OneEye Changeset - 247811dfb9be

Changeset - 247811dfb9be

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Laman - 6 years ago 2019-05-14 16:12:12

data augmentation

3 files changed with 68 insertions and 23 deletions:

exp/kerokero/k_util.py

exp/kerokero/test.py

exp/kerokero/train.py

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exp/kerokero/k_util.py

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 import math
 import keras.backend as K
 import random
 def singleUnorderedLoss(yTrue,yPred):
 	d1=sum(math.sqrt(min((yTrue[i*2]-yPred[j*2])**2+(yTrue[i*2+1]-yPred[j*2+1])**2 for j in range(4))) for i in range(4))
 	d2=sum(math.sqrt(min((yTrue[i*2]-yPred[j*2])**2+(yTrue[i*2+1]-yPred[j*2+1])**2 for i in range(4))) for j in range(4))
 	return (d1+d2)/2
 import numpy as np
 import keras.backend as K
 def averageDistance(yTrue,yPred):
 	squares=K.square(yTrue-yPred)
 	distances=K.sqrt(K.sum(squares,-1))
 	return K.mean(distances,-1)
 def transform(image,label):
 	if random.choice((True,False)):
 		image=image[::-1] # reflect vertically
 		label[...,1]*=-1 # reflect y values
 		label=label[::-1,...] # switch back to counter-clockwise order
 	rot=random.randint(0,3)
 	image=np.rot90(image,rot)
 	if rot==1:
 		label=label[...,::-1]
 		label[...,1]*=-1
 	elif rot==2:
 		label*=-1
 	elif rot==3:
 		label=label[...,::-1]
 		label[...,0]*=-1
 	k=0
 	val=2
 	for (i,point) in enumerate(label): # rotate the upper-leftmost point to the first position
 		v=sum(point)
 		if v<val:
 			k=i
 			val=v
 	label=np.concatenate((label[k:],label[:k]))
 	return (image,label)
 def generateData(images,labels,batch_size=32):
 	n=len(images)
 	keys=list(range(n))
 	while True:
 		random.shuffle(keys)
 		for i in range(0,n,batch_size):
 			ks=keys[i:i+batch_size]
 			imgs=images[ks]
 			labs=labels[ks]
 			for j in range(len(ks)):
 				(imgs[j],labs[j])=transform(imgs[j],labs[j])
 			yield (imgs,labs)

exp/kerokero/test.py

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@@ @@ -3,13 +3,12 @@ import logging as log @@
 import numpy as np
 from keras.models import load_model
 import keras.losses
 import keras.metrics
 from prepare_data import loadDataset,Sample
 from analyzer.epoint import EPoint
 from analyzer.corners import Corners
 from k_util import averageDistance
+from k_util import averageDistance,generateData
 import config as cfg
 keras.losses.averageDistance=averageDistance
@@ @@ -25,11 +24,11 @@ model.summary() @@
 log.info("loading data...")
 with np.load(args.data) as data:
 	testImages=data["testImages"]
 	testLabels=data["testLabels"]
 	testImages=data["testImages"].reshape((-1,224,224,1))
 	testLabels=data["testLabels"].reshape((-1,4,2))
 log.info("done")
-log.info(model.evaluate(testImages.reshape((-1,224,224,1)),testLabels.reshape((-1,4,2))))
+log.info(model.evaluate(testImages,testLabels))
 for img in testImages:
 	label=model.predict(np.reshape(img,(1,224,224,1)))

exp/kerokero/train.py

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 import os
 import math
 from time import time
 import argparse
 import logging as log
@@ @@ -11,7 +12,7 @@ import keras.losses @@
 import keras.metrics
 import config as cfg
 from k_util import averageDistance
+from k_util import averageDistance,generateData
 keras.losses.averageDistance=averageDistance
 keras.metrics.averageDistance=averageDistance
@@ @@ -78,23 +79,29 @@ if args.load_model: @@
 log.info("loading data...")
 with np.load(args.data) as data:
 	trainImages=data["trainImages"]
 	trainLabels=data["trainLabels"]
 	testImages=data["testImages"]
 	testLabels=data["testLabels"]
 	trainImages=data["trainImages"].reshape((-1,224,224,1))
 	trainLabels=data["trainLabels"].reshape((-1,4,2))
 	testImages=data["testImages"].reshape((-1,224,224,1))
 	testLabels=data["testLabels"].reshape((-1,4,2))
 log.info("done")
 n=len(trainImages)
 k=round(n*0.9)
 n_=n-k
 (trainImages,valImages)=(np.float32(trainImages[:k]),np.float32(trainImages[k:]))
 (trainLabels,valLabels)=(np.float32(trainLabels[:k]),np.float32(trainLabels[k:]))
 tensorboard=TensorBoard(log_dir=os.path.join(args.log_dir,"{}".format(time())))
 checkpoint=ModelCheckpoint(args.save_model,monitor="val_loss",period=10)
 model.fit(
 	trainImages.reshape((-1,224,224,1)),
 	trainLabels.reshape((-1,4,2)),
 model.fit_generator(
 	generateData(trainImages,trainLabels,batch_size=20),
 	epochs=args.epochs,
 	initial_epoch=args.initial_epoch,
 	batch_size=20,
 	validation_split=0.2,
 	steps_per_epoch=math.ceil(n_/20),
 	validation_data=generateData(valImages,valLabels,batch_size=20),
 	validation_steps=math.ceil(k/20),
 	callbacks=[tensorboard,checkpoint]
+)
-log.info(model.evaluate(testImages.reshape((-1,224,224,1)),testLabels.reshape((-1,4,2))))
+log.info(model.evaluate(testImages,testLabels))

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