def main():
"""
Inputs:
None
Outputs:
Runs Testing code
"""
# TODO: Make LogDir
# TODO: Display time to end and cleanup other print statements with color
# TODO: Make logging file a parameter
# Parse Command Line arguments
Parser = argparse.ArgumentParser()
Parser.add_argument('--ModelPath', dest='ModelPath', default='/media/nitin/Research/EVDodge/CheckpointsEVDBHomographyDodgeNetLR5e-4Epoch200/199model.ckpt',\
help='Path to load latest model from, Default:ModelPath')
Parser.add_argument('--ReadPath', dest='ReadPath', default='/media/nitin/Research/EVDodge/DatasetChethanEvents/DeblurredHomography',\
help='Path to load images from, Default:ReadPath')
Parser.add_argument('--WritePath', dest='WritePath', default='/media/nitin/Research/EVDodge/DatasetChethanEvents/DeblurredHomographyDodgeNet',\
help='Path to write images to, Default:WritePath')
Parser.add_argument(
'--GPUDevice',
type=int,
default=0,
help='What GPU do you want to use? -1 for CPU, Default:0')
Args = Parser.parse_args()
ModelPath = Args.ModelPath
ReadPath = Args.ReadPath
WritePath = Args.WritePath
GPUDevice = Args.GPUDevice
# Set GPUDevice
tu.SetGPU(GPUDevice)
# Setup all needed parameters including file reading
TrainNames, ImageSize, PatchSize, NumTrainSamples, NumImgsStack = SetupAll(
ReadPath)
# Define PlaceHolder variables for Input and Predicted output
PatchPH = tf.placeholder(tf.float32,
shape=(1, PatchSize[0], PatchSize[1],
2 * PatchSize[2]),
name='Input')
TestOperation(PatchPH, PatchSize, ModelPath, ReadPath, WritePath,
TrainNames, NumTrainSamples)
def main():
"""
Inputs:
None
Outputs:
Runs the Training and testing code based on the Flag
"""
# TODO: Make LogDir
# TODO: Make logging file a parameter
# TODO: Time to complete print
# Parse Command Line arguments
Parser = argparse.ArgumentParser()
Parser.add_argument('--BasePath', default='/home/nitin/EVDodge/DatasetsForPaper/DownfacingEventsProcessed', help='Base path of images, Default:/home/nitin/EVDodge/DatasetsForPaper/DownfacingEventsProcessed')
Parser.add_argument('--NumEpochs', type=int, default=200, help='Number of Epochs to Train for, Default:200')
Parser.add_argument('--DivTrain', type=int, default=1, help='Factor to reduce Train data by per epoch, Default:1')
Parser.add_argument('--MiniBatchSize', type=int, default=256, help='Size of the MiniBatch to use, Default:256')
Parser.add_argument('--LoadCheckPoint', type=int, default=0, help='Load Model from latest Checkpoint from CheckPointsPath?, Default:0')
Parser.add_argument('--LossFuncName', default='PhotoL1', help='Choice of Loss functions, choose from PhotoL1, PhotoChab, PhotoRobust when using TrainingType as US. Default:PhotoL1')
Parser.add_argument('--NetworkType', default='Small', help='Choice of Network type, choose from Small, Large, Default:Small')
Parser.add_argument('--CheckPointPath', default='../CheckpointsHomography/', help='Path to save checkpoints, Default:../CheckpointsHomography/')
Parser.add_argument('--LogsPath', default='/media/nitin/Research/EVDodge/Logs/', help='Path to save Logs, Default:/media/nitin/Research/EVDodge/Logs/')
Parser.add_argument('--GPUDevice', type=int, default=0, help='What GPU do you want to use? -1 for CPU, Default:0')
Parser.add_argument('--LR', type=float, default=1e-4, help='Learning Rate, Default: 1e-4')
Parser.add_argument('--TrainingType', default='US', help='Training Type, S: Supervised, US: Unsupervised, Default: US')
Args = Parser.parse_args()
NumEpochs = Args.NumEpochs
BasePath = Args.BasePath
DivTrain = float(Args.DivTrain)
MiniBatchSize = Args.MiniBatchSize
LoadCheckPoint = Args.LoadCheckPoint
LossFuncName = Args.LossFuncName
NetworkType = Args.NetworkType
CheckPointPath = Args.CheckPointPath
LogsPath = Args.LogsPath
GPUDevice = Args.GPUDevice
LearningRate = Args.LR
TrainingType = Args.TrainingType
# Set GPUDevice
tu.SetGPU(GPUDevice)
# Setup all needed parameters including file reading
TrainNames, ValNames, TestNames, OptimizerParams,\
SaveCheckPoint, ImageSize, Rho, NumTrainSamples, NumValSamples, NumTestSamples,\
NumTestRunsPerEpoch = SetupAll(BasePath, LearningRate)
# If CheckPointPath doesn't exist make the path
if(not (os.path.isdir(CheckPointPath))):
os.makedirs(CheckPointPath)
# Find Latest Checkpoint File
if LoadCheckPoint==1:
LatestFile = FindLatestModel(CheckPointPath)
else:
LatestFile = None
# Pretty print stats
PrettyPrint(NumEpochs, DivTrain, MiniBatchSize, NumTrainSamples, NumTestSamples, LatestFile)
# Define PlaceHolder variables for Input and Predicted output
ImgPH = tf.placeholder(tf.float32, shape=(MiniBatchSize, ImageSize[0], ImageSize[1], 6), name='Input')
# PH for losses
I1PH = tf.placeholder(tf.float32, shape=(MiniBatchSize, 260, 346, 3), name='I1')
I2PH = tf.placeholder(tf.float32, shape=(MiniBatchSize, 260, 346, 3), name='I2')
MaskPH = tf.placeholder(tf.float32, shape=(MiniBatchSize, 260, 346, 3), name='Mask')
AllPtsPH = tf.placeholder(tf.float32, shape=(MiniBatchSize, 4, 2), name='AllPts')
LabelPH = tf.placeholder(tf.float32, shape=(MiniBatchSize, 8, 1), name='Label')
TrainOperation(ImgPH, I1PH, I2PH, MaskPH, AllPtsPH, LabelPH, TrainNames, TestNames, NumTrainSamples, ImageSize, Rho,
NumEpochs, MiniBatchSize, OptimizerParams, SaveCheckPoint, CheckPointPath, NumTestRunsPerEpoch,
DivTrain, LatestFile, LossFuncName, NetworkType, BasePath, LogsPath, TrainingType)
def main():
"""
Inputs:
None
Outputs:
Runs the Training and testing code based on the Flag
"""
# TODO: Make LogDir
# TODO: Make logging file a parameter
# TODO: Time to complete print
# Parse Command Line arguments
Parser = argparse.ArgumentParser()
Parser.add_argument('--BasePath', default='/media/nitin/Research/EVDodge/downfacing_processed', help='Base path of images, Default:/media/nitin/Research/EVDodge/downfacing_processed')
Parser.add_argument('--NumEpochs', type=int, default=200, help='Number of Epochs to Train for, Default:200')
Parser.add_argument('--DivTrain', type=int, default=1, help='Factor to reduce Train data by per epoch, Default:1')
Parser.add_argument('--MiniBatchSize', type=int, default=256, help='Size of the MiniBatch to use, Default:256')
Parser.add_argument('--LoadCheckPoint', type=int, default=0, help='Load Model from latest Checkpoint from CheckPointsPath?, Default:0')
Parser.add_argument('--CheckPointPath', default='../CheckpointsHomography/', help='Path to save checkpoints, Default:../CheckpointsHomography/')
Parser.add_argument('--LogsPath', default='/media/nitin/Research/EVDodge/Logs/', help='Path to save Logs, Default:/media/nitin/Research/EVDodge/Logs/')
Parser.add_argument('--LossFuncName', default='M', help='Choice of Loss functions, choose from M for Mean, V for Variance, Default:M')
Parser.add_argument('--GPUDevice', type=int, default=0, help='What GPU do you want to use? -1 for CPU, Default:0')
Parser.add_argument('--LR', type=float, default=1e-4, help='Learning Rate, Default: 1e-4')
Parser.add_argument('--SymType', default='L1', help='Similarity mapping, choose from L1 and Chab, Default:L1')
Args = Parser.parse_args()
NumEpochs = Args.NumEpochs
BasePath = Args.BasePath
DivTrain = float(Args.DivTrain)
MiniBatchSize = Args.MiniBatchSize
LoadCheckPoint = Args.LoadCheckPoint
CheckPointPath = Args.CheckPointPath
LogsPath = Args.LogsPath
LossFuncName = Args.LossFuncName
GPUDevice = Args.GPUDevice
LearningRate = Args.LR
SymType = Args.SymType
# Set GPUDevice
tu.SetGPU(GPUDevice)
# Setup all needed parameters including file reading
TrainNames, ValNames, TestNames, OptimizerParams,\
SaveCheckPoint, ImageSize, PatchSize, NumTrainSamples, NumValSamples, NumTestSamples,\
NumTestRunsPerEpoch = SetupAll(BasePath, LearningRate)
# Find Latest Checkpoint File
if LoadCheckPoint==1:
LatestFile = FindLatestModel(CheckPointPath)
else:
LatestFile = None
# Pretty print stats
PrettyPrint(NumEpochs, DivTrain, MiniBatchSize, NumTrainSamples, NumTestSamples, LatestFile)
# Define PlaceHolder variables for Input and Predicted output
PatchPH = tf.placeholder(tf.float32, shape=(MiniBatchSize, PatchSize[0], PatchSize[1], PatchSize[2]), name='Input')
# PH for losses
IPH = tf.placeholder(tf.float32, shape=(MiniBatchSize, ImageSize[0], ImageSize[1], ImageSize[2]), name='IPH')
MaskPH = tf.placeholder(tf.float32, shape=(MiniBatchSize, ImageSize[0], ImageSize[1], ImageSize[2]), name='Mask')
TrainOperation(PatchPH, IPH, MaskPH, TrainNames, TestNames, NumTrainSamples, ImageSize, PatchSize,
NumEpochs, MiniBatchSize, OptimizerParams, SaveCheckPoint, CheckPointPath, NumTestRunsPerEpoch,
DivTrain, LatestFile, LossFuncName, BasePath, LogsPath, SymType)
def main():
"""
Inputs:
None
Outputs:
Runs Testing code
"""
# TODO: Make LogDir
# TODO: Display time to end and cleanup other print statements with color
# TODO: Make logging file a parameter
# Parse Command Line arguments
Parser = argparse.ArgumentParser()
Parser.add_argument('--ModelPath', dest='ModelPath', default='/media/nitin/Research/EVDodge/CheckpointsDeblurHomographyLR1e-4Epochs400/399model.ckpt',\
help='Path to load latest model from, Default:ModelPath')
Parser.add_argument('--ReadPath', dest='ReadPath', default='/media/nitin/Research/EVDodge/DatasetChethanEvents/Deblurred',\
help='Path to load images from, Default:ReadPath')
Parser.add_argument('--WritePath', dest='WritePath', default='/media/nitin/Research/EVDodge/DatasetChethanEvents/DeblurredHomography',\
help='Path to load images from, Default:WritePath')
Parser.add_argument(
'--GPUDevice',
type=int,
default=0,
help='What GPU do you want to use? -1 for CPU, Default:0')
Parser.add_argument(
'--CropType',
dest='CropType',
default='C',
help=
'What kind of crop do you want to perform? R: Random, C: Center, Default: C'
)
Args = Parser.parse_args()
ModelPath = Args.ModelPath
ReadPath = Args.ReadPath
WritePath = Args.WritePath
GPUDevice = Args.GPUDevice
CropType = Args.CropType
# Set GPUNum
tu.SetGPU(GPUDevice)
# Setup all needed parameters including file reading
TrainNames, ImageSize, PatchSize, NumTrainSamples = SetupAll(ReadPath)
# Define PlaceHolder variables for Input and Predicted output
PatchPH = tf.placeholder(tf.float32,
shape=(1, PatchSize[0], PatchSize[1],
PatchSize[2] * 2),
name='Input')
I1PH = tf.placeholder(tf.float32,
shape=(1, PatchSize[0], PatchSize[1], PatchSize[2]),
name='I1')
I2PH = tf.placeholder(tf.float32,
shape=(1, PatchSize[0], PatchSize[1], PatchSize[2]),
name='I2')
if (not os.path.exists(WritePath)):
cprint("WARNING: %s doesnt exist, Creating it." % WritePath, 'yellow')
os.mkdir(WritePath)
TestOperation(PatchPH, I1PH, I2PH, PatchSize, ModelPath, ReadPath,
WritePath, TrainNames, NumTrainSamples, CropType)
def main():
"""
Inputs:
None
Outputs:
Runs the Training and testing code based on the Flag
"""
# TODO: Make LogDir
# TODO: Make logging file a parameter
# TODO: Time to complete print
# Parse Command Line arguments
Parser = argparse.ArgumentParser()
Parser.add_argument(
'--BasePath',
default=
'/media/nitin/Research/EVDodge/DatasetChethanEvents/DeblurredHomography',
help=
'Base path of images, Default:/media/nitin/Research/EVDodge/processed')
Parser.add_argument('--NumEpochs',
type=int,
default=200,
help='Number of Epochs to Train for, Default:200')
Parser.add_argument(
'--DivTrain',
type=int,
default=1,
help='Factor to reduce Train data by per epoch, Default:1')
Parser.add_argument('--MiniBatchSize',
type=int,
default=256,
help='Size of the MiniBatch to use, Default:256')
Parser.add_argument(
'--LoadCheckPoint',
type=int,
default=0,
help=
'Load Model from latest Checkpoint from CheckPointsPath?, Default:0')
Parser.add_argument(
'--GPUDevice',
type=int,
default=0,
help='What GPU do you want to use? -1 for CPU, Default:0')
Parser.add_argument(
'--CheckPointPath',
default='../CheckpointsSeg/',
help='Path to save checkpoints, Default:../CheckpointsSeg/')
Parser.add_argument(
'--LogsPath',
default='/media/nitin/Research/EVDodge/LogsSeg/',
help='Path to save Logs, Default:/media/nitin/Research/EVDodge/LogsSeg/'
)
Parser.add_argument('--LR',
type=float,
default=1e-3,
help='Learning Rate, Default: 1e-3')
Parser.add_argument(
'--MaxFrameDiff',
type=int,
default=1,
help='Maximum Frame difference to feed into network, Default: 1')
Args = Parser.parse_args()
NumEpochs = Args.NumEpochs
BasePath = Args.BasePath
DivTrain = float(Args.DivTrain)
MiniBatchSize = Args.MiniBatchSize
LoadCheckPoint = Args.LoadCheckPoint
GPUDevice = Args.GPUDevice
CheckPointPath = Args.CheckPointPath
LogsPath = Args.LogsPath
LearningRate = Args.LR
MaxFrameDiff = Args.MaxFrameDiff
# Set GPUDevice
tu.SetGPU(GPUDevice)
# If CheckPointPath doesn't exist make the path
if (not (os.path.isdir(CheckPointPath))):
os.makedirs(CheckPointPath)
# If LogsPath doesn't exist make the path
if (not (os.path.isdir(LogsPath))):
os.makedirs(LogsPath)
# Setup all needed parameters including file reading
TrainNames, TrainLabels, OptimizerParams, SaveCheckPoint, Factor, ImageSize, NumTrainSamples = SetupAll(
BasePath, LearningRate)
# Find Latest Checkpoint File
if LoadCheckPoint == 1:
LatestFile = FindLatestModel(CheckPointPath)
else:
LatestFile = None
# Pretty print stats
PrettyPrint(NumEpochs, DivTrain, MiniBatchSize, NumTrainSamples,
LatestFile)
# Define PlaceHolder variables for Input and Predicted output
ImgPH = tf.placeholder(tf.float32,
shape=(MiniBatchSize, ImageSize[0], ImageSize[1],
6),
name='Input')
I1PH = tf.placeholder(tf.float32,
shape=(MiniBatchSize, ImageSize[0], ImageSize[1], 3),
name='I1')
I2PH = tf.placeholder(tf.float32,
shape=(MiniBatchSize, ImageSize[0], ImageSize[1], 3),
name='I2')
LabelPH = tf.placeholder(tf.float32,
shape=(MiniBatchSize, ImageSize[0], ImageSize[1],
2),
name='Label') # 2 classes
TrainOperation(ImgPH, I1PH, I2PH, LabelPH, TrainNames, TrainLabels,
NumTrainSamples, Factor, ImageSize, NumEpochs,
MiniBatchSize, OptimizerParams, SaveCheckPoint,
CheckPointPath, DivTrain, LatestFile, MaxFrameDiff,
LogsPath, BasePath)