def ptch_pose_exp2(isRun=False,
deviceId=[1],
numPoseStream=256,
numPatchStream=256):
prms = sp.get_prms(geoFence='dc-v1',
labels=['pose', 'ptch'],
labelType=['quat', 'wngtv'],
lossType=['l2', 'classify'],
labelFrac=[0.5, 0.5],
rawImSz=256,
crpSz=192,
splitDist=100)
nPrms = se.get_nw_prms(imSz=101,
netName='smallnet-v2',
concatLayer='pool4',
lossWeight=10.0,
multiLossProto='v1',
ptchStreamNum=numPatchStream,
poseStreamNum=numPoseStream)
lPrms = se.get_lr_prms(batchsize=256, stepsize=10000, clip_gradients=10.0)
cPrms = se.get_caffe_prms(nPrms, lPrms, deviceId=deviceId)
if isRun:
exp = se.make_experiment(prms, cPrms)
exp.run()
else:
return prms, cPrms
def ptch_pose_euler_mx45_exp1_from_ptch(isRun=False, deviceId=[1], numTrain=1e+7, batchsize=256, extraFc=None, isPythonLayer=True, poseModelIter=10000): #srcPrms, srcCPrms = smallnetv2_pool4_pose_euler_mx45_crp192_rawImSz256(isRun=False, # isPythonLayer=True, extraFc=512) srcPrms, srcCPrms = mept.smallnetv2_pool4_ptch_crp192_rawImSz256(isRun=False, isPythonLayer=True) prms = sp.get_prms(geoFence='dc-v2', labels=['pose', 'ptch'], labelType=['euler', 'wngtv'], lossType=['l2', 'classify'], labelFrac=[0.5,0.5], rawImSz=256, crpSz=192, splitDist=100, numTrain=numTrain, maxEulerRot=45, nBins=[None, None], binTypes=[None, None]) nPrms = se.get_nw_prms(imSz=101, netName='smallnet-v2', concatLayer='pool4', lossWeight=10.0, multiLossProto=None, extraFc=extraFc, isPythonLayer=isPythonLayer) lPrms = se.get_lr_prms(batchsize=batchsize, stepsize=20000, clip_gradients=10.0) cPrms = se.get_caffe_prms(nPrms, lPrms, deviceId=deviceId) exp = se.make_experiment_from_previous(srcPrms, srcCPrms, prms, cPrms, srcModelIter=poseModelIter) if isRun: exp.run() return prms, cPrms
def smallnetv2_pool4_pose_euler_mx45_crp192_rawImSz256(isRun=False,
numTrain=1e+7,
deviceId=[0],
isPythonLayer=False,
isGray=False,
extraFc=None,
resumeIter=0):
prms = sp.get_prms(geoFence='dc-v2',
labels=['pose'],
labelType=['euler'],
lossType=['l2'],
maxEulerRot=45,
rawImSz=256,
splitDist=100,
numTrain=numTrain,
crpSz=192)
nPrms = se.get_nw_prms(imSz=101,
netName='smallnet-v2',
concatLayer='pool4',
lossWeight=10.0,
randCrop=False,
concatDrop=False,
isGray=isGray,
isPythonLayer=isPythonLayer,
extraFc=extraFc)
lPrms = se.get_lr_prms(batchsize=256, stepsize=10000, clip_gradients=10.0)
cPrms = se.get_caffe_prms(nPrms,
lPrms,
deviceId=deviceId,
resumeIter=resumeIter)
if isRun:
exp = se.make_experiment(prms, cPrms)
exp.run()
return prms, cPrms
def smallnetv2_pool4_pose_crp192_rawImSz256(isRun=False,
isGray=False,
numTrain=1e+7,
deviceId=[0],
isPythonLayer=False,
runNum=0):
prms = sp.get_prms_pose(geoFence='dc-v2',
crpSz=192,
rawImSz=256,
splitDist=100,
numTrain=numTrain)
nPrms = se.get_nw_prms(imSz=101,
netName='smallnet-v2',
concatLayer='pool4',
lossWeight=10.0,
randCrop=False,
concatDrop=False,
isGray=isGray,
isPythonLayer=isPythonLayer)
lPrms = se.get_lr_prms(batchsize=256, stepsize=10000, clip_gradients=1.0)
cPrms = se.get_caffe_prms(nPrms, lPrms, deviceId=deviceId, runNum=runNum)
if isRun:
exp = se.make_experiment(prms, cPrms)
exp.run()
else:
return prms, cPrms
def smallnetv5_pose_crp192_fc5_rawImSz256(isRun=False,
isGray=False,
numTrain=1e+7,
deviceId=[0],
isPythonLayer=True,
runNum=0,
extraFc=None,
numFc5=None,
lrAbove=None):
prms = sp.get_prms_pose(geoFence='dc-v2',
crpSz=192,
rawImSz=256,
splitDist=100,
numTrain=numTrain)
nPrms = se.get_nw_prms(imSz=101,
netName='smallnet-v5',
concatLayer='fc5',
lossWeight=10.0,
randCrop=False,
concatDrop=False,
isGray=isGray,
isPythonLayer=isPythonLayer,
numFc5=numFc5,
extraFc=extraFc,
lrAbove=lrAbove)
lPrms = se.get_lr_prms(batchsize=256, stepsize=10000, clip_gradients=1.0)
cPrms = se.get_caffe_prms(nPrms, lPrms, deviceId=deviceId, runNum=runNum)
if isRun:
exp = se.make_experiment(prms, cPrms)
exp.run()
return prms, cPrms
def ptch_pose_euler_mx90_smallnet_v7_fc5_exp2(isRun=False, deviceId=[1], numTrain=1e+7, batchsize=256, extraFc=None, isPythonLayer=True, numFc5=None, numCommonFc=None, numPoseStream=256, numPatchStream=256): #Experiment prms prms = sp.get_prms(geoFence='dc-v2', labels=['pose', 'ptch'], labelType=['euler', 'wngtv'], lossType=['l2', 'classify'], labelFrac=[0.5,0.5], rawImSz=256, crpSz=192, splitDist=100, numTrain=numTrain, maxEulerRot=90, nBins=[None, None], binTypes=[None, None]) #Network architectures nPrms = se.get_nw_prms(imSz=101, netName='smallnet-v7', concatLayer='fc5', lossWeight=10.0, multiLossProto='v1', extraFc=extraFc, isPythonLayer=isPythonLayer, numFc5=numFc5, numCommonFc=numCommonFc, poseStreamNum=numPoseStream, ptchStreamNum=numPatchStream) #Learning rate info lPrms = se.get_lr_prms(batchsize=batchsize, stepsize=20000, clip_gradients=10.0, debug_info=True) cPrms = se.get_caffe_prms(nPrms, lPrms, deviceId=deviceId) if isRun: exp = se.make_experiment(prms, cPrms) exp.run() return prms, cPrms
def smallnetv5_fc5_pose_euler_crp192_rawImSz256_lossl1(isRun=False,
numTrain=1e+7,
deviceId=[0],
isPythonLayer=True,
isGray=False,
extraFc=None,
lrAbove=None,
numFc5=None):
prms = sp.get_prms(geoFence='dc-v2',
labels=['pose'],
labelType=['euler'],
lossType=['l1'],
maxEulerRot=None,
rawImSz=256,
splitDist=100,
numTrain=numTrain,
crpSz=192)
nPrms = se.get_nw_prms(imSz=101,
netName='smallnet-v5',
concatLayer='fc5',
lossWeight=10.0,
randCrop=False,
concatDrop=False,
isGray=isGray,
isPythonLayer=isPythonLayer,
extraFc=extraFc,
numFc5=numFc5,
lrAbove=lrAbove)
lPrms = se.get_lr_prms(batchsize=256, stepsize=10000, clip_gradients=10.0)
cPrms = se.get_caffe_prms(nPrms, lPrms, deviceId=deviceId)
if isRun:
exp = se.make_experiment(prms, cPrms)
exp.run()
return prms, cPrms
def smallnetv2_pool4_pose_randcrp(isRun=False): prms = sp.get_prms_pose(geoFence='dc-v1') nPrms = se.get_nw_prms(imSz=101, netName='smallnet-v2', concatLayer='pool4', randCrop=True) lPrms = se.get_lr_prms(batchsize=256, stepsize=5000) cPrms = se.get_caffe_prms(nPrms, lPrms, deviceId=[0,1,2,3]) if isRun: exp = se.make_experiment(prms, cPrms) exp.run() else: return prms, cPrms
def smallnetv3_pool4_pose_crp192(isRun=False): prms = sp.get_prms_pose(geoFence='dc-v1', crpSz=192) nPrms = se.get_nw_prms(imSz=101, netName='smallnet-v3', concatLayer='pool4', lossWeight=10.0) lPrms = se.get_lr_prms(batchsize=256, stepsize=10000, clip_gradients=10.0) cPrms = se.get_caffe_prms(nPrms, lPrms, deviceId=[0]) if isRun: exp = se.make_experiment(prms, cPrms) exp.run() else: return prms, cPrms
def smallnet_pool4_pose(isRun=False): prms = sp.get_prms_pose(geoFence='dc-v1') nPrms = se.get_nw_prms(imSz=101, netName='smallnet', concatLayer='pool4') lPrms = se.get_lr_prms(batchsize=256) cPrms = se.get_caffe_prms(nPrms, lPrms, deviceId=[0,1]) if isRun: exp = se.make_experiment(prms, cPrms) exp.run() else: return prms, cPrms
def smallnetv3_pool4_pose_euler_mx45_crp192(isRun=False): prms = sp.get_prms(geoFence='dc-v1', labels=['pose'], labelType=['euler'], lossType=['l2'], maxEulerRot=45, crpSz=192) nPrms = se.get_nw_prms(imSz=101, netName='smallnet-v3', concatLayer='pool4', lossWeight=10.0) lPrms = se.get_lr_prms(batchsize=256, clip_gradients=1.0) cPrms = se.get_caffe_prms(nPrms, lPrms, deviceId=[1]) if isRun: exp = se.make_experiment(prms, cPrms) exp.run() else: return prms, cPrms
def smallnetv2_pool4_ptch_pose_crp192_rawImSz256(isRun=False): prms = sp.get_prms(geoFence='dc-v1', labels=['pose', 'ptch'], labelType=['quat', 'wngtv'], lossType=['l2', 'classify'], labelFrac=[0.5,0.5], rawImSz=256, crpSz=192) nPrms = se.get_nw_prms(imSz=101, netName='smallnet-v2', concatLayer='pool4', lossWeight=10.0) lPrms = se.get_lr_prms(batchsize=256, stepsize=10000, clip_gradients=10.0) cPrms = se.get_caffe_prms(nPrms, lPrms, deviceId=[1]) if isRun: exp = se.make_experiment(prms, cPrms) exp.run() else: return prms, cPrms
def smallnetv2_pool4_nrml_crp192_rawImSz256_newsplits(isRun=False): prms = sp.get_prms(geoFence='dc-v1', labels=['nrml'], labelType=['xyz'], lossType=['l2'], rawImSz=256, crpSz=192) nPrms = se.get_nw_prms(imSz=101, netName='smallnet-v2', concatLayer='pool4', lossWeight=10.0, maxJitter=0) lPrms = se.get_lr_prms(batchsize=256, stepsize=10000, clip_gradients=10.0) cPrms = se.get_caffe_prms(nPrms, lPrms, deviceId=[1]) if isRun: exp = se.make_experiment(prms, cPrms) exp.run() else: return prms, cPrms
def smallnetv2_pool4_pose_crp192_rawImSz256_newsplits(isRun=False, isGray=False): prms = sp.get_prms_pose(geoFence='dc-v1', crpSz=192, rawImSz=256, splitDist=100) nPrms = se.get_nw_prms(imSz=101, netName='smallnet-v2', concatLayer='pool4', lossWeight=10.0, randCrop=False, concatDrop=False, isGray=isGray) lPrms = se.get_lr_prms(batchsize=256, stepsize=10000, clip_gradients=10.0) cPrms = se.get_caffe_prms(nPrms, lPrms, deviceId=[1]) if isRun: exp = se.make_experiment(prms, cPrms) exp.run() else: return prms, cPrms
def smallnetv2_pool4_ptch_crp192_rawImSz256(isRun=False, isGray=False, numTrain=1e+7, isPythonLayer=False, deviceId=[2], batchsize=256, resumeIter=0, extraFc=None, lrAbove=None): prms = sp.get_prms_ptch(geoFence='dc-v2', crpSz=192, rawImSz=256, splitDist=100, numTrain=numTrain) nPrms = se.get_nw_prms(imSz=101, netName='smallnet-v2', concatLayer='pool4', lossWeight=10.0, randCrop=False, concatDrop=False, isGray=isGray, isPythonLayer=isPythonLayer, extraFc=extraFc, lrAbove=lrAbove) lPrms = se.get_lr_prms(batchsize=batchsize, stepsize=10000, clip_gradients=10.0) cPrms = se.get_caffe_prms(nPrms, lPrms, deviceId=deviceId, resumeIter=resumeIter) if isRun: exp = se.make_experiment(prms, cPrms) exp.run() return prms, cPrms
def smallnetv2_pool4_nrml_crp192_rawImSz256_nojitter(isRun=False, isGray=False, numTrain=1e+7, deviceId=[0], makeNrmlUni=0.002, isPythonLayer=True): prms = sp.get_prms_nrml(geoFence='dc-v2', crpSz=192, rawImSz=256, splitDist=100, numTrain=numTrain, nrmlMakeUni=makeNrmlUni) nPrms = se.get_nw_prms(imSz=101, netName='smallnet-v2', concatLayer='pool4', lossWeight=10.0, randCrop=False, concatDrop=False, isGray=isGray, maxJitter=0, isPythonLayer=isPythonLayer) lPrms = se.get_lr_prms(batchsize=256, stepsize=10000, clip_gradients=10.0, debug_info=True) cPrms = se.get_caffe_prms(nPrms, lPrms, deviceId=deviceId) if isRun: exp = se.make_experiment(prms, cPrms) exp.run() else: return prms, cPrms
def ptch_pose_euler_mx90_alexnet_exp1(isRun=False, deviceId=[1], numTrain=1e+7, batchsize=256, extraFc=None, isPythonLayer=True): prms = sp.get_prms(geoFence='dc-v2', labels=['pose', 'ptch'], labelType=['euler', 'wngtv'], lossType=['l2', 'classify'], labelFrac=[0.5,0.5], rawImSz=256, crpSz=192, splitDist=100, numTrain=numTrain, maxEulerRot=90, nBins=[None, None], binTypes=[None, None]) nPrms = se.get_nw_prms(imSz=101, netName='alexnet', concatLayer='conv5', lossWeight=10.0, multiLossProto=None, extraFc=extraFc, isPythonLayer=isPythonLayer) lPrms = se.get_lr_prms(batchsize=batchsize, stepsize=20000, clip_gradients=10.0) cPrms = se.get_caffe_prms(nPrms, lPrms, deviceId=deviceId) if isRun: exp = se.make_experiment(prms, cPrms) exp.run() else: return prms, cPrms
def smallnetv5_fc5_ptch_crp192_rawImSz256(isRun=False, isGray=False, numTrain=1e+7, isPythonLayer=True, deviceId=[2], batchsize=256, resumeIter=0, extraFc=None, numFc5=512, runNum=0, lrAbove=None, geoFence='dc-v2', numTest=1e+4): prms = sp.get_prms_ptch(geoFence=geoFence, crpSz=192, rawImSz=256, splitDist=100, numTrain=numTrain, numTest=numTest) nPrms = se.get_nw_prms(imSz=101, netName='smallnet-v5', concatLayer='fc5', lossWeight=10.0, randCrop=False, concatDrop=False, isGray=isGray, isPythonLayer=isPythonLayer, extraFc=extraFc, numFc5=numFc5, lrAbove=lrAbove) lPrms = se.get_lr_prms(batchsize=batchsize, stepsize=10000, clip_gradients=10.0, debug_info=True) cPrms = se.get_caffe_prms(nPrms, lPrms, deviceId=deviceId, resumeIter=resumeIter, runNum=runNum) if isRun: exp = se.make_experiment(prms, cPrms) exp.run() return prms, cPrms
def smallnetv5_pool4_pose_classify_euler_crp192_rawImSz256(
isRun=False,
numTrain=1e+7,
deviceId=[0],
isPythonLayer=True,
isGray=False,
numFc5=512):
prms = sp.get_prms(geoFence='dc-v2',
labels=['pose'],
labelType=['euler'],
lossType=['classify'],
nBins=[20],
binTypes=['uniform'],
maxEulerRot=None,
rawImSz=256,
splitDist=100,
numTrain=numTrain,
crpSz=192)
nPrms = se.get_nw_prms(imSz=101,
netName='smallnet-v5',
concatLayer='fc5',
lossWeight=10.0,
randCrop=False,
concatDrop=False,
isGray=isGray,
isPythonLayer=isPythonLayer,
numFc5=numFc5)
lPrms = se.get_lr_prms(batchsize=256,
stepsize=10000,
clip_gradients=10.0,
debug_info=True)
cPrms = se.get_caffe_prms(nPrms, lPrms, deviceId=deviceId)
if isRun:
exp = se.make_experiment(prms, cPrms)
exp.run()
return prms, cPrms