def __init__(self, workingDir): self.directory = workingDir self.writeSolverFile() self.solver = caffe.SGDSolver(self.directory + 'solver.prototxt') self.iter = 0 self.itersPerEpisode = config.geti('trainingIterationsPerBatch') self.lr = config.getf('learningRate') self.stepSize = config.geti('stepSize') self.gamma = config.getf('gamma') print 'CAFFE SOLVER INITALIZED'
def __init__(self, workingDir): self.directory = workingDir self.writeSolverFile() self.solver = caffe.SGDSolver(self.directory + 'solver.prototxt') self.iter = 0 self.itersPerEpisode = config.geti('trainingIterationsPerBatch') self.lr = config.getf('learningRate') self.stepSize = config.geti('stepSize') self.gamma = config.getf('gamma') print 'CAFFE SOLVER INITALIZED'
def train(self): networkFile = config.get('networkDir') + config.get( 'snapshotPrefix') + '_iter_' + config.get( 'trainingIterationsPerBatch') + '.caffemodel' interactions = config.geti('trainInteractions') minEpsilon = config.getf('minTrainingEpsilon') epochSize = len(self.environment.imageList) / 1 epsilon = 1.0 self.controller.setEpsilonGreedy(epsilon, self.environment.sampleAction) epoch = 1 exEpochs = config.geti('explorationEpochs') while epoch <= exEpochs: s = cu.tic() print 'Epoch', epoch, ': Exploration (epsilon=1.0)' self.runEpoch(interactions, len(self.environment.imageList)) self.task.flushStats() s = cu.toc('Epoch done in ', s) epoch += 1 self.learner = QLearning() self.agent.learner = self.learner egEpochs = config.geti('epsilonGreedyEpochs') while epoch <= egEpochs + exEpochs: s = cu.tic() epsilon = epsilon - (1.0 - minEpsilon) / float(egEpochs) if epsilon < minEpsilon: epsilon = minEpsilon self.controller.setEpsilonGreedy(epsilon, self.environment.sampleAction) print 'Epoch', epoch, '(epsilon-greedy:{:5.3f})'.format(epsilon) self.runEpoch(interactions, epochSize) self.task.flushStats() self.doValidation(epoch) s = cu.toc('Epoch done in ', s) epoch += 1 maxEpochs = config.geti('exploitLearningEpochs') + exEpochs + egEpochs while epoch <= maxEpochs: s = cu.tic() print 'Epoch', epoch, '(exploitation mode: epsilon={:5.3f})'.format( epsilon) self.runEpoch(interactions, epochSize) self.task.flushStats() self.doValidation(epoch) s = cu.toc('Epoch done in ', s) shutil.copy(networkFile, networkFile + '.' + str(epoch)) epoch += 1
def __init__(self, imageList, mode, groundTruthFile=None): self.mode = mode self.cnn = cn.ConvNet() self.testRecord = None self.idx = -1 self.imageList = [x.strip() for x in open(imageList)] self.groundTruth = cu.loadBoxIndexFile(groundTruthFile) #self.imageList = self.rankImages() #self.imageList = self.imageList[0:10] allImgs = set([x.strip() for x in open(config.get('allImagesList'))]) self.negativeSamples = list( allImgs.difference(set(self.groundTruth.keys()))) self.negativeEpisode = False if self.mode == 'train': self.negativeProbability = config.getf('negativeEpisodeProb') random.shuffle(self.imageList) #self.priorMemory = PriorMemory(config.get('allObjectsBoxes'), self.groundTruth, self.cnn) self.loadNextEpisode()
def train(self): networkFile = config.get('networkDir') + config.get('snapshotPrefix') + '_iter_' + config.get('trainingIterationsPerBatch') + '.caffemodel' interactions = config.geti('trainInteractions') minEpsilon = config.getf('minTrainingEpsilon') epochSize = len(self.environment.imageList)/1 epsilon = 1.0 self.controller.setEpsilonGreedy(epsilon, self.environment.sampleAction) epoch = 1 exEpochs = config.geti('explorationEpochs') while epoch <= exEpochs: s = cu.tic() print 'Epoch',epoch,': Exploration (epsilon=1.0)' self.runEpoch(interactions, len(self.environment.imageList)) self.task.flushStats() s = cu.toc('Epoch done in ',s) epoch += 1 self.learner = QLearning() self.agent.learner = self.learner egEpochs = config.geti('epsilonGreedyEpochs') while epoch <= egEpochs + exEpochs: s = cu.tic() epsilon = epsilon - (1.0-minEpsilon)/float(egEpochs) if epsilon < minEpsilon: epsilon = minEpsilon self.controller.setEpsilonGreedy(epsilon, self.environment.sampleAction) print 'Epoch',epoch ,'(epsilon-greedy:{:5.3f})'.format(epsilon) self.runEpoch(interactions, epochSize) self.task.flushStats() self.doValidation(epoch) s = cu.toc('Epoch done in ',s) epoch += 1 maxEpochs = config.geti('exploitLearningEpochs') + exEpochs + egEpochs while epoch <= maxEpochs: s = cu.tic() print 'Epoch',epoch,'(exploitation mode: epsilon={:5.3f})'.format(epsilon) self.runEpoch(interactions, epochSize) self.task.flushStats() self.doValidation(epoch) s = cu.toc('Epoch done in ',s) shutil.copy(networkFile, networkFile + '.' + str(epoch)) epoch += 1
def __init__(self, alpha=0.5): ValueBasedLearner.__init__(self) self.alpha = alpha self.gamma = config.getf('gammaDiscountReward') self.netManager = CaffeMultiLayerPerceptronManagement(config.get('networkDir'))
__author__ = "Juan C. Caicedo, [email protected]" import os import random import numpy as np import scipy.io import caffe import learn.rl.RLConfig as config import BoxSearchState as bs from pybrain.rl.learners.valuebased.valuebased import ValueBasedLearner DETECTION_REWARD = config.getf('detectionReward') ACTION_HISTORY_SIZE = bs.NUM_ACTIONS*config.geti('actionHistoryLength') ACTION_HISTORY_LENTH = config.geti('actionHistoryLength') NETWORK_INPUTS = config.geti('stateFeatures')/config.geti('temporalWindow') REPLAY_MEMORY_SIZE = config.geti('trainingIterationsPerBatch')*config.geti('trainingBatchSize') def generateRandomActionHistory(): actions = np.zeros((ACTION_HISTORY_SIZE)) history = [i*bs.NUM_ACTIONS + np.random.randint(0,bs.PLACE_LANDMARK) for i in range(ACTION_HISTORY_LENTH)] actions[history] = 1 return actions class QLearning(ValueBasedLearner): offPolicy = True batchMode = True dataset = [] trainingSamples = 0
__author__ = "Juan C. Caicedo, [email protected]" from pybrain.rl.environments import Task import BoxSearchState as bss import utils.utils as cu import utils.libDetection as det import numpy as np import learn.rl.RLConfig as config MIN_ACCEPTABLE_IOU = config.getf('minAcceptableIoU') DETECTION_REWARD = config.getf('detectionReward') def center(box): return [(box[2] + box[0]) / 2.0, (box[3] + box[1]) / 2.0] def euclideanDist(c1, c2): return (c1[0] - c2[0])**2 + (c1[1] - c2[1])**2 class BoxSearchTask(Task): def __init__(self, environment=None, groundTruthFile=None): Task.__init__(self, environment) if groundTruthFile is not None: self.groundTruth = cu.loadBoxIndexFile(groundTruthFile) self.image = '' self.epochRecall = [] self.epochMaxIoU = []
X_COORD_UP = 0 Y_COORD_UP = 1 SCALE_UP = 2 ASPECT_RATIO_UP = 3 X_COORD_DOWN = 4 Y_COORD_DOWN = 5 SCALE_DOWN = 6 ASPECT_RATIO_DOWN = 7 PLACE_LANDMARK = 8 SKIP_REGION = 9 # BOX LIMITS MIN_ASPECT_RATIO = 0.15 MAX_ASPECT_RATIO = 6.00 MIN_BOX_SIDE = 10 STEP_FACTOR = config.getf('boxResizeStep') DELTA_SIZE = config.getf('boxResizeStep') # OTHER DEFINITIONS NUM_ACTIONS = config.geti('outputActions') RESET_BOX_FACTOR = 2 QUADRANT_SIZE = 0.7 def fingerprint(b): return '_'.join( map(str, map(int, b)) ) class BoxSearchState(): def __init__(self, imageName, boxReset='Full', groundTruth=None): self.imageName = imageName self.visibleImage = Image.open(config.get('imageDir') + '/' + self.imageName + '.jpg')
def __init__(self, alpha=0.5): ValueBasedLearner.__init__(self) self.alpha = alpha self.gamma = config.getf('gammaDiscountReward') self.netManager = CaffeMultiLayerPerceptronManagement( config.get('networkDir'))
__author__ = "Juan C. Caicedo, [email protected]" from pybrain.rl.environments import Task import BoxSearchState as bss import utils.utils as cu import utils.libDetection as det import numpy as np import learn.rl.RLConfig as config MIN_ACCEPTABLE_IOU = config.getf('minAcceptableIoU') DETECTION_REWARD = config.getf('detectionReward') def center(box): return [ (box[2] + box[0])/2.0 , (box[3] + box[1])/2.0 ] def euclideanDist(c1, c2): return (c1[0] - c2[0])**2 + (c1[1] - c2[1])**2 class BoxSearchTask(Task): def __init__(self, environment=None, groundTruthFile=None): Task.__init__(self, environment) if groundTruthFile is not None: self.groundTruth = cu.loadBoxIndexFile(groundTruthFile) self.image = '' self.epochRecall = [] self.epochMaxIoU = [] self.epochLandmarks = []
__author__ = "Juan C. Caicedo, [email protected]" import os import random import numpy as np import scipy.io import caffe import learn.rl.RLConfig as config import BoxSearchState as bs from pybrain.rl.learners.valuebased.valuebased import ValueBasedLearner DETECTION_REWARD = config.getf('detectionReward') ACTION_HISTORY_SIZE = bs.NUM_ACTIONS * config.geti('actionHistoryLength') ACTION_HISTORY_LENTH = config.geti('actionHistoryLength') NETWORK_INPUTS = config.geti('stateFeatures') / config.geti('temporalWindow') REPLAY_MEMORY_SIZE = config.geti('trainingIterationsPerBatch') * config.geti( 'trainingBatchSize') def generateRandomActionHistory(): actions = np.zeros((ACTION_HISTORY_SIZE)) history = [ i * bs.NUM_ACTIONS + np.random.randint(0, bs.PLACE_LANDMARK) for i in range(ACTION_HISTORY_LENTH) ] actions[history] = 1 return actions class QLearning(ValueBasedLearner):
def test(self): interactions = config.geti('testInteractions') self.controller.setEpsilonGreedy(config.getf('testEpsilon')) self.runEpoch(interactions, len(self.environment.imageList))
import learn.rl.RLConfig as config import numpy as np import scipy.io import utils.MemoryUsage import BoxSearchState as bss import PriorMemory as prm import random STATE_FEATURES = config.geti('stateFeatures')/config.geti('temporalWindow') NUM_ACTIONS = config.geti('outputActions') TEMPORAL_WINDOW = config.geti('temporalWindow') HISTORY_FACTOR = config.geti('historyFactor') NEGATIVE_PROBABILITY = config.getf('negativeEpisodeProb') class BoxSearchAgent(): image = None observation = None action = None reward = None timer = 0 def __init__(self, qnet, learner=None): self.controller = qnet self.learner = learner self.avgReward = 0 self.replayMemory = None self.priorMemory = None
__author__ = "Juan C. Caicedo, [email protected]" import os import utils.utils as cu import numpy as np import caffe from caffe import wrapperv0 import learn.rl.RLConfig as config LAYER = config.get('convnetLayer') MARK_WIDTH = config.getf('markWidth') class ConvNet(): def __init__(self): self.net = None self.image = '' self.id = 0 self.loadNetwork() def loadNetwork(self): self.imgDim = config.geti('imageDim') self.cropSize = config.geti('cropSize') self.contextPad = config.geti('contextPad') #self.stateContextFactor = config.geti('stateContextFactor') modelFile = config.get('convnetDir') + config.get('convNetDef') networkFile = config.get('convnetDir') + config.get('trainedConvNet') self.net = wrapperv0.ImageNetClassifier(modelFile, networkFile, IMAGE_DIM=self.imgDim, CROPPED_DIM=self.cropSize, MEAN_IMAGE=config.get('meanImage')) self.net.caffenet.set_mode_gpu()
def test(self): interactions = config.geti('testInteractions') self.controller.setEpsilonGreedy(config.getf('testEpsilon')) self.runEpoch(interactions, len(self.environment.imageList))
import learn.rl.RLConfig as config import numpy as np import scipy.io import utils.MemoryUsage import BoxSearchState as bss import PriorMemory as prm import random STATE_FEATURES = config.geti('stateFeatures') / config.geti('temporalWindow') NUM_ACTIONS = config.geti('outputActions') TEMPORAL_WINDOW = config.geti('temporalWindow') HISTORY_FACTOR = config.geti('historyFactor') NEGATIVE_PROBABILITY = config.getf('negativeEpisodeProb') class BoxSearchAgent(): image = None observation = None action = None reward = None timer = 0 def __init__(self, qnet, learner=None): self.controller = qnet self.learner = learner self.avgReward = 0 self.replayMemory = None
__author__ = "Juan C. Caicedo, [email protected]" import os import utils.utils as cu import numpy as np import caffe from caffe import wrapperv0 import learn.rl.RLConfig as config LAYER = config.get('convnetLayer') MARK_WIDTH = config.getf('markWidth') class ConvNet(): def __init__(self): self.net = None self.image = '' self.id = 0 self.loadNetwork() def loadNetwork(self): self.imgDim = config.geti('imageDim') self.cropSize = config.geti('cropSize') self.contextPad = config.geti('contextPad') #self.stateContextFactor = config.geti('stateContextFactor') modelFile = config.get('convnetDir') + config.get('convNetDef') networkFile = config.get('convnetDir') + config.get('trainedConvNet') self.net = wrapperv0.ImageNetClassifier( modelFile,