/
utils.py
264 lines (208 loc) · 8.45 KB
/
utils.py
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import random
import numpy as np
###########################################################
def shuffData(images,labels):
index=[i for i in range(len(labels))]
random.shuffle(index)
return images[index],labels[index]
def normalizeData(images):
means = []
stds = []
# for every channel in image(assume this is last dimension)
for ch in range(images.shape[-1]):
means.append(np.mean(images[:, :, :, ch]))
stds.append(np.std(images[:, :, :, ch]))
for i in range(images.shape[-1]):
images[:, :, :, i] = ((images[:, :, :, i] - means[i]) / stds[i])
def _augmentImage(image, pad,needflip):
"""Perform zero padding, randomly crop image to original size,
maybe mirror horizontally"""
init_shape = image.shape
new_shape = [init_shape[0] + pad * 2,
init_shape[1] + pad * 2,
init_shape[2]]
zeros_padded = np.zeros(new_shape)
zeros_padded[pad:init_shape[0] + pad, pad:init_shape[1] + pad, :] = image
# randomly crop to original size
init_x = np.random.randint(0, pad * 2)
init_y = np.random.randint(0, pad * 2)
cropped = zeros_padded[
init_x: init_x + init_shape[0],
init_y: init_y + init_shape[1],
:]
flip = random.getrandbits(1) and needflip
if flip:
cropped = cropped[:, ::-1, :]
return cropped
def augmentData(initial_images,needflip):
new_images = np.zeros(initial_images.shape)
for i in range(initial_images.shape[0]):
new_images[i] = _augmentImage(initial_images[i], pad=4,needflip=needflip)
return new_images
#above code come from https://github.com/ikhlestov/vision_networks/tree/master/data_providers
###############################################
class DatasetLiterator:
def __init__(self,images,labels,epochs,batchSize):
self.images=images
self.labels=np.array(labels,np.int32)
self.epochs=epochs
self.batchSize=batchSize
if batchSize>len(images):
raise Exception("too big batch size")
def getDatasetSize(self):
return len(self.labels)
def getBatchSize(self):
return self.batchSize
def getNextBatch(self):
index=[]
for i in range(self.epochs):
index+=[i for i in range(len(self.images))]
random.shuffle(index)
while len(index)>=self.batchSize:
batchIndex=index[:self.batchSize]
index=index[self.batchSize:]
batchImage=self.images[batchIndex]
batchLabel=self.labels[batchIndex]
yield batchImage,batchLabel
#yield last images, batch size is changed
if len(index)!=0:
yield self.images[index],self.labels[index]
class AugmentDatasetLiterator:
def __init__(self,images,labels,epochs,batchSize,needflip=True):
self.iterator=DatasetLiterator(images,labels,epochs,batchSize)
self.needFlip=needflip
def getNextBatch(self):
for batchImage,batchLabel in self.iterator.getNextBatch():
yield augmentData(batchImage,self.needFlip), batchLabel
def getDatasetSize(self):
return self.iterator.getDatasetSize()
def getBatchSize(self):
return self.iterator.getBatchSize()
#################################################################################
class LocalLearningRateTuner():
"""
base on validate test err in slide window
"""
def __init__(self,startLearningRate=1e-3,decayFactor=0.5,threshold=0.02,slideWindow=4,maxCoolDown=3,maxDontSave=6,minLearningRate=5e-5):
self.learningRateRecoder=[]
self.curLearningRate=startLearningRate
self.decayFactor=decayFactor
self.threshold=threshold
self.slidWindow=slideWindow
self.validateErrs=[]
self.maxCoolDown=maxCoolDown
self.maxDontSave=maxDontSave
self.minLearningRate=minLearningRate
self.curLowestErr=1
self.dontSaveCount=0
self.coolDown=0
def updateValidateErr(self, err):
if self.curLowestErr>err:
self.curLowestErr=err
self.dontSaveCount=0
else:
self.dontSaveCount+=1
self.validateErrs.append(err)
if len(self.validateErrs)<self.slidWindow:
return
if len(self.validateErrs)>self.slidWindow:
self.validateErrs.pop(0)
oldAvgErr=np.mean(self.validateErrs[:int(len(self.validateErrs) / 2)])
newAvgErr =np.mean(self.validateErrs[int(len(self.validateErrs) / 2):])
relativeDec=(oldAvgErr-newAvgErr) / oldAvgErr
# print("\n" * 2 + "*" * 20)
# print(self.validateErrs)
# print("oldAvgErr:"+str(oldAvgErr)+" newAvgErr:"+str(newAvgErr))
# print("relativeDec:"+str(relativeDec))
self.coolDown -= 1
if relativeDec<self.threshold:
if self.coolDown>0:
#print("keep learning rate to:" + str(self.curLearningRate))
pass
else:
self.coolDown=self.maxCoolDown
self.curLearningRate*=self.decayFactor
if self.curLearningRate<self.minLearningRate:
self.curLearningRate=self.minLearningRate
#print("decay learning rate to:"+str(self.curLearningRate))
else:
#print("keep learning rate to:"+str(self.curLearningRate))
pass
def getLearningRate(self):
self.learningRateRecoder.append(self.curLearningRate)
return self.curLearningRate
def isShouldSave(self):
return self.dontSaveCount==0
def isShouldStop(self):
if self.dontSaveCount>=self.maxDontSave:
return True
return False
def getFixTuner(self):
return FixLearningRateTuner(self.learningRateRecoder[:-(self.dontSaveCount-1)],isEarlyStop=False)
class GlobalLearningRateTuner():
"""
base on global validate test err
"""
def __init__(self,startLearningRate=1e-3,decayFactor=0.5,threshold=3,maxDontSave=7,minLearningRate=5e-5):
self.learningRateRecoder=[]
self.curLearningRate = startLearningRate
self.decayFactor = decayFactor
self.threshold = threshold
self.maxDontSave = maxDontSave
self.minLearningRate = minLearningRate
self.curLowestErr = 1
self.dontSaveCount = 0
def updateValidateErr(self, err):
if self.curLowestErr>err:
self.curLowestErr=err
self.dontSaveCount=0
#print("err:"+str(err)+" cur lowest")
return
else:
self.dontSaveCount+=1
if self.dontSaveCount % self.threshold==0 and self.dontSaveCount!=0:
self.curLearningRate *= self.decayFactor
if self.curLearningRate < self.minLearningRate:
self.curLearningRate = self.minLearningRate
#print("err:"+str(err)+" decay learning rate to:"+str(self.curLearningRate))
def getLearningRate(self):
self.learningRateRecoder.append(self.curLearningRate)
return self.curLearningRate
def isShouldSave(self):
return self.dontSaveCount==0
def isShouldStop(self):
if self.dontSaveCount>=self.maxDontSave:
return True
return False
def getFixTuner(self):
return FixLearningRateTuner(self.learningRateRecoder[:-(self.dontSaveCount-1)],isEarlyStop=False)
class FixLearningRateTuner():
def __init__(self,learningRateIndex,isEarlyStop=False):
self.checkCount=0
self.learningRateIndex=learningRateIndex
self.isEarlyStop=isEarlyStop
self.curLowestErr=1.0
self.dontSaveCount=0
def updateValidateErr(self, err):
self.checkCount+=1
if err<self.curLowestErr:
self.curLowestErr=err
self.dontSaveCount=0
else:
self.dontSaveCount+=1
def getLearningRate(self):
if self.checkCount<len(self.learningRateIndex):
return self.learningRateIndex[self.checkCount]
return self.learningRateIndex[-1]
def isShouldSave(self):
if self.isEarlyStop==True and self.dontSaveCount!=0:
return False
return True
def isShouldStop(self):
return len(self.learningRateIndex) == self.checkCount
def getFixLearningRateTuner(checkCountList, learningRateList, isEarlyStop):
learningRateIndex = []
for i in range(len(checkCountList)):
for _ in range(checkCountList[i]):
learningRateIndex.append(learningRateList[i])
return FixLearningRateTuner(learningRateIndex,isEarlyStop)