pad=1 +activation=relu +batch_normalize=1 + +[convolutional] +filters=192 +size=3 +stride=1 +pad=1 +activation=relu +batch_normalize=1 + +[convolutional] +filters=192 +size=3 +stride=1 +pad=1 +activation=relu +batch_normalize=1 + +[convolutional] +filters=192 +size=3 +stride=1 +pad=1 +activation=relu +batch_normalize=1 + +[convolutional] +filters=192 +size=3 +stride=1 +pad=1 +activation=relu +batch_normalize=1 + +[convolutional] +filters=192 +size=3 +stride=1 +pad=1 +activation=relu +batch_normalize=1 + + [convolutional] filters=1 size=1 stride=1 pad=1 -activation=leaky +activation=linear [softmax] diff --git a/cfg/imagenet1k.dataset b/cfg/imagenet1k.dataset index 92d711d7..6634a274 100644 --- a/cfg/imagenet1k.dataset +++ b/cfg/imagenet1k.dataset @@ -1,9 +1,8 @@ classes=1000 -labels = data/inet.labels.list -names = data/shortnames.txt -train = /data/imagenet/imagenet1k.train.list -valid = /data/imagenet/imagenet1k.valid.list -top=5 -test = /Users/pjreddie/Documents/sites/selfie/paths.list +train = /data/imagenet/imagenet1k.train.list +valid = /data/imagenet/imagenet1k.valid.list backup = /home/pjreddie/backup/ +labels = data/imagenet.labels.list +names = data/imagenet.shortnames.list +top=5 diff --git a/cfg/yolo.cfg b/cfg/yolo.cfg index 6876eff7..c4f415c1 100644 --- a/cfg/yolo.cfg +++ b/cfg/yolo.cfg @@ -1,11 +1,14 @@ [net] -batch=64 -subdivisions=2 +batch=1 +subdivisions=1 height=448 width=448 channels=3 momentum=0.9 decay=0.0005 +saturation=1.5 +exposure=1.5 +hue=.1 learning_rate=0.0005 policy=steps @@ -13,15 +16,6 @@ steps=200,400,600,20000,30000 scales=2.5,2,2,.1,.1 max_batches = 40000 -[crop] -crop_width=448 -crop_height=448 -flip=0 -angle=0 -saturation = 1.5 -exposure = 1.5 -noadjust=1 - [convolutional] batch_normalize=1 filters=64 diff --git a/cfg/yolo.train.cfg b/cfg/yolo.train.cfg new file mode 100644 index 00000000..01aeb5ee --- /dev/null +++ b/cfg/yolo.train.cfg @@ -0,0 +1,257 @@ +[net] +batch=64 +subdivisions=4 +height=448 +width=448 +channels=3 +momentum=0.9 +decay=0.0005 +saturation=1.5 +exposure=1.5 +hue=.1 + +learning_rate=0.0005 +policy=steps +steps=200,400,600,20000,30000 +scales=2.5,2,2,.1,.1 +max_batches = 40000 + +[convolutional] +batch_normalize=1 +filters=64 +size=7 +stride=2 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=192 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +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=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=256 +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=256 +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=256 +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=256 +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=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +####### + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[local] +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[dropout] +probability=.5 + +[connected] +output= 1715 +activation=linear + +[detection] +classes=20 +coords=4 +rescore=1 +side=7 +num=3 +softmax=0 +sqrt=1 +jitter=.2 + +object_scale=1 +noobject_scale=.5 +class_scale=1 +coord_scale=5 + diff --git a/data/dog.jpg b/data/dog.jpg index 665a81c523b042c07e53848cd85c1d0b2c42d55a..77b0381222eaed50867643f4166092c781e56d5b 100644 GIT binary patch delta 39 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-n04591157 -n04540053 -n03866082 -n04136333 -n04026417 -n02865351 -n02834397 -n03888257 -n04235860 -n04404412 -n04371430 -n03733805 -n07920052 -n07873807 -n02895154 -n04204238 -n04597913 -n04131690 -n07836838 -n09835506 -n03443371 -n13037406 -n04336792 -n04557648 -n03187595 -n04254120 -n03595614 -n04146614 -n03598930 -n03958227 -n04069434 -n03188531 -n02786058 -n07615774 -n04525038 -n04409515 -n03424325 -n03223299 -n03680355 -n07614500 -n07695742 -n04033995 -n03710721 -n04392985 -n03047690 -n03584254 -n13054560 -n10565667 -n03950228 -n03729826 -n02837789 -n04254777 -n02988304 -n03657121 -n04417672 -n04523525 -n02815834 -n09229709 -n07697313 -n03888605 -n03355925 -n03063599 -n04116512 -n04325704 -n07831146 -n03255030 diff --git a/data/shortnames.txt b/data/shortnames.txt deleted file mode 100644 index b6c5d03f..00000000 --- a/data/shortnames.txt +++ /dev/null @@ -1,1000 +0,0 @@ -kit fox -English setter -Siberian husky -Australian terrier -English springer -grey whale -lesser panda -Egyptian cat -ibex -Persian cat -cougar -gazelle -porcupine -sea lion -malamute -badger -Great Dane -Walker hound -Welsh springer spaniel -whippet -Scottish deerhound -killer whale -mink -African elephant -Weimaraner -soft-coated wheaten terrier -Dandie Dinmont -red wolf -Old English sheepdog -jaguar -otterhound -bloodhound -Airedale -hyena -meerkat -giant schnauzer -titi -three-toed sloth -sorrel -black-footed ferret -dalmatian -black-and-tan coonhound -papillon -skunk -Staffordshire bullterrier -Mexican hairless -Bouvier des Flandres -weasel -miniature poodle -Cardigan -malinois -bighorn -fox squirrel -colobus -tiger cat -Lhasa -impala -coyote -Yorkshire terrier -Newfoundland -brown bear -red fox -Norwegian elkhound -Rottweiler -hartebeest -Saluki -grey fox -schipperke -Pekinese -Brabancon griffon -West Highland white terrier -Sealyham terrier -guenon -mongoose -indri -tiger -Irish wolfhound -wild boar -EntleBucher -zebra -ram -French bulldog -orangutan -basenji -leopard -Bernese mountain dog -Maltese dog -Norfolk terrier -toy terrier -vizsla -cairn -squirrel monkey -groenendael -clumber -Siamese cat -chimpanzee -komondor -Afghan hound -Japanese spaniel -proboscis monkey -guinea pig -white wolf -ice bear -gorilla -borzoi -toy poodle -Kerry blue terrier -ox -Scotch terrier -Tibetan mastiff -spider monkey -Doberman -Boston bull -Greater Swiss Mountain dog -Appenzeller -Shih-Tzu -Irish water spaniel -Pomeranian -Bedlington terrier -warthog -Arabian camel -siamang -miniature schnauzer -collie -golden retriever -Irish terrier -affenpinscher -Border collie -hare -boxer -silky terrier -beagle -Leonberg -German short-haired pointer -patas -dhole -baboon -macaque -Chesapeake Bay retriever -bull mastiff -kuvasz -capuchin -pug -curly-coated retriever -Norwich terrier -flat-coated retriever -hog -keeshond -Eskimo dog -Brittany spaniel -standard poodle -Lakeland terrier -snow leopard -Gordon setter -dingo -standard schnauzer -hamster -Tibetan terrier -Arctic fox -wire-haired fox terrier -basset -water buffalo -American black bear -Angora -bison -howler monkey -hippopotamus -chow -giant panda -American Staffordshire terrier -Shetland sheepdog -Great Pyrenees -Chihuahua -tabby -marmoset -Labrador retriever -Saint Bernard -armadillo -Samoyed -bluetick -redbone -polecat -marmot -kelpie -gibbon -llama -miniature pinscher -wood rabbit -Italian greyhound -lion -cocker spaniel -Irish setter -dugong -Indian elephant -beaver -Sussex spaniel -Pembroke -Blenheim spaniel -Madagascar cat -Rhodesian ridgeback -lynx -African hunting dog -langur -Ibizan hound -timber wolf -cheetah -English foxhound -briard -sloth bear -Border terrier -German shepherd -otter -koala -tusker -echidna -wallaby -platypus -wombat -revolver -umbrella -schooner -soccer ball -accordion -ant -starfish -chambered nautilus -grand piano -laptop -strawberry -airliner -warplane -airship -balloon -space shuttle -fireboat -gondola -speedboat -lifeboat -canoe -yawl -catamaran -trimaran -container ship -liner -pirate -aircraft carrier -submarine -wreck -half track -tank -missile -bobsled -dogsled -bicycle-built-for-two -mountain bike -freight car -passenger car -barrow -shopping cart -motor scooter -forklift -electric locomotive -steam locomotive -amphibian -ambulance -beach wagon -cab -convertible -jeep -limousine -minivan -Model T -racer -sports car -go-kart -golfcart -moped -snowplow -fire engine -garbage truck -pickup -tow truck -trailer truck -moving van -police van -recreational vehicle -streetcar -snowmobile -tractor -mobile home -tricycle -unicycle -horse cart -jinrikisha -oxcart -bassinet -cradle -crib -four-poster -bookcase -china cabinet -medicine chest -chiffonier -table lamp -file -park bench -barber chair -throne -folding chair -rocking chair -studio couch -toilet seat -desk -pool table -dining table -entertainment center -wardrobe -Granny Smith -orange -lemon -fig -pineapple -banana -jackfruit -custard apple -pomegranate -acorn -hip -ear -rapeseed -corn -buckeye -organ -upright -chime -drum -gong -maraca -marimba -steel drum -banjo -cello -violin -harp -acoustic guitar -electric guitar -cornet -French horn -trombone -harmonica -ocarina -panpipe -bassoon -oboe -sax -flute -daisy -yellow lady's slipper -cliff -valley -alp -volcano -promontory -sandbar -coral reef -lakeside -seashore -geyser -hatchet -cleaver -letter opener -plane -power drill -lawn mower -hammer -corkscrew -can opener -plunger -screwdriver -shovel -plow -chain saw -cock -hen -ostrich -brambling -goldfinch -house finch -junco -indigo bunting -robin -bulbul -jay -magpie -chickadee -water ouzel -kite -bald eagle -vulture -great grey owl -black grouse -ptarmigan -ruffed grouse -prairie chicken -peacock -quail -partridge -African grey -macaw -sulphur-crested cockatoo -lorikeet -coucal -bee eater -hornbill -hummingbird -jacamar -toucan -drake -red-breasted merganser -goose -black swan -white stork -black stork -spoonbill -flamingo -American egret -little blue heron -bittern -crane -limpkin -American coot -bustard -ruddy turnstone -red-backed sandpiper -redshank -dowitcher -oystercatcher -European gallinule -pelican -king penguin -albatross -great white shark -tiger shark -hammerhead -electric ray -stingray -barracouta -coho -tench -goldfish -eel -rock beauty -anemone fish -lionfish -puffer -sturgeon -gar -loggerhead -leatherback turtle -mud turtle -terrapin -box turtle -banded gecko -common iguana -American chameleon -whiptail -agama -frilled lizard -alligator lizard -Gila monster -green lizard -African chameleon -Komodo dragon -triceratops -African crocodile -American alligator -thunder snake -ringneck snake -hognose snake -green snake -king snake -garter snake -water snake -vine snake -night snake -boa constrictor -rock python -Indian cobra -green mamba -sea snake -horned viper -diamondback -sidewinder -European fire salamander -common newt -eft -spotted salamander -axolotl -bullfrog -tree frog -tailed frog -whistle -wing -paintbrush -hand blower -oxygen mask -snorkel -loudspeaker -microphone -screen -mouse -electric fan -oil filter -strainer -space heater -stove -guillotine -barometer -rule -odometer -scale -analog clock -digital clock -wall clock -hourglass -sundial -parking meter -stopwatch -digital watch -stethoscope -syringe -magnetic compass -binoculars -projector -sunglasses -loupe -radio telescope -bow -cannon -assault rifle -rifle -projectile -computer keyboard -typewriter keyboard -crane -lighter -abacus -cash machine -slide rule -desktop computer -hand-held computer -notebook -web site -harvester -thresher -printer -slot -vending machine -sewing machine -joystick -switch -hook -car wheel -paddlewheel -pinwheel -potter's wheel -gas pump -carousel -swing -reel -radiator -puck -hard disc -sunglass -pick -car mirror -solar dish -remote control -disk brake -buckle -hair slide -knot -combination lock -padlock -nail -safety pin -screw -muzzle -seat belt -ski -candle -jack-o'-lantern -spotlight -torch -neck brace -pier -tripod -maypole -mousetrap -spider web -trilobite -harvestman -scorpion -black and gold garden spider -barn spider -garden spider -black widow -tarantula -wolf spider -tick -centipede -isopod -Dungeness crab -rock crab -fiddler crab -king crab -American lobster -spiny lobster -crayfish -hermit crab -tiger beetle -ladybug -ground beetle -long-horned beetle -leaf beetle -dung beetle -rhinoceros beetle -weevil -fly -bee -grasshopper -cricket -walking stick -cockroach -mantis -cicada -leafhopper -lacewing -dragonfly -damselfly -admiral -ringlet -monarch -cabbage butterfly -sulphur butterfly -lycaenid -jellyfish -sea anemone -brain coral -flatworm -nematode -conch -snail -slug -sea slug -chiton -sea urchin -sea cucumber -iron -espresso maker -microwave -Dutch oven -rotisserie -toaster -waffle iron -vacuum -dishwasher -refrigerator -washer -Crock Pot -frying pan -wok -caldron -coffeepot -teapot -spatula -altar -triumphal arch -patio -steel arch bridge -suspension bridge -viaduct -barn -greenhouse -palace -monastery -library -apiary -boathouse -church -mosque -stupa -planetarium -restaurant -cinema -home theater -lumbermill -coil -obelisk -totem pole -castle -prison -grocery store -bakery -barbershop -bookshop -butcher shop -confectionery -shoe shop -tobacco shop -toyshop -fountain -cliff dwelling -yurt -dock -brass -megalith -bannister -breakwater -dam -chainlink fence -picket fence -worm fence -stone wall -grille -sliding door -turnstile -mountain tent -scoreboard -honeycomb -plate rack -pedestal -beacon -mashed potato -bell pepper -head cabbage -broccoli -cauliflower -zucchini -spaghetti squash -acorn squash -butternut squash -cucumber -artichoke -cardoon -mushroom -shower curtain -jean -carton -handkerchief -sandal -ashcan -safe -plate -necklace -croquet ball -fur coat -thimble -pajama -running shoe -cocktail shaker -chest -manhole cover -modem -tub -tray -balance beam -bagel -prayer rug -kimono -hot pot -whiskey jug -knee pad -book jacket -spindle -ski mask -beer bottle -crash helmet -bottlecap -tile roof -mask -maillot -Petri dish -football helmet -bathing cap -teddy -holster -pop bottle -photocopier -vestment -crossword puzzle -golf ball -trifle -suit -water tower -feather boa -cloak -red wine -drumstick -shield -Christmas stocking -hoopskirt -menu -stage -bonnet -meat loaf -baseball -face powder -scabbard -sunscreen -beer glass -hen-of-the-woods -guacamole -lampshade -wool -hay -bow tie -mailbag -water jug -bucket -dishrag -soup bowl -eggnog -mortar -trench coat -paddle -chain -swab -mixing bowl -potpie -wine bottle -shoji -bulletproof vest -drilling platform -binder -cardigan -sweatshirt -pot -birdhouse -hamper -ping-pong ball -pencil box -pay-phone -consomme -apron -punching bag -backpack -groom -bearskin -pencil sharpener -broom -mosquito net -abaya -mortarboard -poncho -crutch -Polaroid camera -space bar -cup -racket -traffic light -quill -radio -dough -cuirass -military uniform -lipstick -shower cap -monitor -oscilloscope -mitten -brassiere -French loaf -vase -milk can -rugby ball -paper towel -earthstar -envelope -miniskirt -cowboy hat -trolleybus -perfume -bathtub -hotdog -coral fungus -bullet train -pillow -toilet tissue -cassette -carpenter's kit -ladle -stinkhorn -lotion -hair spray -academic gown -dome -crate -wig -burrito -pill bottle -chain mail -theater curtain -window shade -barrel -washbasin -ballpoint -basketball -bath towel -cowboy boot -gown -window screen -agaric -cellular telephone -nipple -barbell -mailbox -lab coat -fire screen -minibus -packet -maze -pole -horizontal bar -sombrero -pickelhaube -rain barrel -wallet -cassette player -comic book -piggy bank -street sign -bell cote -fountain pen -Windsor tie -volleyball -overskirt -sarong -purse -bolo tie -bib -parachute -sleeping bag -television -swimming trunks -measuring cup -espresso -pizza -breastplate -shopping basket -wooden spoon -saltshaker -chocolate sauce -ballplayer -goblet -gyromitra -stretcher -water bottle -dial telephone -soap dispenser -jersey -school bus -jigsaw puzzle -plastic bag -reflex camera -diaper -Band Aid -ice lolly -velvet -tennis ball -gasmask -doormat -Loafer -ice cream -pretzel -quilt -maillot -tape player -clog -iPod -bolete -scuba diver -pitcher -matchstick -bikini -sock -CD player -lens cap -thatch -vault -beaker -bubble -cheeseburger -parallel bars -flagpole -coffee mug -rubber eraser -stole -carbonara -dumbbell diff --git a/src/blas.h b/src/blas.h index 4fdaa412..95374c95 100644 --- a/src/blas.h +++ b/src/blas.h @@ -40,6 +40,7 @@ void axpy_ongpu_offset(int N, float ALPHA, float * X, int OFFX, int INCX, float void copy_ongpu(int N, float * X, int INCX, float * Y, int INCY); void copy_ongpu_offset(int N, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY); void scal_ongpu(int N, float ALPHA, float * X, int INCX); +void supp_ongpu(int N, float ALPHA, float * X, int INCX); void mask_ongpu(int N, float * X, float mask_num, float * mask); void const_ongpu(int N, float ALPHA, float *X, int INCX); void pow_ongpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY); diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 3f7f1f95..271f0178 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -368,6 +368,14 @@ __global__ void constrain_kernel(int N, float ALPHA, float *X, int INCX) if(i < N) X[i*INCX] = min(ALPHA, max(-ALPHA, X[i*INCX])); } +__global__ void supp_kernel(int N, float ALPHA, float *X, int INCX) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < N) { + if((X[i*INCX] * X[i*INCX]) < (ALPHA * ALPHA)) X[i*INCX] = 0; + } +} + __global__ void scal_kernel(int N, float ALPHA, float *X, int INCX) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; @@ -552,6 +560,12 @@ extern "C" void scal_ongpu(int N, float ALPHA, float * X, int INCX) check_error(cudaPeekAtLastError()); } +extern "C" void supp_ongpu(int N, float ALPHA, float * X, int INCX) +{ + supp_kernel<<>>(N, ALPHA, X, INCX); + check_error(cudaPeekAtLastError()); +} + extern "C" void fill_ongpu(int N, float ALPHA, float * X, int INCX) { fill_kernel<<>>(N, ALPHA, X, INCX); @@ -633,6 +647,7 @@ extern "C" void l2_gpu(int n, float *pred, float *truth, float *delta, float *er } + __global__ void weighted_sum_kernel(int n, float *a, float *b, float *s, float *c) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; diff --git a/src/classifier.c b/src/classifier.c index ee6d212e..e59f7ae3 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -39,6 +39,18 @@ list *read_data_cfg(char *filename) return options; } +float *get_regression_values(char **labels, int n) +{ + float *v = calloc(n, sizeof(float)); + int i; + for(i = 0; i < n; ++i){ + char *p = strchr(labels[i], ' '); + *p = 0; + v[i] = atof(p+1); + } + return v; +} + void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int clear) { int nthreads = 8; @@ -85,6 +97,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int clear) args.angle = net.angle; args.exposure = net.exposure; args.saturation = net.saturation; + args.hue = net.hue; args.size = net.w; args.paths = paths; @@ -116,6 +129,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int clear) printf("Loaded: %lf seconds\n", sec(clock()-time)); time=clock(); + #ifdef OPENCV if(0){ int u; for(u = 0; u < imgs; ++u){ @@ -124,6 +138,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int clear) cvWaitKey(0); } } + #endif float loss = train_network(net, train); if(avg_loss == -1) avg_loss = loss; @@ -440,7 +455,7 @@ void validate_classifier_multi(char *datacfg, char *filename, char *weightfile) char **labels = get_labels(label_list); list *plist = get_paths(valid_list); - int scales[] = {192, 224, 288, 320, 352}; + int scales[] = {224, 288, 320, 352, 384}; int nscales = sizeof(scales)/sizeof(scales[0]); char **paths = (char **)list_to_array(plist); @@ -484,6 +499,88 @@ void validate_classifier_multi(char *datacfg, char *filename, char *weightfile) } } +void try_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int layer_num) +{ + network net = parse_network_cfg(cfgfile); + if(weightfile){ + load_weights(&net, weightfile); + } + set_batch_network(&net, 1); + srand(2222222); + + list *options = read_data_cfg(datacfg); + + char *name_list = option_find_str(options, "names", 0); + if(!name_list) name_list = option_find_str(options, "labels", "data/labels.list"); + int top = option_find_int(options, "top", 1); + + int i = 0; + char **names = get_labels(name_list); + clock_t time; + int *indexes = calloc(top, sizeof(int)); + char buff[256]; + char *input = buff; + while(1){ + if(filename){ + strncpy(input, filename, 256); + }else{ + printf("Enter Image Path: "); + fflush(stdout); + input = fgets(input, 256, stdin); + if(!input) return; + strtok(input, "\n"); + } + image orig = load_image_color(input, 0, 0); + image r = resize_min(orig, 256); + image im = crop_image(r, (r.w - 224 - 1)/2 + 1, (r.h - 224 - 1)/2 + 1, 224, 224); + float mean[] = {0.48263312050943, 0.45230225481413, 0.40099074308742}; + float std[] = {0.22590347483426, 0.22120921437787, 0.22103996251583}; + float var[3]; + var[0] = std[0]*std[0]; + var[1] = std[1]*std[1]; + var[2] = std[2]*std[2]; + + normalize_cpu(im.data, mean, var, 1, 3, im.w*im.h); + + float *X = im.data; + time=clock(); + float *predictions = network_predict(net, X); + + layer l = net.layers[layer_num]; + for(i = 0; i < l.c; ++i){ + if(l.rolling_mean) printf("%f %f %f\n", l.rolling_mean[i], l.rolling_variance[i], l.scales[i]); + } + #ifdef GPU + cuda_pull_array(l.output_gpu, l.output, l.outputs); + #endif + for(i = 0; i < l.outputs; ++i){ + printf("%f\n", l.output[i]); + } + /* + + printf("\n\nWeights\n"); + for(i = 0; i < l.n*l.size*l.size*l.c; ++i){ + printf("%f\n", l.filters[i]); + } + + printf("\n\nBiases\n"); + for(i = 0; i < l.n; ++i){ + printf("%f\n", l.biases[i]); + } + */ + + top_predictions(net, top, indexes); + printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); + for(i = 0; i < top; ++i){ + int index = indexes[i]; + printf("%s: %f\n", names[index], predictions[index]); + } + free_image(im); + if (filename) break; + } +} + + void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename) { network net = parse_network_cfg(cfgfile); @@ -649,6 +746,127 @@ void test_classifier(char *datacfg, char *cfgfile, char *weightfile, int target_ } +void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename) +{ +#ifdef OPENCV + float threat = 0; + float roll = .2; + + printf("Classifier Demo\n"); + network net = parse_network_cfg(cfgfile); + if(weightfile){ + load_weights(&net, weightfile); + } + set_batch_network(&net, 1); + list *options = read_data_cfg(datacfg); + + srand(2222222); + CvCapture * cap; + + if(filename){ + cap = cvCaptureFromFile(filename); + }else{ + cap = cvCaptureFromCAM(cam_index); + } + + int top = option_find_int(options, "top", 1); + + char *name_list = option_find_str(options, "names", 0); + char **names = get_labels(name_list); + + int *indexes = calloc(top, sizeof(int)); + + if(!cap) error("Couldn't connect to webcam.\n"); + //cvNamedWindow("Threat", CV_WINDOW_NORMAL); + //cvResizeWindow("Threat", 512, 512); + float fps = 0; + int i; + + int count = 0; + + while(1){ + ++count; + struct timeval tval_before, tval_after, tval_result; + gettimeofday(&tval_before, NULL); + + image in = get_image_from_stream(cap); + if(!in.data) break; + image in_s = resize_image(in, net.w, net.h); + + image out = in; + int x1 = out.w / 20; + int y1 = out.h / 20; + int x2 = 2*x1; + int y2 = out.h - out.h/20; + + int border = .01*out.h; + int h = y2 - y1 - 2*border; + int w = x2 - x1 - 2*border; + + float *predictions = network_predict(net, in_s.data); + float curr_threat = predictions[0] * 0 + predictions[1] * .6 + predictions[2]; + threat = roll * curr_threat + (1-roll) * threat; + + draw_box_width(out, x2 + border, y1 + .02*h, x2 + .5 * w, y1 + .02*h + border, border, 0,0,0); + if(threat > .97) { + draw_box_width(out, x2 + .5 * w + border, + y1 + .02*h - 2*border, + x2 + .5 * w + 6*border, + y1 + .02*h + 3*border, 3*border, 1,0,0); + } + draw_box_width(out, x2 + .5 * w + border, + y1 + .02*h - 2*border, + x2 + .5 * w + 6*border, + y1 + .02*h + 3*border, .5*border, 0,0,0); + draw_box_width(out, x2 + border, y1 + .42*h, x2 + .5 * w, y1 + .42*h + border, border, 0,0,0); + if(threat > .57) { + draw_box_width(out, x2 + .5 * w + border, + y1 + .42*h - 2*border, + x2 + .5 * w + 6*border, + y1 + .42*h + 3*border, 3*border, 1,1,0); + } + draw_box_width(out, x2 + .5 * w + border, + y1 + .42*h - 2*border, + x2 + .5 * w + 6*border, + y1 + .42*h + 3*border, .5*border, 0,0,0); + + draw_box_width(out, x1, y1, x2, y2, border, 0,0,0); + for(i = 0; i < threat * h ; ++i){ + float ratio = (float) i / h; + float r = (ratio < .5) ? (2*(ratio)) : 1; + float g = (ratio < .5) ? 1 : 1 - 2*(ratio - .5); + draw_box_width(out, x1 + border, y2 - border - i, x2 - border, y2 - border - i, 1, r, g, 0); + } + top_predictions(net, top, indexes); + char buff[256]; + sprintf(buff, "/home/pjreddie/tmp/threat_%06d", count); + save_image(out, buff); + + printf("\033[2J"); + printf("\033[1;1H"); + printf("\nFPS:%.0f\n",fps); + + for(i = 0; i < top; ++i){ + int index = indexes[i]; + printf("%.1f%%: %s\n", predictions[index]*100, names[index]); + } + + if(0){ + show_image(out, "Threat"); + cvWaitKey(10); + } + free_image(in_s); + free_image(in); + + gettimeofday(&tval_after, NULL); + timersub(&tval_after, &tval_before, &tval_result); + float curr = 1000000.f/((long int)tval_result.tv_usec); + fps = .9*fps + .1*curr; + } +#endif +} + + void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename) { #ifdef OPENCV @@ -732,8 +950,10 @@ void run_classifier(int argc, char **argv) char *layer_s = (argc > 7) ? argv[7]: 0; int layer = layer_s ? atoi(layer_s) : -1; if(0==strcmp(argv[2], "predict")) predict_classifier(data, cfg, weights, filename); + else if(0==strcmp(argv[2], "try")) try_classifier(data, cfg, weights, filename, atoi(layer_s)); else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights, clear); else if(0==strcmp(argv[2], "demo")) demo_classifier(data, cfg, weights, cam_index, filename); + else if(0==strcmp(argv[2], "threat")) threat_classifier(data, cfg, weights, cam_index, filename); else if(0==strcmp(argv[2], "test")) test_classifier(data, cfg, weights, layer); else if(0==strcmp(argv[2], "label")) label_classifier(data, cfg, weights); else if(0==strcmp(argv[2], "valid")) validate_classifier_single(data, cfg, weights); diff --git a/src/coco.c b/src/coco.c index 8ded9511..13718705 100644 --- a/src/coco.c +++ b/src/coco.c @@ -25,6 +25,7 @@ void train_coco(char *cfgfile, char *weightfile) //char *train_images = "/home/pjreddie/data/voc/test/train.txt"; //char *train_images = "/home/pjreddie/data/coco/train.txt"; char *train_images = "data/coco.trainval.txt"; + //char *train_images = "data/bags.train.list"; char *backup_directory = "/home/pjreddie/backup/"; srand(time(0)); data_seed = time(0); @@ -63,6 +64,11 @@ void train_coco(char *cfgfile, char *weightfile) args.d = &buffer; args.type = REGION_DATA; + args.angle = net.angle; + args.exposure = net.exposure; + args.saturation = net.saturation; + args.hue = net.hue; + pthread_t load_thread = load_data_in_thread(args); clock_t time; //while(i*imgs < N*120){ @@ -94,6 +100,11 @@ void train_coco(char *cfgfile, char *weightfile) sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i); save_weights(net, buff); } + if(i%100==0){ + char buff[256]; + sprintf(buff, "%s/%s.backup", backup_directory, base); + save_weights(net, buff); + } free_data(train); } char buff[256]; diff --git a/src/col2im.c b/src/col2im.c index d63a1e0b..5c4605e1 100644 --- a/src/col2im.c +++ b/src/col2im.c @@ -16,13 +16,9 @@ void col2im_cpu(float* data_col, int ksize, int stride, int pad, float* data_im) { int c,h,w; - int height_col = (height - ksize) / stride + 1; - int width_col = (width - ksize) / stride + 1; - if (pad){ - height_col = 1 + (height-1) / stride; - width_col = 1 + (width-1) / stride; - pad = ksize/2; - } + int height_col = (height + 2*pad - ksize) / stride + 1; + int width_col = (width + 2*pad - ksize) / stride + 1; + int channels_col = channels * ksize * ksize; for (c = 0; c < channels_col; ++c) { int w_offset = c % ksize; diff --git a/src/col2im_kernels.cu b/src/col2im_kernels.cu index b381f1a7..aed2df9b 100644 --- a/src/col2im_kernels.cu +++ b/src/col2im_kernels.cu @@ -46,7 +46,6 @@ void col2im_ongpu(float *data_col, int ksize, int stride, int pad, float *data_im){ // We are going to launch channels * height_col * width_col kernels, each // kernel responsible for copying a single-channel grid. - pad = pad ? ksize/2 : 0; int height_col = (height + 2 * pad - ksize) / stride + 1; int width_col = (width + 2 * pad - ksize) / stride + 1; int num_kernels = channels * height * width; diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 96398df2..43b3f9aa 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -17,7 +17,7 @@ __global__ void binarize_kernel(float *x, int n, float *binary) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (i >= n) return; - binary[i] = (x[i] > 0) ? 1 : -1; + binary[i] = (x[i] >= 0) ? 1 : -1; } void binarize_gpu(float *x, int n, float *binary) @@ -60,6 +60,7 @@ __global__ void binarize_filters_kernel(float *filters, int n, int size, float * mean = mean / size; for(i = 0; i < size; ++i){ binary[f*size + i] = (filters[f*size + i] > 0) ? mean : -mean; + //binary[f*size + i] = filters[f*size + i]; } } diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 006dc4c6..ad2d8a58 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -70,18 +70,12 @@ void binarize_input(float *input, int n, int size, float *binary) int convolutional_out_height(convolutional_layer l) { - int h = l.h; - if (!l.pad) h -= l.size; - else h -= 1; - return h/l.stride + 1; + return (l.h + 2*l.pad - l.size) / l.stride + 1; } int convolutional_out_width(convolutional_layer l) { - int w = l.w; - if (!l.pad) w -= l.size; - else w -= 1; - return w/l.stride + 1; + return (l.w + 2*l.pad - l.size) / l.stride + 1; } image get_convolutional_image(convolutional_layer l) @@ -148,8 +142,7 @@ void cudnn_convolutional_setup(layer *l) 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); + cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION); cudnnGetConvolutionForwardAlgorithm(cudnn_handle(), l->srcTensorDesc, l->filterDesc, @@ -178,7 +171,7 @@ 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_normalize, int binary, int xnor) +convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor) { int i; convolutional_layer l = {0}; @@ -193,7 +186,7 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int l.batch = batch; l.stride = stride; l.size = size; - l.pad = pad; + l.pad = padding; l.batch_normalize = batch_normalize; l.filters = calloc(c*n*size*size, sizeof(float)); diff --git a/src/cost_layer.c b/src/cost_layer.c index fdba7771..0d8cb8c1 100644 --- a/src/cost_layer.c +++ b/src/cost_layer.c @@ -98,6 +98,15 @@ void push_cost_layer(cost_layer l) cuda_push_array(l.delta_gpu, l.delta, l.batch*l.inputs); } +int float_abs_compare (const void * a, const void * b) +{ + float fa = *(const float*) a; + if(fa < 0) fa = -fa; + float fb = *(const float*) b; + if(fb < 0) fb = -fb; + return (fa > fb) - (fa < fb); +} + void forward_cost_layer_gpu(cost_layer l, network_state state) { if (!state.truth) return; @@ -111,6 +120,16 @@ void forward_cost_layer_gpu(cost_layer l, network_state state) l2_gpu(l.batch*l.inputs, state.input, state.truth, l.delta_gpu, l.output_gpu); } + if(l.ratio){ + cuda_pull_array(l.delta_gpu, l.delta, l.batch*l.inputs); + qsort(l.delta, l.batch*l.inputs, sizeof(float), float_abs_compare); + int n = (1-l.ratio) * l.batch*l.inputs; + float thresh = l.delta[n]; + thresh = 0; + printf("%f\n", thresh); + supp_ongpu(l.batch*l.inputs, thresh, l.delta_gpu, 1); + } + cuda_pull_array(l.output_gpu, l.output, l.batch*l.inputs); l.cost[0] = sum_array(l.output, l.batch*l.inputs); } diff --git a/src/darknet.c b/src/darknet.c index c367abff..263349eb 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -13,7 +13,6 @@ #endif extern void run_voxel(int argc, char **argv); -extern void run_imagenet(int argc, char **argv); extern void run_yolo(int argc, char **argv); extern void run_detector(int argc, char **argv); extern void run_coco(int argc, char **argv); @@ -327,9 +326,7 @@ int main(int argc, char **argv) } #endif - if(0==strcmp(argv[1], "imagenet")){ - run_imagenet(argc, argv); - } else if (0 == strcmp(argv[1], "average")){ + if (0 == strcmp(argv[1], "average")){ average(argc, argv); } else if (0 == strcmp(argv[1], "yolo")){ run_yolo(argc, argv); diff --git a/src/data.c b/src/data.c index 9aa08af4..09872e5f 100644 --- a/src/data.c +++ b/src/data.c @@ -100,7 +100,7 @@ matrix load_image_paths(char **paths, int n, int w, int h) return X; } -matrix load_image_augment_paths(char **paths, int n, int min, int max, int size, float angle, float exposure, float saturation) +matrix load_image_augment_paths(char **paths, int n, int min, int max, int size, float angle, float hue, float saturation, float exposure) { int i; matrix X; @@ -113,10 +113,7 @@ matrix load_image_augment_paths(char **paths, int n, int min, int max, int size, image crop = random_augment_image(im, angle, min, max, size); int flip = rand_r(&data_seed)%2; if (flip) flip_image(crop); - float exp = rand_uniform(1./exposure, exposure); - float sat = rand_uniform(1./saturation, saturation); - exposure_image(crop, exp); - exposure_image(crop, sat); + random_distort_image(crop, hue, saturation, exposure); /* show_image(im, "orig"); @@ -241,6 +238,7 @@ void fill_truth_region(char *path, float *truth, int classes, int num_boxes, int labelpath = find_replace(labelpath, "JPEGImages", "labels"); labelpath = find_replace(labelpath, ".jpg", ".txt"); + labelpath = find_replace(labelpath, ".png", ".txt"); labelpath = find_replace(labelpath, ".JPG", ".txt"); labelpath = find_replace(labelpath, ".JPEG", ".txt"); int count = 0; @@ -287,6 +285,7 @@ void fill_truth_detection(char *path, int num_boxes, float *truth, int classes, labelpath = find_replace(labelpath, "JPEGImages", "labels"); labelpath = find_replace(labelpath, ".jpg", ".txt"); + labelpath = find_replace(labelpath, ".png", ".txt"); labelpath = find_replace(labelpath, ".JPG", ".txt"); labelpath = find_replace(labelpath, ".JPEG", ".txt"); int count = 0; @@ -443,7 +442,7 @@ void free_data(data d) } } -data load_data_region(int n, char **paths, int m, int w, int h, int size, int classes, float jitter) +data load_data_region(int n, char **paths, int m, int w, int h, int size, int classes, float jitter, float hue, float saturation, float exposure) { char **random_paths = get_random_paths(paths, n, m); int i; @@ -485,6 +484,7 @@ data load_data_region(int n, char **paths, int m, int w, int h, int size, int cl image sized = resize_image(cropped, w, h); if(flip) flip_image(sized); + random_distort_image(sized, hue, saturation, exposure); d.X.vals[i] = sized.data; fill_truth_region(random_paths[i], d.y.vals[i], classes, size, flip, dx, dy, 1./sx, 1./sy); @@ -611,7 +611,7 @@ data load_data_swag(char **paths, int n, int classes, float jitter) return d; } -data load_data_detection(int n, char **paths, int m, int w, int h, int boxes, int classes, float jitter) +data load_data_detection(int n, char **paths, int m, int w, int h, int boxes, int classes, float jitter, float hue, float saturation, float exposure) { char **random_paths = get_random_paths(paths, n, m); int i; @@ -651,6 +651,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int boxes, in image sized = resize_image(cropped, w, h); if(flip) flip_image(sized); + random_distort_image(sized, hue, saturation, exposure); d.X.vals[i] = sized.data; fill_truth_detection(random_paths[i], boxes, d.y.vals[i], classes, flip, dx, dy, 1./sx, 1./sy); @@ -679,17 +680,17 @@ void *load_thread(void *ptr) if (a.type == OLD_CLASSIFICATION_DATA){ *a.d = load_data(a.paths, a.n, a.m, a.labels, a.classes, a.w, a.h); } else if (a.type == CLASSIFICATION_DATA){ - *a.d = load_data_augment(a.paths, a.n, a.m, a.labels, a.classes, a.min, a.max, a.size, a.angle, a.exposure, a.saturation); + *a.d = load_data_augment(a.paths, a.n, a.m, a.labels, a.classes, a.min, a.max, a.size, a.angle, a.hue, a.saturation, a.exposure); } else if (a.type == SUPER_DATA){ *a.d = load_data_super(a.paths, a.n, a.m, a.w, a.h, a.scale); } else if (a.type == STUDY_DATA){ - *a.d = load_data_study(a.paths, a.n, a.m, a.labels, a.classes, a.min, a.max, a.size, a.angle, a.exposure, a.saturation); + *a.d = load_data_study(a.paths, a.n, a.m, a.labels, a.classes, a.min, a.max, a.size, a.angle, a.hue, a.saturation, a.exposure); } else if (a.type == WRITING_DATA){ *a.d = load_data_writing(a.paths, a.n, a.m, a.w, a.h, a.out_w, a.out_h); } else if (a.type == REGION_DATA){ - *a.d = load_data_region(a.n, a.paths, a.m, a.w, a.h, a.num_boxes, a.classes, a.jitter); + *a.d = load_data_region(a.n, a.paths, a.m, a.w, a.h, a.num_boxes, a.classes, a.jitter, a.hue, a.saturation, a.exposure); } else if (a.type == DETECTION_DATA){ - *a.d = load_data_detection(a.n, a.paths, a.m, a.w, a.h, a.num_boxes, a.classes, a.jitter); + *a.d = load_data_detection(a.n, a.paths, a.m, a.w, a.h, a.num_boxes, a.classes, a.jitter, a.hue, a.saturation, a.exposure); } else if (a.type == SWAG_DATA){ *a.d = load_data_swag(a.paths, a.n, a.classes, a.jitter); } else if (a.type == COMPARE_DATA){ @@ -698,7 +699,7 @@ void *load_thread(void *ptr) *(a.im) = load_image_color(a.path, 0, 0); *(a.resized) = resize_image(*(a.im), a.w, a.h); } else if (a.type == TAG_DATA){ - *a.d = load_data_tag(a.paths, a.n, a.m, a.classes, a.min, a.max, a.size, a.angle, a.exposure, a.saturation); + *a.d = load_data_tag(a.paths, a.n, a.m, a.classes, a.min, a.max, a.size, a.angle, a.hue, a.saturation, a.exposure); //*a.d = load_data(a.paths, a.n, a.m, a.labels, a.classes, a.w, a.h); } free(ptr); @@ -740,13 +741,13 @@ data load_data(char **paths, int n, int m, char **labels, int k, int w, int h) return d; } -data load_data_study(char **paths, int n, int m, char **labels, int k, int min, int max, int size, float angle, float exposure, float saturation) +data load_data_study(char **paths, int n, int m, char **labels, int k, int min, int max, int size, float angle, float hue, float saturation, float exposure) { data d = {0}; d.indexes = calloc(n, sizeof(int)); if(m) paths = get_random_paths_indexes(paths, n, m, d.indexes); d.shallow = 0; - d.X = load_image_augment_paths(paths, n, min, max, size, angle, exposure, saturation); + d.X = load_image_augment_paths(paths, n, min, max, size, angle, hue, saturation, exposure); d.y = load_labels_paths(paths, n, labels, k); if(m) free(paths); return d; @@ -782,25 +783,25 @@ data load_data_super(char **paths, int n, int m, int w, int h, int scale) return d; } -data load_data_augment(char **paths, int n, int m, char **labels, int k, int min, int max, int size, float angle, float exposure, float saturation) +data load_data_augment(char **paths, int n, int m, char **labels, int k, int min, int max, int size, float angle, float hue, float saturation, float exposure) { if(m) paths = get_random_paths(paths, n, m); data d = {0}; d.shallow = 0; - d.X = load_image_augment_paths(paths, n, min, max, size, angle, exposure, saturation); + d.X = load_image_augment_paths(paths, n, min, max, size, angle, hue, saturation, exposure); d.y = load_labels_paths(paths, n, labels, k); if(m) free(paths); return d; } -data load_data_tag(char **paths, int n, int m, int k, int min, int max, int size, float angle, float exposure, float saturation) +data load_data_tag(char **paths, int n, int m, int k, int min, int max, int size, float angle, float hue, float saturation, float exposure) { if(m) paths = get_random_paths(paths, n, m); data d = {0}; d.w = size; d.h = size; d.shallow = 0; - d.X = load_image_augment_paths(paths, n, min, max, size, angle, exposure, saturation); + d.X = load_image_augment_paths(paths, n, min, max, size, angle, hue, saturation, exposure); d.y = load_tags_paths(paths, n, k); if(m) free(paths); return d; diff --git a/src/data.h b/src/data.h index 1220be05..c3e74cdb 100644 --- a/src/data.h +++ b/src/data.h @@ -54,6 +54,7 @@ typedef struct load_args{ float angle; float saturation; float exposure; + float hue; data *d; image *im; image *resized; @@ -74,11 +75,12 @@ void print_letters(float *pred, int n); data load_data_captcha(char **paths, int n, int m, int k, int w, int h); data load_data_captcha_encode(char **paths, int n, int m, int w, int h); data load_data(char **paths, int n, int m, char **labels, int k, int w, int h); -data load_data_detection(int n, char **paths, int m, int w, int h, int boxes, int classes, float jitter); -data load_data_tag(char **paths, int n, int m, int k, int min, int max, int size, float angle, float exposure, float saturation); -data load_data_augment(char **paths, int n, int m, char **labels, int k, int min, int max, int size, float angle, float exposure, float saturation); +data load_data_detection(int n, char **paths, int m, int w, int h, int boxes, int classes, float jitter, float hue, float saturation, float exposure); +data load_data_tag(char **paths, int n, int m, int k, int min, int max, int size, float angle, float hue, float saturation, float exposure); +matrix load_image_augment_paths(char **paths, int n, int min, int max, int size, float angle, float hue, float saturation, float exposure); data load_data_super(char **paths, int n, int m, int w, int h, int scale); -data load_data_study(char **paths, int n, int m, char **labels, int k, int min, int max, int size, float angle, float exposure, float saturation); +data load_data_study(char **paths, int n, int m, char **labels, int k, int min, int max, int size, float angle, float hue, float saturation, float exposure); +data load_data_augment(char **paths, int n, int m, char **labels, int k, int min, int max, int size, float angle, float hue, float saturation, float exposure); data load_go(char *filename); box_label *read_boxes(char *filename, int *n); diff --git a/src/demo.c b/src/demo.c index 2e7ab938..020513f6 100644 --- a/src/demo.c +++ b/src/demo.c @@ -8,7 +8,7 @@ #include "demo.h" #include -#define FRAMES 1 +#define FRAMES 3 #ifdef OPENCV #include "opencv2/highgui/highgui_c.h" @@ -48,7 +48,7 @@ void *fetch_in_thread(void *ptr) void *detect_in_thread(void *ptr) { - float nms = .4; + float nms = .1; detection_layer l = net.layers[net.n-1]; float *X = det_s.data; @@ -153,13 +153,19 @@ void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, const ch if(pthread_create(&fetch_thread, 0, fetch_in_thread, 0)) error("Thread creation failed"); if(pthread_create(&detect_thread, 0, detect_in_thread, 0)) error("Thread creation failed"); - show_image(disp, "Demo"); - int c = cvWaitKey(1); - if (c == 10){ - if(frame_skip == 0) frame_skip = 60; - else if(frame_skip == 4) frame_skip = 0; - else if(frame_skip == 60) frame_skip = 4; - else frame_skip = 0; + if(1){ + show_image(disp, "Demo"); + int c = cvWaitKey(1); + if (c == 10){ + if(frame_skip == 0) frame_skip = 60; + else if(frame_skip == 4) frame_skip = 0; + else if(frame_skip == 60) frame_skip = 4; + else frame_skip = 0; + } + }else{ + char buff[256]; + sprintf(buff, "/home/pjreddie/tmp/bag_%07d", count); + save_image(disp, buff); } pthread_join(fetch_thread, 0); diff --git a/src/detection_layer.c b/src/detection_layer.c index 8b4045af..1fe67677 100644 --- a/src/detection_layer.c +++ b/src/detection_layer.c @@ -22,6 +22,8 @@ detection_layer make_detection_layer(int batch, int inputs, int n, int side, int l.coords = coords; l.rescore = rescore; l.side = side; + l.w = side; + l.h = side; assert(side*side*((1 + l.coords)*l.n + l.classes) == inputs); l.cost = calloc(1, sizeof(float)); l.outputs = l.inputs; @@ -44,6 +46,7 @@ void forward_detection_layer(const detection_layer l, network_state state) int locations = l.side*l.side; int i,j; memcpy(l.output, state.input, l.outputs*l.batch*sizeof(float)); + //if(l.reorg) reorg(l.output, l.w*l.h, size*l.n, l.batch, 1); int b; if (l.softmax){ for(b = 0; b < l.batch; ++b){ @@ -204,6 +207,7 @@ void forward_detection_layer(const detection_layer l, network_state state) printf("Detection Avg IOU: %f, Pos Cat: %f, All Cat: %f, Pos Obj: %f, Any Obj: %f, count: %d\n", avg_iou/count, avg_cat/count, avg_allcat/(count*l.classes), avg_obj/count, avg_anyobj/(l.batch*locations*l.n), count); + //if(l.reorg) reorg(l.delta, l.w*l.h, size*l.n, l.batch, 0); } } diff --git a/src/detector.c b/src/detector.c index 64adaf37..becdd6c2 100644 --- a/src/detector.c +++ b/src/detector.c @@ -51,6 +51,11 @@ void train_detector(char *cfgfile, char *weightfile) args.d = &buffer; args.type = DETECTION_DATA; + args.angle = net.angle; + args.exposure = net.exposure; + args.saturation = net.saturation; + args.hue = net.hue; + pthread_t load_thread = load_data_in_thread(args); clock_t time; //while(i*imgs < N*120){ diff --git a/src/im2col.c b/src/im2col.c index 1ff45c8c..69ec98a9 100644 --- a/src/im2col.c +++ b/src/im2col.c @@ -18,13 +18,9 @@ void im2col_cpu(float* data_im, int ksize, int stride, int pad, float* data_col) { int c,h,w; - int height_col = (height - ksize) / stride + 1; - int width_col = (width - ksize) / stride + 1; - if (pad){ - height_col = 1 + (height-1) / stride; - width_col = 1 + (width-1) / stride; - pad = ksize/2; - } + int height_col = (height + 2*pad - ksize) / stride + 1; + int width_col = (width + 2*pad - ksize) / stride + 1; + int channels_col = channels * ksize * ksize; for (c = 0; c < channels_col; ++c) { int w_offset = c % ksize; diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index c2dd7802..d42d600b 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -33,8 +33,12 @@ __global__ void im2col_gpu_kernel(const int n, const float* data_im, for (int j = 0; j < ksize; ++j) { int h = h_in + i; int w = w_in + j; + *data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ? data_im_ptr[i * width + j] : 0; + + //*data_col_ptr = data_im_ptr[ii * width + jj]; + data_col_ptr += height_col * width_col; } } @@ -46,7 +50,6 @@ void im2col_ongpu(float *im, int ksize, int stride, int pad, float *data_col){ // We are going to launch channels * height_col * width_col kernels, each // kernel responsible for copying a single-channel grid. - pad = pad ? ksize/2 : 0; int height_col = (height + 2 * pad - ksize) / stride + 1; int width_col = (width + 2 * pad - ksize) / stride + 1; int num_kernels = channels * height_col * width_col; @@ -56,93 +59,3 @@ void im2col_ongpu(float *im, stride, height_col, width_col, data_col); } -/* - __global__ void im2col_pad_kernel(float *im, - int channels, int height, int width, - int ksize, int stride, float *data_col) - { - int c,h,w; - int height_col = 1 + (height-1) / stride; - int width_col = 1 + (width-1) / stride; - int channels_col = channels * ksize * ksize; - - int pad = ksize/2; - - int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; - int col_size = height_col*width_col*channels_col; - if (id >= col_size) return; - - int col_index = id; - w = id % width_col; - id /= width_col; - h = id % height_col; - id /= height_col; - c = id % channels_col; - id /= channels_col; - - int w_offset = c % ksize; - int h_offset = (c / ksize) % ksize; - int im_channel = c / ksize / ksize; - int im_row = h_offset + h * stride - pad; - int im_col = w_offset + w * stride - pad; - - int im_index = im_col + width*(im_row + height*im_channel); - float val = (im_row < 0 || im_col < 0 || im_row >= height || im_col >= width) ? 0 : im[im_index]; - - data_col[col_index] = val; - } - - __global__ void im2col_nopad_kernel(float *im, - int channels, int height, int width, - int ksize, int stride, float *data_col) - { - int c,h,w; - int height_col = (height - ksize) / stride + 1; - int width_col = (width - ksize) / stride + 1; - int channels_col = channels * ksize * ksize; - - int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; - int col_size = height_col*width_col*channels_col; - if (id >= col_size) return; - - int col_index = id; - w = id % width_col; - id /= width_col; - h = id % height_col; - id /= height_col; - c = id % channels_col; - id /= channels_col; - - int w_offset = c % ksize; - int h_offset = (c / ksize) % ksize; - int im_channel = c / ksize / ksize; - int im_row = h_offset + h * stride; - int im_col = w_offset + w * stride; - - int im_index = im_col + width*(im_row + height*im_channel); - float val = (im_row < 0 || im_col < 0 || im_row >= height || im_col >= width) ? 0 : im[im_index]; - - data_col[col_index] = val; - } - - extern "C" void im2col_ongpu(float *im, - int channels, int height, int width, -int ksize, int stride, int pad, float *data_col) -{ - - int height_col = (height - ksize) / stride + 1; - int width_col = (width - ksize) / stride + 1; - int channels_col = channels * ksize * ksize; - - if (pad){ - height_col = 1 + (height-1) / stride; - width_col = 1 + (width-1) / stride; - } - - size_t n = channels_col*height_col*width_col; - - if(pad)im2col_pad_kernel<<>>(im, channels, height, width, ksize, stride, data_col); - else im2col_nopad_kernel<<>>(im, channels, height, width, ksize, stride, data_col); - check_error(cudaPeekAtLastError()); -} -*/ diff --git a/src/image.c b/src/image.c index fe63b349..de896f22 100644 --- a/src/image.c +++ b/src/image.c @@ -1,6 +1,7 @@ #include "image.h" #include "utils.h" #include "blas.h" +#include "cuda.h" #include #include @@ -247,6 +248,26 @@ void constrain_image(image im) } void normalize_image(image p) +{ + int i; + float min = 9999999; + float max = -999999; + + for(i = 0; i < p.h*p.w*p.c; ++i){ + float v = p.data[i]; + if(v < min) min = v; + if(v > max) max = v; + } + if(max - min < .000000001){ + min = 0; + max = 1; + } + for(i = 0; i < p.c*p.w*p.h; ++i){ + p.data[i] = (p.data[i] - min)/(max-min); + } +} + +void normalize_image2(image p) { float *min = calloc(p.c, sizeof(float)); float *max = calloc(p.c, sizeof(float)); @@ -320,7 +341,6 @@ void show_image_cv(image p, const char *name) } free_image(copy); if(0){ - //if(disp->height < 448 || disp->width < 448 || disp->height > 1000){ int w = 448; int h = w*p.h/p.w; if(h > 1000){ @@ -334,202 +354,202 @@ void show_image_cv(image p, const char *name) } cvShowImage(buff, disp); cvReleaseImage(&disp); - } +} #endif - void show_image(image p, const char *name) - { +void show_image(image p, const char *name) +{ #ifdef OPENCV - show_image_cv(p, name); + show_image_cv(p, name); #else - fprintf(stderr, "Not compiled with OpenCV, saving to %s.png instead\n", name); - save_image(p, name); + fprintf(stderr, "Not compiled with OpenCV, saving to %s.png instead\n", name); + save_image(p, name); #endif - } +} #ifdef OPENCV - image get_image_from_stream(CvCapture *cap) - { - IplImage* src = cvQueryFrame(cap); - if (!src) return make_empty_image(0,0,0); - image im = ipl_to_image(src); - rgbgr_image(im); - return im; - } +image get_image_from_stream(CvCapture *cap) +{ + IplImage* src = cvQueryFrame(cap); + if (!src) return make_empty_image(0,0,0); + image im = ipl_to_image(src); + rgbgr_image(im); + return im; +} #endif #ifdef OPENCV - void save_image_jpg(image p, const char *name) - { - image copy = copy_image(p); - rgbgr_image(copy); - int x,y,k; +void save_image_jpg(image p, const char *name) +{ + image copy = copy_image(p); + if(p.c == 3) rgbgr_image(copy); + int x,y,k; - char buff[256]; - sprintf(buff, "%s.jpg", name); + char buff[256]; + sprintf(buff, "%s.jpg", name); - IplImage *disp = cvCreateImage(cvSize(p.w,p.h), IPL_DEPTH_8U, p.c); - int step = disp->widthStep; - for(y = 0; y < p.h; ++y){ - for(x = 0; x < p.w; ++x){ - for(k= 0; k < p.c; ++k){ - disp->imageData[y*step + x*p.c + k] = (unsigned char)(get_pixel(copy,x,y,k)*255); - } + IplImage *disp = cvCreateImage(cvSize(p.w,p.h), IPL_DEPTH_8U, p.c); + int step = disp->widthStep; + for(y = 0; y < p.h; ++y){ + for(x = 0; x < p.w; ++x){ + for(k= 0; k < p.c; ++k){ + disp->imageData[y*step + x*p.c + k] = (unsigned char)(get_pixel(copy,x,y,k)*255); } } - cvSaveImage(buff, disp,0); - cvReleaseImage(&disp); - free_image(copy); } + cvSaveImage(buff, disp,0); + cvReleaseImage(&disp); + free_image(copy); +} #endif - void save_image(image im, const char *name) - { - #ifdef OPENCV - save_image_jpg(im, name); - #else - char buff[256]; - //sprintf(buff, "%s (%d)", name, windows); - sprintf(buff, "%s.png", name); - unsigned char *data = calloc(im.w*im.h*im.c, sizeof(char)); - int i,k; - for(k = 0; k < im.c; ++k){ - for(i = 0; i < im.w*im.h; ++i){ - data[i*im.c+k] = (unsigned char) (255*im.data[i + k*im.w*im.h]); +void save_image(image im, const char *name) +{ +#ifdef OPENCV + save_image_jpg(im, name); +#else + char buff[256]; + //sprintf(buff, "%s (%d)", name, windows); + sprintf(buff, "%s.png", name); + unsigned char *data = calloc(im.w*im.h*im.c, sizeof(char)); + int i,k; + for(k = 0; k < im.c; ++k){ + for(i = 0; i < im.w*im.h; ++i){ + data[i*im.c+k] = (unsigned char) (255*im.data[i + k*im.w*im.h]); + } + } + int success = stbi_write_png(buff, im.w, im.h, im.c, data, im.w*im.c); + free(data); + if(!success) fprintf(stderr, "Failed to write image %s\n", buff); +#endif +} + + +void show_image_layers(image p, char *name) +{ + int i; + char buff[256]; + for(i = 0; i < p.c; ++i){ + sprintf(buff, "%s - Layer %d", name, i); + image layer = get_image_layer(p, i); + show_image(layer, buff); + free_image(layer); + } +} + +void show_image_collapsed(image p, char *name) +{ + image c = collapse_image_layers(p, 1); + show_image(c, name); + free_image(c); +} + +image make_empty_image(int w, int h, int c) +{ + image out; + out.data = 0; + out.h = h; + out.w = w; + out.c = c; + return out; +} + +image make_image(int w, int h, int c) +{ + image out = make_empty_image(w,h,c); + out.data = calloc(h*w*c, sizeof(float)); + return out; +} + +image make_random_image(int w, int h, int c) +{ + image out = make_empty_image(w,h,c); + out.data = calloc(h*w*c, sizeof(float)); + int i; + for(i = 0; i < w*h*c; ++i){ + out.data[i] = (rand_normal() * .25) + .5; + } + return out; +} + +image float_to_image(int w, int h, int c, float *data) +{ + image out = make_empty_image(w,h,c); + out.data = data; + return out; +} + +image rotate_crop_image(image im, float rad, float s, int w, int h, int dx, int dy) +{ + int x, y, c; + float cx = im.w/2.; + float cy = im.h/2.; + image rot = make_image(w, h, im.c); + for(c = 0; c < im.c; ++c){ + for(y = 0; y < h; ++y){ + for(x = 0; x < w; ++x){ + float rx = cos(rad)*(x/s + dx/s -cx) - sin(rad)*(y/s + dy/s -cy) + cx; + float ry = sin(rad)*(x/s + dx/s -cx) + cos(rad)*(y/s + dy/s -cy) + cy; + float val = bilinear_interpolate(im, rx, ry, c); + set_pixel(rot, x, y, c, val); } } - int success = stbi_write_png(buff, im.w, im.h, im.c, data, im.w*im.c); - free(data); - if(!success) fprintf(stderr, "Failed to write image %s\n", buff); - #endif } + return rot; +} - - void show_image_layers(image p, char *name) - { - int i; - char buff[256]; - for(i = 0; i < p.c; ++i){ - sprintf(buff, "%s - Layer %d", name, i); - image layer = get_image_layer(p, i); - show_image(layer, buff); - free_image(layer); +image rotate_image(image im, float rad) +{ + int x, y, c; + float cx = im.w/2.; + float cy = im.h/2.; + image rot = make_image(im.w, im.h, im.c); + for(c = 0; c < im.c; ++c){ + for(y = 0; y < im.h; ++y){ + for(x = 0; x < im.w; ++x){ + float rx = cos(rad)*(x-cx) - sin(rad)*(y-cy) + cx; + float ry = sin(rad)*(x-cx) + cos(rad)*(y-cy) + cy; + float val = bilinear_interpolate(im, rx, ry, c); + set_pixel(rot, x, y, c, val); + } } } + return rot; +} - void show_image_collapsed(image p, char *name) - { - image c = collapse_image_layers(p, 1); - show_image(c, name); - free_image(c); - } +void translate_image(image m, float s) +{ + int i; + for(i = 0; i < m.h*m.w*m.c; ++i) m.data[i] += s; +} - image make_empty_image(int w, int h, int c) - { - image out; - out.data = 0; - out.h = h; - out.w = w; - out.c = c; - return out; - } +void scale_image(image m, float s) +{ + int i; + for(i = 0; i < m.h*m.w*m.c; ++i) m.data[i] *= s; +} - image make_image(int w, int h, int c) - { - image out = make_empty_image(w,h,c); - out.data = calloc(h*w*c, sizeof(float)); - return out; - } - - image make_random_image(int w, int h, int c) - { - image out = make_empty_image(w,h,c); - out.data = calloc(h*w*c, sizeof(float)); - int i; - for(i = 0; i < w*h*c; ++i){ - out.data[i] = (rand_normal() * .25) + .5; - } - return out; - } - - image float_to_image(int w, int h, int c, float *data) - { - image out = make_empty_image(w,h,c); - out.data = data; - return out; - } - - image rotate_crop_image(image im, float rad, float s, int w, int h, int dx, int dy) - { - int x, y, c; - float cx = im.w/2.; - float cy = im.h/2.; - image rot = make_image(w, h, im.c); - for(c = 0; c < im.c; ++c){ - for(y = 0; y < h; ++y){ - for(x = 0; x < w; ++x){ - float rx = cos(rad)*(x/s + dx/s -cx) - sin(rad)*(y/s + dy/s -cy) + cx; - float ry = sin(rad)*(x/s + dx/s -cx) + cos(rad)*(y/s + dy/s -cy) + cy; - float val = bilinear_interpolate(im, rx, ry, c); - set_pixel(rot, x, y, c, val); +image crop_image(image im, int dx, int dy, int w, int h) +{ + image cropped = make_image(w, h, im.c); + int i, j, k; + for(k = 0; k < im.c; ++k){ + for(j = 0; j < h; ++j){ + for(i = 0; i < w; ++i){ + int r = j + dy; + int c = i + dx; + float val = 0; + r = constrain_int(r, 0, im.h-1); + c = constrain_int(c, 0, im.w-1); + if (r >= 0 && r < im.h && c >= 0 && c < im.w) { + val = get_pixel(im, c, r, k); } + set_pixel(cropped, i, j, k, val); } } - return rot; - } - - image rotate_image(image im, float rad) - { - int x, y, c; - float cx = im.w/2.; - float cy = im.h/2.; - image rot = make_image(im.w, im.h, im.c); - for(c = 0; c < im.c; ++c){ - for(y = 0; y < im.h; ++y){ - for(x = 0; x < im.w; ++x){ - float rx = cos(rad)*(x-cx) - sin(rad)*(y-cy) + cx; - float ry = sin(rad)*(x-cx) + cos(rad)*(y-cy) + cy; - float val = bilinear_interpolate(im, rx, ry, c); - set_pixel(rot, x, y, c, val); - } - } - } - return rot; - } - - void translate_image(image m, float s) - { - int i; - for(i = 0; i < m.h*m.w*m.c; ++i) m.data[i] += s; - } - - void scale_image(image m, float s) - { - int i; - for(i = 0; i < m.h*m.w*m.c; ++i) m.data[i] *= s; - } - - image crop_image(image im, int dx, int dy, int w, int h) - { - image cropped = make_image(w, h, im.c); - int i, j, k; - for(k = 0; k < im.c; ++k){ - for(j = 0; j < h; ++j){ - for(i = 0; i < w; ++i){ - int r = j + dy; - int c = i + dx; - float val = 0; - r = constrain_int(r, 0, im.h-1); - c = constrain_int(c, 0, im.w-1); - if (r >= 0 && r < im.h && c >= 0 && c < im.w) { - val = get_pixel(im, c, r, k); - } - set_pixel(cropped, i, j, k, val); - } - } - } - return cropped; } + return cropped; +} int best_3d_shift_r(image a, image b, int min, int max) { @@ -666,7 +686,7 @@ void rgb_to_hsv(image im) v = max; if(max == 0){ s = 0; - h = -1; + h = 0; }else{ s = delta/max; if(r == max){ @@ -677,6 +697,7 @@ void rgb_to_hsv(image im) h = 4 + (r - g) / delta; } if (h < 0) h += 6; + h = h/6.; } set_pixel(im, i, j, 0, h); set_pixel(im, i, j, 1, s); @@ -694,7 +715,7 @@ void hsv_to_rgb(image im) float f, p, q, t; for(j = 0; j < im.h; ++j){ for(i = 0; i < im.w; ++i){ - h = get_pixel(im, i , j, 0); + h = 6 * get_pixel(im, i , j, 0); s = get_pixel(im, i , j, 1); v = get_pixel(im, i , j, 2); if (s == 0) { @@ -781,6 +802,18 @@ void scale_image_channel(image im, int c, float v) } } +void translate_image_channel(image im, int c, float v) +{ + int i, j; + for(j = 0; j < im.h; ++j){ + for(i = 0; i < im.w; ++i){ + float pix = get_pixel(im, i, j, c); + pix = pix+v; + set_pixel(im, i, j, c, pix); + } + } +} + image binarize_image(image im) { image c = copy_image(im); @@ -800,6 +833,19 @@ void saturate_image(image im, float sat) constrain_image(im); } +void hue_image(image im, float hue) +{ + rgb_to_hsv(im); + int i; + for(i = 0; i < im.w*im.h; ++i){ + im.data[i] = im.data[i] + hue; + if (im.data[i] > 1) im.data[i] -= 1; + if (im.data[i] < 0) im.data[i] += 1; + } + hsv_to_rgb(im); + constrain_image(im); +} + void exposure_image(image im, float sat) { rgb_to_hsv(im); @@ -808,6 +854,29 @@ void exposure_image(image im, float sat) constrain_image(im); } +void distort_image(image im, float hue, float sat, float val) +{ + rgb_to_hsv(im); + scale_image_channel(im, 1, sat); + scale_image_channel(im, 2, val); + int i; + for(i = 0; i < im.w*im.h; ++i){ + im.data[i] = im.data[i] + hue; + if (im.data[i] > 1) im.data[i] -= 1; + if (im.data[i] < 0) im.data[i] += 1; + } + hsv_to_rgb(im); + constrain_image(im); +} + +void random_distort_image(image im, float hue, float saturation, float exposure) +{ + float dhue = rand_uniform(-hue, hue); + float dsat = rand_scale(saturation); + float dexp = rand_scale(exposure); + distort_image(im, dhue, dsat, dexp); +} + void saturate_exposure_image(image im, float sat, float exposure) { rgb_to_hsv(im); @@ -876,7 +945,6 @@ image resize_image(image im, int w, int h) return resized; } -#include "cuda.h" void test_resize(char *filename) { @@ -885,59 +953,40 @@ void test_resize(char *filename) printf("L2 Norm: %f\n", mag); image gray = grayscale_image(im); - image sat2 = copy_image(im); - saturate_image(sat2, 2); + image c1 = copy_image(im); + image c2 = copy_image(im); + image c3 = copy_image(im); + image c4 = copy_image(im); + distort_image(c1, .1, 1.5, 1.5); + distort_image(c2, -.1, .66666, .66666); + distort_image(c3, .1, 1.5, .66666); + distort_image(c4, .1, .66666, 1.5); - image sat5 = copy_image(im); - saturate_image(sat5, .5); - image exp2 = copy_image(im); - exposure_image(exp2, 2); - - image exp5 = copy_image(im); - exposure_image(exp5, .5); - - image bin = binarize_image(im); - -/* -#ifdef GPU - image r = resize_image(im, im.w, im.h); - image black = make_image(im.w*2 + 3, im.h*2 + 3, 9); - image black2 = make_image(im.w, im.h, 3); - - float *r_gpu = cuda_make_array(r.data, r.w*r.h*r.c); - float *black_gpu = cuda_make_array(black.data, black.w*black.h*black.c); - float *black2_gpu = cuda_make_array(black2.data, black2.w*black2.h*black2.c); - shortcut_gpu(3, r.w, r.h, 1, r_gpu, black.w, black.h, 3, black_gpu); - //flip_image(r); - //shortcut_gpu(3, r.w, r.h, 1, r.data, black.w, black.h, 3, black.data); - - shortcut_gpu(3, black.w, black.h, 3, black_gpu, black2.w, black2.h, 1, black2_gpu); - cuda_pull_array(black_gpu, black.data, black.w*black.h*black.c); - cuda_pull_array(black2_gpu, black2.data, black2.w*black2.h*black2.c); - show_image_layers(black, "Black"); - show_image(black2, "Recreate"); -#endif -*/ - image rot = rotate_crop_image(im, -.2618, 1, im.w/2, im.h/2, 0, 0); - image rot3 = rotate_crop_image(im, -.2618, 2, im.w, im.h, im.w/2, 0); - image rot2 = rotate_crop_image(im, -.2618, 1, im.w, im.h, 0, 0); - show_image(rot, "Rotated"); - show_image(rot2, "base"); - - show_image(rot3, "Rotated2"); - -/* show_image(im, "Original"); - show_image(bin, "Binary"); show_image(gray, "Gray"); - show_image(sat2, "Saturation-2"); - show_image(sat5, "Saturation-.5"); - show_image(exp2, "Exposure-2"); - show_image(exp5, "Exposure-.5"); - */ + show_image(c1, "C1"); + show_image(c2, "C2"); + show_image(c3, "C3"); + show_image(c4, "C4"); #ifdef OPENCV - cvWaitKey(0); + while(1){ + float exposure = 1.15; + float saturation = 1.15; + float hue = .05; + + image c = copy_image(im); + + float dexp = rand_scale(exposure); + float dsat = rand_scale(saturation); + float dhue = rand_uniform(-hue, hue); + + distort_image(c, dhue, dsat, dexp); + show_image(c, "rand"); + printf("%f %f %f\n", dhue, dsat, dexp); + free_image(c); + cvWaitKey(0); + } #endif } @@ -1180,10 +1229,8 @@ void show_images(image *ims, int n, char *window) image sized = resize_image(m, w, h); */ normalize_image(m); - image sized = resize_image(m, m.w, m.h); - save_image(sized, window); - show_image(sized, window); - free_image(sized); + save_image(m, window); + show_image(m, window); free_image(m); } diff --git a/src/image.h b/src/image.h index 9c53fd3f..213c4b45 100644 --- a/src/image.h +++ b/src/image.h @@ -32,6 +32,7 @@ void scale_image(image m, float s); image crop_image(image im, int dx, int dy, int w, int h); image random_crop_image(image im, int w, int h); image random_augment_image(image im, float angle, int low, int high, int size); +void random_distort_image(image im, float hue, float saturation, float exposure); image resize_image(image im, int w, int h); image resize_min(image im, int min); void translate_image(image m, float s); @@ -41,6 +42,7 @@ void rotate_image_cw(image im, int times); void embed_image(image source, image dest, int dx, int dy); void saturate_image(image im, float sat); void exposure_image(image im, float sat); +void distort_image(image im, float hue, float sat, float val); void saturate_exposure_image(image im, float sat, float exposure); void hsv_to_rgb(image im); void rgbgr_image(image im); diff --git a/src/imagenet.c b/src/imagenet.c deleted file mode 100644 index 16255263..00000000 --- a/src/imagenet.c +++ /dev/null @@ -1,237 +0,0 @@ -#include "network.h" -#include "utils.h" -#include "parser.h" - -#ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#endif - -void train_imagenet(char *cfgfile, char *weightfile) -{ - data_seed = time(0); - srand(time(0)); - float avg_loss = -1; - char *base = basecfg(cfgfile); - char *backup_directory = "/home/pjreddie/backup/"; - printf("%s\n", base); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - int imgs = 1024; - char **labels = get_labels("data/inet.labels.list"); - list *plist = get_paths("data/inet.train.list"); - char **paths = (char **)list_to_array(plist); - printf("%d\n", plist->size); - int N = plist->size; - clock_t time; - pthread_t load_thread; - data train; - data buffer; - - load_args args = {0}; - args.w = net.w; - args.h = net.h; - args.paths = paths; - args.classes = 1000; - args.n = imgs; - args.m = N; - args.labels = labels; - args.d = &buffer; - args.type = OLD_CLASSIFICATION_DATA; - - load_thread = load_data_in_thread(args); - int epoch = (*net.seen)/N; - while(get_current_batch(net) < net.max_batches || net.max_batches == 0){ - time=clock(); - pthread_join(load_thread, 0); - train = buffer; - - load_thread = load_data_in_thread(args); - printf("Loaded: %lf seconds\n", sec(clock()-time)); - time=clock(); - float loss = train_network(net, train); - if(avg_loss == -1) avg_loss = loss; - avg_loss = avg_loss*.9 + loss*.1; - printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); - free_data(train); - if(*net.seen/N > epoch){ - epoch = *net.seen/N; - char buff[256]; - sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch); - save_weights(net, buff); - } - if(*net.seen%1000 == 0){ - char buff[256]; - sprintf(buff, "%s/%s.backup",backup_directory,base); - save_weights(net, buff); - } - } - char buff[256]; - sprintf(buff, "%s/%s.weights", backup_directory, base); - save_weights(net, buff); - - pthread_join(load_thread, 0); - free_data(buffer); - free_network(net); - free_ptrs((void**)labels, 1000); - free_ptrs((void**)paths, plist->size); - free_list(plist); - free(base); -} - -void validate_imagenet(char *filename, char *weightfile) -{ - int i = 0; - network net = parse_network_cfg(filename); - if(weightfile){ - load_weights(&net, weightfile); - } - srand(time(0)); - - char **labels = get_labels("data/inet.labels.list"); - //list *plist = get_paths("data/inet.suppress.list"); - list *plist = get_paths("data/inet.val.list"); - - char **paths = (char **)list_to_array(plist); - int m = plist->size; - free_list(plist); - - clock_t time; - float avg_acc = 0; - float avg_top5 = 0; - int splits = 50; - int num = (i+1)*m/splits - i*m/splits; - - data val, buffer; - - load_args args = {0}; - args.w = net.w; - args.h = net.h; - args.paths = paths; - args.classes = 1000; - args.n = num; - args.m = 0; - args.labels = labels; - args.d = &buffer; - args.type = OLD_CLASSIFICATION_DATA; - - pthread_t load_thread = load_data_in_thread(args); - for(i = 1; i <= splits; ++i){ - time=clock(); - - pthread_join(load_thread, 0); - val = buffer; - - num = (i+1)*m/splits - i*m/splits; - char **part = paths+(i*m/splits); - if(i != splits){ - args.paths = part; - load_thread = load_data_in_thread(args); - } - printf("Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock()-time)); - - time=clock(); - float *acc = network_accuracies(net, val, 5); - avg_acc += acc[0]; - avg_top5 += acc[1]; - printf("%d: top1: %f, top5: %f, %lf seconds, %d images\n", i, avg_acc/i, avg_top5/i, sec(clock()-time), val.X.rows); - free_data(val); - } -} - -void test_imagenet(char *cfgfile, char *weightfile, char *filename) -{ - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - srand(2222222); - int i = 0; - char **names = get_labels("data/shortnames.txt"); - clock_t time; - int indexes[10]; - char buff[256]; - char *input = buff; - while(1){ - if(filename){ - strncpy(input, filename, 256); - }else{ - printf("Enter Image Path: "); - fflush(stdout); - input = fgets(input, 256, stdin); - if(!input) return; - strtok(input, "\n"); - } - image im = load_image_color(input, 256, 256); - float *X = im.data; - time=clock(); - float *predictions = network_predict(net, X); - top_predictions(net, 10, indexes); - printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); - for(i = 0; i < 10; ++i){ - int index = indexes[i]; - printf("%s: %f\n", names[index], predictions[index]); - } - free_image(im); - if (filename) break; - } -} - -void run_imagenet(int argc, char **argv) -{ - if(argc < 4){ - fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); - return; - } - - char *cfg = argv[3]; - char *weights = (argc > 4) ? argv[4] : 0; - char *filename = (argc > 5) ? argv[5]: 0; - if(0==strcmp(argv[2], "test")) test_imagenet(cfg, weights, filename); - else if(0==strcmp(argv[2], "train")) train_imagenet(cfg, weights); - else if(0==strcmp(argv[2], "valid")) validate_imagenet(cfg, weights); -} - -/* - void train_imagenet_distributed(char *address) - { - float avg_loss = 1; - srand(time(0)); - network net = parse_network_cfg("cfg/net.cfg"); - set_learning_network(&net, 0, 1, 0); - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - int imgs = net.batch; - int i = 0; - char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list"); - list *plist = get_paths("/data/imagenet/cls.train.list"); - char **paths = (char **)list_to_array(plist); - printf("%d\n", plist->size); - clock_t time; - data train, buffer; - pthread_t load_thread = load_data_thread(paths, imgs, plist->size, labels, 1000, 224, 224, &buffer); - while(1){ - i += 1; - - time=clock(); - client_update(net, address); - printf("Updated: %lf seconds\n", sec(clock()-time)); - - time=clock(); - pthread_join(load_thread, 0); - train = buffer; - normalize_data_rows(train); - load_thread = load_data_thread(paths, imgs, plist->size, labels, 1000, 224, 224, &buffer); - printf("Loaded: %lf seconds\n", sec(clock()-time)); - time=clock(); - - float loss = train_network(net, train); - avg_loss = avg_loss*.9 + loss*.1; - printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), i*imgs); - free_data(train); - } - } - */ - diff --git a/src/layer.h b/src/layer.h index 10d64e50..5e9dc3a9 100644 --- a/src/layer.h +++ b/src/layer.h @@ -72,6 +72,7 @@ struct layer{ float saturation; float exposure; float shift; + float ratio; int softmax; int classes; int coords; @@ -82,6 +83,7 @@ struct layer{ int joint; int noadjust; int reorg; + int log; float alpha; float beta; diff --git a/src/maxpool_layer.c b/src/maxpool_layer.c index 7af49546..3e0ea156 100644 --- a/src/maxpool_layer.c +++ b/src/maxpool_layer.c @@ -18,7 +18,7 @@ image get_maxpool_delta(maxpool_layer l) return float_to_image(w,h,c,l.delta); } -maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int stride) +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}; @@ -27,8 +27,9 @@ maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int s l.h = h; l.w = w; l.c = c; - l.out_w = (w-1)/stride + 1; - l.out_h = (h-1)/stride + 1; + l.pad = padding; + l.out_w = (w + 2*padding - size + 1)/stride + 1; + l.out_h = (h + 2*padding - size + 1)/stride + 1; l.out_c = c; l.outputs = l.out_h * l.out_w * l.out_c; l.inputs = h*w*c; @@ -48,13 +49,12 @@ maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int s void resize_maxpool_layer(maxpool_layer *l, int w, int h) { - int stride = l->stride; l->h = h; l->w = w; l->inputs = h*w*l->c; - l->out_w = (w-1)/stride + 1; - l->out_h = (h-1)/stride + 1; + l->out_w = (w + 2*l->pad - l->size + 1)/l->stride + 1; + l->out_h = (h + 2*l->pad - l->size + 1)/l->stride + 1; l->outputs = l->out_w * l->out_h * l->c; int output_size = l->outputs * l->batch; @@ -75,11 +75,11 @@ void resize_maxpool_layer(maxpool_layer *l, int w, int h) void forward_maxpool_layer(const maxpool_layer l, network_state state) { int b,i,j,k,m,n; - int w_offset = (-l.size-1)/2 + 1; - int h_offset = (-l.size-1)/2 + 1; + int w_offset = -l.pad; + int h_offset = -l.pad; - int h = (l.h-1)/l.stride + 1; - int w = (l.w-1)/l.stride + 1; + int h = l.out_h; + int w = l.out_w; int c = l.c; for(b = 0; b < l.batch; ++b){ @@ -112,8 +112,8 @@ void forward_maxpool_layer(const maxpool_layer l, network_state state) void backward_maxpool_layer(const maxpool_layer l, network_state state) { int i; - int h = (l.h-1)/l.stride + 1; - int w = (l.w-1)/l.stride + 1; + int h = l.out_h; + int w = l.out_w; int c = l.c; for(i = 0; i < h*w*c*l.batch; ++i){ int index = l.indexes[i]; diff --git a/src/maxpool_layer.h b/src/maxpool_layer.h index 4729e43a..ce56dd88 100644 --- a/src/maxpool_layer.h +++ b/src/maxpool_layer.h @@ -9,7 +9,7 @@ typedef layer maxpool_layer; image get_maxpool_image(maxpool_layer l); -maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int stride); +maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int stride, int padding); void resize_maxpool_layer(maxpool_layer *l, int w, int h); void forward_maxpool_layer(const maxpool_layer l, network_state state); void backward_maxpool_layer(const maxpool_layer l, network_state state); diff --git a/src/maxpool_layer_kernels.cu b/src/maxpool_layer_kernels.cu index 331a5e2c..fc54f527 100644 --- a/src/maxpool_layer_kernels.cu +++ b/src/maxpool_layer_kernels.cu @@ -7,10 +7,10 @@ extern "C" { #include "cuda.h" } -__global__ void forward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c, int stride, int size, float *input, float *output, int *indexes) +__global__ void forward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c, int stride, int size, int pad, float *input, float *output, int *indexes) { - int h = (in_h-1)/stride + 1; - int w = (in_w-1)/stride + 1; + int h = (in_h + 2*pad - size + 1)/stride + 1; + int w = (in_w + 2*pad - size + 1)/stride + 1; int c = in_c; int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; @@ -24,8 +24,8 @@ __global__ void forward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c id /= c; int b = id; - int w_offset = (-size-1)/2 + 1; - int h_offset = (-size-1)/2 + 1; + int w_offset = -pad; + int h_offset = -pad; int out_index = j + w*(i + h*(k + c*b)); float max = -INFINITY; @@ -47,10 +47,10 @@ __global__ void forward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c indexes[out_index] = max_i; } -__global__ void backward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c, int stride, int size, float *delta, float *prev_delta, int *indexes) +__global__ void backward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c, int stride, int size, int pad, float *delta, float *prev_delta, int *indexes) { - int h = (in_h-1)/stride + 1; - int w = (in_w-1)/stride + 1; + int h = (in_h + 2*pad - size + 1)/stride + 1; + int w = (in_w + 2*pad - size + 1)/stride + 1; int c = in_c; int area = (size-1)/stride; @@ -66,8 +66,8 @@ __global__ void backward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_ id /= in_c; int b = id; - int w_offset = (-size-1)/2 + 1; - int h_offset = (-size-1)/2 + 1; + int w_offset = -pad; + int h_offset = -pad; float d = 0; int l, m; @@ -86,13 +86,13 @@ __global__ void backward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_ extern "C" void forward_maxpool_layer_gpu(maxpool_layer layer, network_state state) { - int h = (layer.h-1)/layer.stride + 1; - int w = (layer.w-1)/layer.stride + 1; + int h = layer.out_h; + int w = layer.out_w; int c = layer.c; size_t n = h*w*c*layer.batch; - forward_maxpool_layer_kernel<<>>(n, layer.h, layer.w, layer.c, layer.stride, layer.size, state.input, layer.output_gpu, layer.indexes_gpu); + forward_maxpool_layer_kernel<<>>(n, layer.h, layer.w, layer.c, layer.stride, layer.size, layer.pad, state.input, layer.output_gpu, layer.indexes_gpu); check_error(cudaPeekAtLastError()); } @@ -100,7 +100,7 @@ extern "C" void backward_maxpool_layer_gpu(maxpool_layer layer, network_state st { size_t n = layer.h*layer.w*layer.c*layer.batch; - backward_maxpool_layer_kernel<<>>(n, layer.h, layer.w, layer.c, layer.stride, layer.size, layer.delta_gpu, state.delta, layer.indexes_gpu); + backward_maxpool_layer_kernel<<>>(n, layer.h, layer.w, layer.c, layer.stride, layer.size, layer.pad, layer.delta_gpu, state.delta, layer.indexes_gpu); check_error(cudaPeekAtLastError()); } diff --git a/src/network.c b/src/network.c index 91baafe4..c9a198f5 100644 --- a/src/network.c +++ b/src/network.c @@ -420,7 +420,7 @@ int resize_network(network *net, int w, int h) net->h = h; int inputs = 0; size_t workspace_size = 0; - //fprintf(stderr, "Resizing to %d x %d...", w, h); + //fprintf(stderr, "Resizing to %d x %d...\n", w, h); //fflush(stderr); for (i = 0; i < net->n; ++i){ layer l = net->layers[i]; diff --git a/src/network.h b/src/network.h index 3306962e..b7ba39cb 100644 --- a/src/network.h +++ b/src/network.h @@ -43,6 +43,7 @@ typedef struct network{ float angle; float exposure; float saturation; + float hue; int gpu_index; diff --git a/src/parser.c b/src/parser.c index 904df1a0..626f510c 100644 --- a/src/parser.c +++ b/src/parser.c @@ -2,6 +2,7 @@ #include #include +#include "blas.h" #include "parser.h" #include "assert.h" #include "activations.h" @@ -147,7 +148,10 @@ convolutional_layer parse_convolutional(list *options, size_params params) int n = option_find_int(options, "filters",1); int size = option_find_int(options, "size",1); int stride = option_find_int(options, "stride",1); - int pad = option_find_int(options, "pad",0); + int pad = option_find_int_quiet(options, "pad",0); + int padding = option_find_int_quiet(options, "padding",0); + if(pad) padding = size/2; + char *activation_s = option_find_str(options, "activation", "logistic"); ACTIVATION activation = get_activation(activation_s); @@ -161,7 +165,7 @@ convolutional_layer parse_convolutional(list *options, size_params params) int binary = option_find_int_quiet(options, "binary", 0); int xnor = option_find_int_quiet(options, "xnor", 0); - convolutional_layer layer = make_convolutional_layer(batch,h,w,c,n,size,stride,pad,activation, batch_normalize, binary, xnor); + convolutional_layer layer = make_convolutional_layer(batch,h,w,c,n,size,stride,padding,activation, batch_normalize, binary, xnor); layer.flipped = option_find_int_quiet(options, "flipped", 0); layer.dot = option_find_float_quiet(options, "dot", 0); @@ -234,9 +238,16 @@ layer parse_region(list *options, size_params params) int coords = option_find_int(options, "coords", 4); int classes = option_find_int(options, "classes", 20); int num = option_find_int(options, "num", 1); + + params.w = option_find_int(options, "side", params.w); + params.h = option_find_int(options, "side", params.h); + layer l = make_region_layer(params.batch, params.w, params.h, num, classes, coords); assert(l.outputs == params.inputs); + l.log = option_find_int_quiet(options, "log", 0); + l.sqrt = option_find_int_quiet(options, "sqrt", 0); + l.softmax = option_find_int(options, "softmax", 0); l.max_boxes = option_find_int_quiet(options, "max",30); l.jitter = option_find_float(options, "jitter", .2); @@ -278,6 +289,7 @@ cost_layer parse_cost(list *options, size_params params) COST_TYPE type = get_cost_type(type_s); float scale = option_find_float_quiet(options, "scale",1); cost_layer layer = make_cost_layer(params.batch, params.inputs, type, scale); + layer.ratio = option_find_float_quiet(options, "ratio",0); return layer; } @@ -324,6 +336,7 @@ maxpool_layer parse_maxpool(list *options, size_params params) { int stride = option_find_int(options, "stride",1); int size = option_find_int(options, "size",stride); + int padding = option_find_int_quiet(options, "padding", (size-1)/2); int batch,h,w,c; h = params.h; @@ -332,7 +345,7 @@ maxpool_layer parse_maxpool(list *options, size_params params) batch=params.batch; if(!(h && w && c)) error("Layer before maxpool layer must output image."); - maxpool_layer layer = make_maxpool_layer(batch,h,w,c,size,stride); + maxpool_layer layer = make_maxpool_layer(batch,h,w,c,size,stride,padding); return layer; } @@ -486,6 +499,7 @@ void parse_net_options(list *options, network *net) net->angle = option_find_float_quiet(options, "angle", 0); net->saturation = option_find_float_quiet(options, "saturation", 1); net->exposure = option_find_float_quiet(options, "exposure", 1); + net->hue = option_find_float_quiet(options, "hue", 0); if(!net->inputs && !(net->h && net->w && net->c)) error("No input parameters supplied"); @@ -1085,6 +1099,7 @@ void load_convolutional_weights(layer l, FILE *fp) fread(l.rolling_variance, sizeof(float), l.n, fp); } fread(l.filters, sizeof(float), num, fp); + //if(l.c == 3) scal_cpu(num, 1./256, l.filters, 1); if (l.flipped) { transpose_matrix(l.filters, l.c*l.size*l.size, l.n); } diff --git a/src/region_layer.c b/src/region_layer.c index 5fe37c5f..2185ea59 100644 --- a/src/region_layer.c +++ b/src/region_layer.c @@ -80,8 +80,8 @@ box get_region_box(float *x, int index, int i, int j, int w, int h, int adjust, b.w = logistic_activate(x[index + 2]); b.h = logistic_activate(x[index + 3]); } - if(adjust && b.w < .01) b.w = .01; - if(adjust && b.h < .01) b.h = .01; + //if(adjust && b.w < .01) b.w = .01; + //if(adjust && b.h < .01) b.h = .01; return b; } @@ -149,6 +149,7 @@ void forward_region_layer(const region_layer l, network_state state) l.delta[index + 4] = l.noobject_scale * ((0 - l.output[index + 4]) * logistic_gradient(l.output[index + 4])); if(best_iou > .5) l.delta[index + 4] = 0; + /* if(*(state.net.seen) < 6400){ box truth = {0}; truth.x = (i + .5)/l.w; @@ -157,6 +158,7 @@ void forward_region_layer(const region_layer l, network_state state) truth.h = .5; delta_region_box(truth, l.output, index, i, j, l.w, l.h, l.delta, LOG, 1); } + */ } } } diff --git a/src/tag.c b/src/tag.c index e3e17070..dd105917 100644 --- a/src/tag.c +++ b/src/tag.c @@ -45,6 +45,11 @@ void train_tag(char *cfgfile, char *weightfile, int clear) args.d = &buffer; args.type = TAG_DATA; + args.angle = net.angle; + args.exposure = net.exposure; + args.saturation = net.saturation; + args.hue = net.hue; + fprintf(stderr, "%d classes\n", net.outputs); load_thread = load_data_in_thread(args); diff --git a/src/utils.c b/src/utils.c index 41893fc7..a6cb4216 100644 --- a/src/utils.c +++ b/src/utils.c @@ -585,6 +585,13 @@ float rand_uniform(float min, float max) return ((float)rand()/RAND_MAX * (max - min)) + min; } +float rand_scale(float s) +{ + float scale = rand_uniform(1, s); + if(rand()%2) return scale; + return 1./scale; +} + float **one_hot_encode(float *a, int n, int k) { int i; diff --git a/src/utils.h b/src/utils.h index ae0cba25..3f0233ab 100644 --- a/src/utils.h +++ b/src/utils.h @@ -42,6 +42,7 @@ float mse_array(float *a, int n); float rand_normal(); size_t rand_size_t(); float rand_uniform(float min, float max); +float rand_scale(float s); int rand_int(int min, int max); float sum_array(float *a, int n); float mean_array(float *a, int n); diff --git a/src/voxel.c b/src/voxel.c index b41cf77d..a047c6a3 100644 --- a/src/voxel.c +++ b/src/voxel.c @@ -9,37 +9,37 @@ void extract_voxel(char *lfile, char *rfile, char *prefix) { +#ifdef OPENCV int w = 1920; int h = 1080; -#ifdef OPENCV - int shift = 0; - int count = 0; - CvCapture *lcap = cvCaptureFromFile(lfile); - CvCapture *rcap = cvCaptureFromFile(rfile); - while(1){ - image l = get_image_from_stream(lcap); - image r = get_image_from_stream(rcap); - if(!l.w || !r.w) break; - if(count%100 == 0) { - shift = best_3d_shift_r(l, r, -l.h/100, l.h/100); - printf("%d\n", shift); - } - image ls = crop_image(l, (l.w - w)/2, (l.h - h)/2, w, h); - image rs = crop_image(r, 105 + (r.w - w)/2, (r.h - h)/2 + shift, w, h); - char buff[256]; - sprintf(buff, "%s_%05d_l", prefix, count); - save_image(ls, buff); - sprintf(buff, "%s_%05d_r", prefix, count); - save_image(rs, buff); - free_image(l); - free_image(r); - free_image(ls); - free_image(rs); - ++count; + int shift = 0; + int count = 0; + CvCapture *lcap = cvCaptureFromFile(lfile); + CvCapture *rcap = cvCaptureFromFile(rfile); + while(1){ + image l = get_image_from_stream(lcap); + image r = get_image_from_stream(rcap); + if(!l.w || !r.w) break; + if(count%100 == 0) { + shift = best_3d_shift_r(l, r, -l.h/100, l.h/100); + printf("%d\n", shift); } + image ls = crop_image(l, (l.w - w)/2, (l.h - h)/2, w, h); + image rs = crop_image(r, 105 + (r.w - w)/2, (r.h - h)/2 + shift, w, h); + char buff[256]; + sprintf(buff, "%s_%05d_l", prefix, count); + save_image(ls, buff); + sprintf(buff, "%s_%05d_r", prefix, count); + save_image(rs, buff); + free_image(l); + free_image(r); + free_image(ls); + free_image(rs); + ++count; + } #else -printf("need OpenCV for extraction\n"); + printf("need OpenCV for extraction\n"); #endif } @@ -164,6 +164,6 @@ void run_voxel(int argc, char **argv) else if(0==strcmp(argv[2], "test")) test_voxel(cfg, weights, filename); else if(0==strcmp(argv[2], "extract")) extract_voxel(argv[3], argv[4], argv[5]); /* - else if(0==strcmp(argv[2], "valid")) validate_voxel(cfg, weights); - */ + else if(0==strcmp(argv[2], "valid")) validate_voxel(cfg, weights); + */ } diff --git a/src/yolo.c b/src/yolo.c index 62455f2e..fefa7635 100644 --- a/src/yolo.c +++ b/src/yolo.c @@ -54,6 +54,11 @@ void train_yolo(char *cfgfile, char *weightfile) args.d = &buffer; args.type = REGION_DATA; + args.angle = net.angle; + args.exposure = net.exposure; + args.saturation = net.saturation; + args.hue = net.hue; + pthread_t load_thread = load_data_in_thread(args); clock_t time; //while(i*imgs < N*120){