def save_for_matconvnet(modelIter=60000, isSlowness=False, concatLayer='fc6', trnSeq=[], convConcat=False): modelIter = modelIter outDir = '/data1/pulkitag/others/joao/' if trnSeq != []: trnStr = 'trnSeq' + ''.join(['-%d' % t for t in trnSeq]) else: trnStr = '' print (trnStr) if not isSlowness: prms = ku.get_prms(poseType='sigMotion', maxFrameDiff=7, imSz=None, isNewExpDir=True, trnSeq=trnSeq) cPrms = get_caffe_prms(concatLayer=concatLayer, convConcat=convConcat) outName = 'kitti-' + trnStr + '-iter' + '%d.mat' % modelIter else: prms = ku.get_prms(poseType='slowness', maxFrameDiff=7, imSz=None, lossType='contrastive', nrmlzType='none', trnSeq=trnSeq) cPrms = get_caffe_prms(concatLayer=concatLayer, contrastiveMargin=1, convConcat=convConcat) outName = 'kitti_slowness_concat%s_ctMrgn%d-%d.mat' % (concatLayer, 1, modelIter) exp = setup_experiment(prms, cPrms) snapFile = exp.get_snapshot_name(modelIter) defFile = '/work4/pulkitag-code/pkgs/caffe-v2-2/modelFiles/kitti/base_files/kitti_finetune_fc6_deploy_input.prototxt' print snapFile net = mp.MyNet(defFile, snapFile) outName = os.path.join(outDir, outName) matlabRefFile = '/home/carreira/imagenet-caffe-alex.mat' mpio.save_weights_for_matconvnet(net, outName, matlabRefFile=matlabRefFile)
def train(poseType, concatLayer, maxFrameDiff, isNewExpDir, trnSeq, convConcat): prms = ku.get_prms(poseType=poseType, concatLayer=concatLayer, isNewExpDir=isNewExpDir, maxFrameDiff=maxFrameDiff, trnSeq=trnSeq, imSz=None) cPrms = kn.get_caffe_prms(concatLayer=concatLayer, convConcat=convConcat) kn.run_experiment(prms, cPrms)
def run_sun_layerwise(deviceId=1,
runNum=2,
addFc=True,
addDrop=True,
fine_base_lr=0.001,
imgntMean=False,
stepsize=20000,
resumeIter=None):
#maxLayers = ['fc6', 'pool5', 'relu4', 'relu3', 'pool2', 'pool1']
#lrAbove = ['fc6', 'conv5', 'conv4', 'conv3', 'conv2', 'conv2']
maxLayers = ['fc6']
lrAbove = ['conv1']
prms = ku.get_prms(poseType='sigMotion',
maxFrameDiff=7,
imSz=None,
isNewExpDir=True)
for mxl, abv in zip(reversed(maxLayers), reversed(lrAbove)):
cPrms = get_caffe_prms(concatLayer='fc6',
fineMaxLayer=mxl,
lrAbove=abv,
fineMaxIter=40000,
deviceId=deviceId,
fineRunNum=runNum,
fine_base_lr=fine_base_lr,
extraFc=addFc,
addDrop=addDrop,
imgntMean=imgntMean)
#exp = make_experiment(prms, cPrms, True, resumeIter=resumeIter)
run_experiment(prms, cPrms, True, resumeIter=resumeIter)
def get_default_prms(netType='kitti', poseType='sigMotion', trnSeq=[]):
'''
netType : 'kitti'- for net trained on kitti
poseType: 'sigMotion' - for training with kitti with description in paper
'slowness' - the slowness n/w - used as baseline in paper
trnSeq: [] - to use the default n/w
[0] - for 25% of data (appx)
[0,8] - for 45% of data (appx)
'''
kwargs = {}
kwargs['concatLayer'] = 'conv5'
kwargs['maxFrameDiff'] = 7
kwargs['imSz'] = None
kwargs['trnSeq'] = trnSeq
kwargs['poseType'] = poseType
kwargs['isNewExpDir'] = False
if 'conv' in kwargs['concatLayer']:
convConcat = True
else:
convConcat = False
if netType == 'kitti':
prms = ku.get_prms(**kwargs)
cPrms = kn.get_caffe_prms(concatLayer='conv5', convConcat=convConcat)
else:
raise Exception('netType not recognized')
return prms, cPrms
def get_default_prms(netType='kitti', poseType='sigMotion', trnSeq=[]):
'''
netType : 'kitti'- for net trained on kitti
poseType: 'sigMotion' - for training with kitti with description in paper
'slowness' - the slowness n/w - used as baseline in paper
trnSeq: [] - to use the default n/w
[0] - for 25% of data (appx)
[0,8] - for 45% of data (appx)
'''
kwargs = {}
kwargs['concatLayer'] = 'conv5'
kwargs['maxFrameDiff'] = 7
kwargs['imSz'] = None
kwargs['trnSeq'] = trnSeq
kwargs['poseType'] = poseType
kwargs['isNewExpDir'] = False
if 'conv' in kwargs['concatLayer']:
convConcat = True
else:
convConcat = False
if netType == 'kitti':
prms = ku.get_prms(**kwargs)
cPrms = kn.get_caffe_prms(concatLayer='conv5', convConcat=convConcat)
else:
raise Exception('netType not recognized')
return prms, cPrms
def run_sun_finetune(deviceId=1,
runNum=2,
addFc=True,
addDrop=True,
fine_base_lr=0.001,
imgntMean=True,
stepsize=20000,
resumeIter=None,
fineMaxIter=100000,
concatLayer='fc6',
sourceModelIter=150000):
#Set the prms
prms = ku.get_prms(poseType='sigMotion',
maxFrameDiff=7,
imSz=None,
isNewExpDir=True)
#Get CaffePrms
cPrms = get_caffe_prms(concatLayer=concatLayer,
sourceModelIter=sourceModelIter,
fineMaxIter=fineMaxIter,
fine_base_lr=fine_base_lr,
extraFc=addFc,
addDrop=addDrop,
fineRunNum=runNum,
deviceId=deviceId,
imgntMean=imgntMean)
run_experiment(prms, cPrms, True, resumeIter)
def make_window_file_rotOnly(trnSeq=[]):
prms = ku.get_prms(poseType='rotOnly',
concatLayer='conv5',
isNewExpDir=True,
maxFrameDiff=7,
trnSeq=trnSeq,
imSz=None)
kn.make_window_file(prms)
def run_sun_scratch(deviceId=1, runNum=1, addDrop=True, addFc=True, fine_base_lr=0.001, imgntMean=False): #I shouldnt be using prms here, but I am. prms = ku.get_prms(poseType='sigMotion', maxFrameDiff=7, imSz=None, isNewExpDir=True) cPrms = get_caffe_prms(concatLayer='fc6', sourceModelIter=None, fineMaxIter=40000, deviceId=deviceId, fineRunNum=runNum, addDrop=addDrop, extraFc=addFc, fine_base_lr=fine_base_lr, imgntMean=imgntMean) run_experiment(prms, cPrms, True)
def train(poseType, concatLayer, maxFrameDiff, isNewExpDir, trnSeq,
convConcat):
prms = ku.get_prms(poseType=poseType,
concatLayer=concatLayer,
isNewExpDir=isNewExpDir,
maxFrameDiff=maxFrameDiff,
trnSeq=trnSeq,
imSz=None)
cPrms = kn.get_caffe_prms(concatLayer=concatLayer, convConcat=convConcat)
kn.run_experiment(prms, cPrms)
def save_for_matconvnet(modelIter=60000,
isSlowness=False,
concatLayer='fc6',
trnSeq=[],
convConcat=False):
modelIter = modelIter
outDir = '/data1/pulkitag/others/joao/'
if trnSeq != []:
trnStr = 'trnSeq' + ''.join(['-%d' % t for t in trnSeq])
else:
trnStr = ''
print(trnStr)
if not isSlowness:
prms = ku.get_prms(poseType='sigMotion',
maxFrameDiff=7,
imSz=None,
isNewExpDir=True,
trnSeq=trnSeq)
cPrms = get_caffe_prms(concatLayer=concatLayer, convConcat=convConcat)
outName = 'kitti-' + trnStr + '-iter' + '%d.mat' % modelIter
else:
prms = ku.get_prms(poseType='slowness',
maxFrameDiff=7,
imSz=None,
lossType='contrastive',
nrmlzType='none',
trnSeq=trnSeq)
cPrms = get_caffe_prms(concatLayer=concatLayer,
contrastiveMargin=1,
convConcat=convConcat)
outName = 'kitti_slowness_concat%s_ctMrgn%d-%d.mat' % (concatLayer, 1,
modelIter)
exp = setup_experiment(prms, cPrms)
snapFile = exp.get_snapshot_name(modelIter)
defFile = '/work4/pulkitag-code/pkgs/caffe-v2-2/modelFiles/kitti/base_files/kitti_finetune_fc6_deploy_input.prototxt'
print snapFile
net = mp.MyNet(defFile, snapFile)
outName = os.path.join(outDir, outName)
matlabRefFile = '/home/carreira/imagenet-caffe-alex.mat'
mpio.save_weights_for_matconvnet(net, outName, matlabRefFile=matlabRefFile)
def run_sun_finetune(deviceId=1, runNum=2, addFc=True, addDrop=True, fine_base_lr=0.001, imgntMean=True, stepsize=20000, resumeIter=None, fineMaxIter=100000, concatLayer='fc6', sourceModelIter=150000): #Set the prms prms = ku.get_prms(poseType='sigMotion', maxFrameDiff=7, imSz=None, isNewExpDir=True) #Get CaffePrms cPrms = get_caffe_prms(concatLayer=concatLayer, sourceModelIter=sourceModelIter, fineMaxIter=fineMaxIter, fine_base_lr=fine_base_lr, extraFc=addFc, addDrop=addDrop, fineRunNum=runNum, deviceId=deviceId, imgntMean=imgntMean) run_experiment(prms, cPrms, True, resumeIter)
def run_sun_layerwise(deviceId=1, runNum=2, addFc=True, addDrop=True, fine_base_lr=0.001, imgntMean=False, stepsize=20000, resumeIter=None): #maxLayers = ['fc6', 'pool5', 'relu4', 'relu3', 'pool2', 'pool1'] #lrAbove = ['fc6', 'conv5', 'conv4', 'conv3', 'conv2', 'conv2'] maxLayers = ['fc6'] lrAbove = ['conv1'] prms = ku.get_prms(poseType='sigMotion', maxFrameDiff=7, imSz=None, isNewExpDir=True) for mxl, abv in zip(reversed(maxLayers), reversed(lrAbove)): cPrms = get_caffe_prms(concatLayer='fc6', fineMaxLayer=mxl, lrAbove=abv, fineMaxIter=40000, deviceId=deviceId, fineRunNum=runNum, fine_base_lr = fine_base_lr, extraFc = addFc, addDrop = addDrop, imgntMean = imgntMean) #exp = make_experiment(prms, cPrms, True, resumeIter=resumeIter) run_experiment(prms, cPrms, True, resumeIter=resumeIter)
def run_sun_scratch(deviceId=1,
runNum=1,
addDrop=True,
addFc=True,
fine_base_lr=0.001,
imgntMean=False):
#I shouldnt be using prms here, but I am.
prms = ku.get_prms(poseType='sigMotion',
maxFrameDiff=7,
imSz=None,
isNewExpDir=True)
cPrms = get_caffe_prms(concatLayer='fc6',
sourceModelIter=None,
fineMaxIter=40000,
deviceId=deviceId,
fineRunNum=runNum,
addDrop=addDrop,
extraFc=addFc,
fine_base_lr=fine_base_lr,
imgntMean=imgntMean)
run_experiment(prms, cPrms, True)
def finetune_odometry(netType='alex', addFc=False, addDrop=False, imgntMean=True,
concatLayer='conv5', convConcat=True, fine_base_lr=0.001,
contextPad=16, deviceId=0, kittiTrnSeq=[]):
prms = get_prms()
cPrms = get_caffe_prms(concatLayer=concatLayer, convConcat=convConcat,
contextPad=contextPad, extraFc=addFc, addDrop=addDrop,
imgntMean=imgntMean, fine_base_lr=0.001, deviceId=deviceId)
if kittiTrnSeq==[]:
trnStr = ''
else:
trnStr = '_trnSeq' + ''.join('-%d' % t for t in kittiTrnSeq)
prms['expName'] = prms['expName'] + 'fine_from_' + netType + trnStr
if netType=='alex':
srcModelFile = '/data1/pulkitag/caffe_models/caffe_imagenet_train_iter_310000'
elif netType == 'kitti':
srcPrms = ku.get_prms(poseType='sigMotion', maxFrameDiff=7,
lossType='classify', imSz=None, trnSeq=kittiTrnSeq)
srcCPrms = kn.get_caffe_prms(concatLayer='conv5', convConcat=True)
srcExp = kn.setup_experiment(srcPrms, srcCPrms)
srcModelFile = srcExp.get_snapshot_name(60000)
print (srcModelFile)
def make_window_file_rotOnly(trnSeq=[]): prms = ku.get_prms(poseType='rotOnly', concatLayer='conv5', isNewExpDir=True, maxFrameDiff=7, trnSeq=trnSeq, imSz=None) kn.make_window_file(prms)
def run_sun_layerwise_small(deviceId=0,
runNum=1,
fineNumData=10,
addFc=True,
addDrop=True,
sourceModelIter=150000,
imgntMean=True,
concatLayer='fc6',
resumeIter=None,
fine_base_lr=0.001,
runType='run',
convConcat=False,
prms=None,
srcDefFile=None,
srcModelFile=None,
isMySimple=False,
contrastiveMargin=None,
maxLayers=None,
trnSeq=[]):
#Set the prms
if prms is None:
print('PRMS IS NONE - creating it', trnSeq)
prms = ku.get_prms(poseType='sigMotion',
maxFrameDiff=7,
imSz=None,
isNewExpDir=True,
trnSeq=trnSeq)
print(prms['trnSeq'])
imSz = prms['imSz']
if imSz is None:
imSz, testImSz, testCrpSz = 227, 256, 227
else:
imSz, testImSz, testCrpSz = 128, 128, 112
acc = {}
if maxLayers is None:
if isMySimple:
maxLayers = ['relu1', 'relu2', 'relu3', 'relu4']
else:
#maxLayers = ['pool1', 'pool2','relu3','relu4','pool5', 'fc6']
maxLayers = ['pool2', 'relu3', 'relu4', 'pool5']
#maxLayers = ['pool1']
else:
assert type(maxLayers) == list
if addFc:
lrAbove = ['fc-extra'] * len(maxLayers)
else:
lrAbove = ['class_fc'] * len(maxLayers)
for mxl, abv in zip(maxLayers, lrAbove):
#Get CaffePrms
cPrms = get_caffe_prms(concatLayer=concatLayer,
sourceModelIter=sourceModelIter,
fineMaxIter=None,
stepsize=None,
fine_base_lr=fine_base_lr,
extraFc=addFc,
addDrop=addDrop,
fineMaxLayer=mxl,
lrAbove=abv,
fineRunNum=runNum,
fineNumData=fineNumData,
deviceId=deviceId,
imgntMean=imgntMean,
convConcat=convConcat,
imSz=imSz,
isMySimple=isMySimple,
contrastiveMargin=contrastiveMargin)
if runType == 'run':
isExist = find_experiment(prms, cPrms, srcDefFile=srcDefFile)
#pdb.set_trace()
if isExist:
print 'EXPERIMENT FOUND, SKIPPING'
continue
else:
run_experiment(prms,
cPrms,
True,
resumeIter,
srcDefFile=srcDefFile,
srcModelFile=srcModelFile)
elif runType == 'test':
run_test(prms,
cPrms,
imH=testImSz,
imW=testImSz,
cropH=testCrpSz,
cropW=testCrpSz,
srcDefFile=srcDefFile)
elif runType == 'accuracy':
acc[mxl] = read_accuracy(prms, cPrms)
if runType == 'accuracy':
return acc, maxLayers
def run_sun_layerwise_small(deviceId=0, runNum=1, fineNumData=10,
addFc=True, addDrop=True,
sourceModelIter=150000, imgntMean=True, concatLayer='fc6',
resumeIter=None, fine_base_lr=0.001, runType='run',
convConcat=False,
prms=None, srcDefFile=None, srcModelFile=None,
isMySimple=False, contrastiveMargin=None,
maxLayers=None, trnSeq=[]):
#Set the prms
if prms is None:
print ('PRMS IS NONE - creating it', trnSeq)
prms = ku.get_prms(poseType='sigMotion', maxFrameDiff=7,
imSz=None, isNewExpDir=True, trnSeq=trnSeq)
print (prms['trnSeq'])
imSz = prms['imSz']
if imSz is None:
imSz, testImSz, testCrpSz = 227, 256, 227
else:
imSz, testImSz, testCrpSz = 128, 128, 112
acc = {}
if maxLayers is None:
if isMySimple:
maxLayers = ['relu1', 'relu2','relu3','relu4']
else:
#maxLayers = ['pool1', 'pool2','relu3','relu4','pool5', 'fc6']
maxLayers = ['pool2','relu3','relu4', 'pool5']
#maxLayers = ['pool1']
else:
assert type(maxLayers) == list
if addFc:
lrAbove = ['fc-extra'] * len(maxLayers)
else:
lrAbove = ['class_fc'] * len(maxLayers)
for mxl, abv in zip(maxLayers, lrAbove):
#Get CaffePrms
cPrms = get_caffe_prms(concatLayer=concatLayer, sourceModelIter=sourceModelIter,
fineMaxIter=None, stepsize=None, fine_base_lr=fine_base_lr,
extraFc=addFc, addDrop=addDrop,
fineMaxLayer=mxl, lrAbove=abv,
fineRunNum=runNum, fineNumData=fineNumData,
deviceId=deviceId, imgntMean=imgntMean,
convConcat=convConcat, imSz=imSz,
isMySimple=isMySimple, contrastiveMargin=contrastiveMargin)
if runType =='run':
isExist = find_experiment(prms, cPrms, srcDefFile=srcDefFile)
#pdb.set_trace()
if isExist:
print 'EXPERIMENT FOUND, SKIPPING'
continue
else:
run_experiment(prms, cPrms, True, resumeIter,
srcDefFile=srcDefFile, srcModelFile=srcModelFile)
elif runType == 'test':
run_test(prms, cPrms, imH=testImSz, imW=testImSz,
cropH=testCrpSz, cropW=testCrpSz,
srcDefFile=srcDefFile)
elif runType == 'accuracy':
acc[mxl] = read_accuracy(prms,cPrms)
if runType=='accuracy':
return acc, maxLayers