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This commit is contained in:
Joseph Redmon
parent
75fe603722
commit
b3c4fc9f22
134
cfg/yolo-tiny.cfg
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134
cfg/yolo-tiny.cfg
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@ -0,0 +1,134 @@
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[net]
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batch=64
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subdivisions=8
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width=416
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height=416
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channels=3
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momentum=0.9
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decay=0.0005
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angle=0
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saturation = 1.5
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exposure = 1.5
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hue=.1
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learning_rate=0.001
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max_batches = 120000
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policy=steps
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steps=-1,100,80000,100000
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scales=.1,10,.1,.1
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[convolutional]
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batch_normalize=1
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filters=16
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size=3
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stride=1
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pad=1
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activation=leaky
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[maxpool]
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size=2
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stride=2
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[convolutional]
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batch_normalize=1
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filters=32
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size=3
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stride=1
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pad=1
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activation=leaky
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[maxpool]
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size=2
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stride=2
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[convolutional]
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batch_normalize=1
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filters=64
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size=3
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stride=1
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pad=1
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activation=leaky
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[maxpool]
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size=2
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stride=2
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[convolutional]
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batch_normalize=1
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filters=128
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size=3
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stride=1
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pad=1
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activation=leaky
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[maxpool]
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size=2
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stride=2
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[convolutional]
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batch_normalize=1
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filters=256
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size=3
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stride=1
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pad=1
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activation=leaky
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[maxpool]
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size=2
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stride=2
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[convolutional]
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batch_normalize=1
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filters=512
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size=3
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stride=1
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pad=1
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activation=leaky
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[maxpool]
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size=2
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stride=1
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[convolutional]
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batch_normalize=1
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filters=1024
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size=3
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stride=1
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pad=1
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activation=leaky
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###########
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[convolutional]
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batch_normalize=1
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size=3
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stride=1
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pad=1
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filters=1024
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activation=leaky
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[convolutional]
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size=1
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stride=1
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pad=1
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filters=425
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activation=linear
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[region]
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anchors = 0.738768,0.874946, 2.42204,2.65704, 4.30971,7.04493, 10.246,4.59428, 12.6868,11.8741
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bias_match=1
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classes=80
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coords=4
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num=5
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softmax=1
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jitter=.2
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rescore=1
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object_scale=5
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noobject_scale=1
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class_scale=1
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coord_scale=1
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absolute=1
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thresh = .6
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random=1
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134
cfg/yolo-tiny_voc.cfg
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134
cfg/yolo-tiny_voc.cfg
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@ -0,0 +1,134 @@
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[net]
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batch=64
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subdivisions=8
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width=416
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height=416
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channels=3
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momentum=0.9
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decay=0.0005
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angle=0
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saturation = 1.5
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exposure = 1.5
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hue=.1
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learning_rate=0.001
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max_batches = 40100
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policy=steps
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steps=-1,100,20000,30000
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scales=.1,10,.1,.1
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[convolutional]
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batch_normalize=1
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filters=16
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size=3
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stride=1
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pad=1
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activation=leaky
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[maxpool]
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size=2
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stride=2
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[convolutional]
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batch_normalize=1
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filters=32
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size=3
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stride=1
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pad=1
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activation=leaky
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[maxpool]
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size=2
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stride=2
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[convolutional]
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batch_normalize=1
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filters=64
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size=3
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stride=1
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pad=1
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activation=leaky
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[maxpool]
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size=2
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stride=2
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[convolutional]
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batch_normalize=1
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filters=128
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size=3
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stride=1
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pad=1
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activation=leaky
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[maxpool]
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size=2
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stride=2
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[convolutional]
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batch_normalize=1
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filters=256
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size=3
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stride=1
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pad=1
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activation=leaky
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[maxpool]
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size=2
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stride=2
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[convolutional]
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batch_normalize=1
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filters=512
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size=3
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stride=1
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pad=1
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activation=leaky
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[maxpool]
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size=2
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stride=1
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[convolutional]
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batch_normalize=1
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filters=1024
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size=3
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stride=1
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pad=1
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activation=leaky
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###########
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[convolutional]
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batch_normalize=1
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size=3
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stride=1
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pad=1
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filters=1024
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activation=leaky
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[convolutional]
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size=1
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stride=1
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pad=1
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filters=125
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activation=linear
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[region]
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anchors = 1.08,1.19, 3.42,4.41, 6.63,11.38, 9.42,5.11, 16.62,10.52
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bias_match=1
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classes=20
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coords=4
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num=5
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softmax=1
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jitter=.2
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rescore=1
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object_scale=5
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noobject_scale=1
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class_scale=1
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coord_scale=1
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absolute=1
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thresh = .6
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random=1
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@ -63,7 +63,7 @@ void *detect_in_thread(void *ptr)
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if(l.type == DETECTION){
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get_detection_boxes(l, 1, 1, demo_thresh, probs, boxes, 0);
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} else if (l.type == REGION){
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get_region_boxes(l, 1, 1, demo_thresh, probs, boxes, 0);
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get_region_boxes(l, 1, 1, demo_thresh, probs, boxes, 0, 0);
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} else {
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error("Last layer must produce detections\n");
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}
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@ -66,7 +66,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
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args.num_boxes = l.max_boxes;
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args.d = &buffer;
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args.type = DETECTION_DATA;
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args.threads = 4;
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args.threads = 8;
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args.angle = net.angle;
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args.exposure = net.exposure;
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@ -81,6 +81,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
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if(l.random && count++%10 == 0){
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printf("Resizing\n");
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int dim = (rand() % 10 + 10) * 32;
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if (get_current_batch(net)+100 > net.max_batches) dim = 544;
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//int dim = (rand() % 4 + 16) * 32;
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printf("%d\n", dim);
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args.w = dim;
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@ -208,7 +209,7 @@ void print_detector_detections(FILE **fps, char *id, box *boxes, float **probs,
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}
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}
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void print_imagenet_detections(FILE *fp, int id, box *boxes, float **probs, int total, int classes, int w, int h, int *map)
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void print_imagenet_detections(FILE *fp, int id, box *boxes, float **probs, int total, int classes, int w, int h)
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{
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int i, j;
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for(i = 0; i < total; ++i){
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@ -224,7 +225,6 @@ void print_imagenet_detections(FILE *fp, int id, box *boxes, float **probs, int
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for(j = 0; j < classes; ++j){
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int class = j;
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if (map) class = map[j];
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if (probs[i][class]) fprintf(fp, "%d %d %f %f %f %f %f\n", id, j+1, probs[i][class],
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xmin, ymin, xmax, ymax);
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}
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@ -233,6 +233,7 @@ void print_imagenet_detections(FILE *fp, int id, box *boxes, float **probs, int
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void validate_detector(char *datacfg, char *cfgfile, char *weightfile)
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{
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int j;
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list *options = read_data_cfg(datacfg);
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char *valid_images = option_find_str(options, "valid", "data/train.list");
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char *name_list = option_find_str(options, "names", "data/names.list");
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@ -242,23 +243,6 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile)
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int *map = 0;
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if (mapf) map = read_map(mapf);
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char buff[1024];
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char *type = option_find_str(options, "eval", "voc");
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FILE *fp = 0;
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int coco = 0;
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int imagenet = 0;
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if(0==strcmp(type, "coco")){
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snprintf(buff, 1024, "%s/coco_results.json", prefix);
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fp = fopen(buff, "w");
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fprintf(fp, "[\n");
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coco = 1;
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} else if(0==strcmp(type, "imagenet")){
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snprintf(buff, 1024, "%s/imagenet-detection.txt", prefix);
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fp = fopen(buff, "w");
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imagenet = 1;
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}
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network net = parse_network_cfg(cfgfile);
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if(weightfile){
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load_weights(&net, weightfile);
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@ -274,12 +258,31 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile)
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layer l = net.layers[net.n-1];
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int classes = l.classes;
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int j;
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FILE **fps = calloc(classes, sizeof(FILE *));
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for(j = 0; j < classes; ++j){
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snprintf(buff, 1024, "%s/%s%s.txt", prefix, base, names[j]);
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fps[j] = fopen(buff, "w");
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char buff[1024];
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char *type = option_find_str(options, "eval", "voc");
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FILE *fp = 0;
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FILE **fps = 0;
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int coco = 0;
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int imagenet = 0;
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if(0==strcmp(type, "coco")){
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snprintf(buff, 1024, "%s/coco_results.json", prefix);
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fp = fopen(buff, "w");
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fprintf(fp, "[\n");
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coco = 1;
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} else if(0==strcmp(type, "imagenet")){
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snprintf(buff, 1024, "%s/imagenet-detection.txt", prefix);
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fp = fopen(buff, "w");
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imagenet = 1;
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classes = 200;
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} else {
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fps = calloc(classes, sizeof(FILE *));
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for(j = 0; j < classes; ++j){
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snprintf(buff, 1024, "%s/%s%s.txt", prefix, base, names[j]);
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fps[j] = fopen(buff, "w");
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}
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}
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box *boxes = calloc(l.w*l.h*l.n, sizeof(box));
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float **probs = calloc(l.w*l.h*l.n, sizeof(float *));
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for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(classes, sizeof(float *));
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@ -330,12 +333,12 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile)
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network_predict(net, X);
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int w = val[t].w;
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int h = val[t].h;
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get_region_boxes(l, w, h, thresh, probs, boxes, 0);
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get_region_boxes(l, w, h, thresh, probs, boxes, 0, map);
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if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, classes, nms);
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if (coco){
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print_cocos(fp, path, boxes, probs, l.w*l.h*l.n, classes, w, h);
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} else if (imagenet){
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print_imagenet_detections(fp, i+t-nthreads+1 + 9741, boxes, probs, l.w*l.h*l.n, 200, w, h, map);
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print_imagenet_detections(fp, i+t-nthreads+1, boxes, probs, l.w*l.h*l.n, classes, w, h);
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} else {
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print_detector_detections(fps, id, boxes, probs, l.w*l.h*l.n, classes, w, h);
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}
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@ -345,7 +348,7 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile)
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}
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}
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for(j = 0; j < classes; ++j){
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fclose(fps[j]);
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if(fps) fclose(fps[j]);
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}
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if(coco){
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fseek(fp, -2, SEEK_CUR);
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@ -394,7 +397,7 @@ void validate_detector_recall(char *cfgfile, char *weightfile)
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image sized = resize_image(orig, net.w, net.h);
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char *id = basecfg(path);
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network_predict(net, sized.data);
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get_region_boxes(l, 1, 1, thresh, probs, boxes, 1);
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get_region_boxes(l, 1, 1, thresh, probs, boxes, 1, 0);
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if (nms) do_nms(boxes, probs, l.w*l.h*l.n, 1, nms);
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char labelpath[4096];
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@ -473,7 +476,7 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam
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time=clock();
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network_predict(net, X);
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printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
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get_region_boxes(l, 1, 1, thresh, probs, boxes, 0);
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get_region_boxes(l, 1, 1, thresh, probs, boxes, 0, 0);
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if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, l.classes, nms);
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draw_detections(im, l.w*l.h*l.n, thresh, boxes, probs, names, alphabet, l.classes);
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save_image(im, "predictions");
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@ -196,7 +196,8 @@ void forward_region_layer(const region_layer l, network_state state)
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if(truth.x > 100000 && truth.y > 100000){
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for(n = 0; n < l.n*l.w*l.h; ++n){
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int index = size*n + b*l.outputs + 5;
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float p = get_hierarchy_probability(l.output + index, l.softmax_tree, class);
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float scale = l.output[index-1];
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float p = scale*get_hierarchy_probability(l.output + index, l.softmax_tree, class);
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if(p > maxp){
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maxp = p;
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maxi = n;
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@ -324,7 +325,7 @@ void backward_region_layer(const region_layer l, network_state state)
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axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, state.delta, 1);
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}
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void get_region_boxes(layer l, int w, int h, float thresh, float **probs, box *boxes, int only_objectness)
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void get_region_boxes(layer l, int w, int h, float thresh, float **probs, box *boxes, int only_objectness, int *map)
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{
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int i,j,n;
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float *predictions = l.output;
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@ -348,8 +349,13 @@ void get_region_boxes(layer l, int w, int h, float thresh, float **probs, box *b
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hierarchy_predictions(predictions + class_index, l.classes, l.softmax_tree, 0);
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int found = 0;
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for(j = l.classes - 1; j >= 0; --j){
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if(1){
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if(map){
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for(j = 0; j < 200; ++j){
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float prob = scale*predictions[class_index+map[j]];
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probs[index][j] = (prob > thresh) ? prob : 0;
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}
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} else {
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for(j = l.classes - 1; j >= 0; --j){
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if(!found && predictions[class_index + j] > .5){
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found = 1;
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} else {
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@ -357,12 +363,9 @@ void get_region_boxes(layer l, int w, int h, float thresh, float **probs, box *b
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}
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float prob = predictions[class_index+j];
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probs[index][j] = (scale > thresh) ? prob : 0;
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}else{
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float prob = scale*predictions[class_index+j];
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probs[index][j] = (prob > thresh) ? prob : 0;
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}
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}
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}else{
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} else {
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for(j = 0; j < l.classes; ++j){
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float prob = scale*predictions[class_index+j];
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probs[index][j] = (prob > thresh) ? prob : 0;
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@ -9,7 +9,7 @@ typedef layer region_layer;
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region_layer make_region_layer(int batch, int h, int w, int n, int classes, int coords);
|
||||
void forward_region_layer(const region_layer l, network_state state);
|
||||
void backward_region_layer(const region_layer l, network_state state);
|
||||
void get_region_boxes(layer l, int w, int h, float thresh, float **probs, box *boxes, int only_objectness);
|
||||
void get_region_boxes(layer l, int w, int h, float thresh, float **probs, box *boxes, int only_objectness, int *map);
|
||||
void resize_region_layer(layer *l, int w, int h);
|
||||
|
||||
#ifdef GPU
|
||||
|
||||
Loading…
Reference in New Issue
Block a user