def main():
parser = argparse.ArgumentParser(description='Visualizes a trained StackedLayers model.')
parser.add_argument('--name', type = str, default = 'junk',
help = 'Name for GitResultsManager results directory (default: junk)')
#parser.add_argument('--quick', action='store_true', help = 'Enable quick mode (default: off)')
parser.add_argument('--nodiary', action='store_true', help = 'Disable diary (default: diary is on)')
parser.add_argument('stackedLayersFilename', type = str,
help = 'File where a StackedLayers model was stored, something like stackedLayers.pkl.gz')
parser.add_argument('command', type = str, default = 'embed', choices = ['visall', 'embed'], nargs='?',
help = 'What to do: one of {visall (save all plots), embed (drop into shell)}. Default: embed.')
args = parser.parse_args()
resman.start(args.name, diary = not args.nodiary)
saveDir = resman.rundir
print 'Loading StackedLayers from %s' % args.stackedLayersFilename
sl = loadFromPklGz(args.stackedLayersFilename)
print 'Loaded these StackedLayers:'
sl.printStatus()
if args.command == 'embed':
embed()
elif args.command == 'visall':
sl.visAll(saveDir)
else:
print 'Unknown command:', args.command
resman.stop()
def main():
resman.start('junk', diary = True)
params = {}
randomParams = False
if randomParams:
params['hiddenISize'] = random.choice((2, 4, 6, 8, 10, 15, 20))
params['hiddenJSize'] = params['hiddenISize']
params['neighborhoodSize'] = random.choice((.1, .3, .5, .7, 1.0, 1.5, 2.0, 2.5, 3.5, 5.0))
lambd = exp(random.uniform(log(.0001), log(10))) # Uniform in log space
params['lambd'] = round(lambd, 1-int(floor(log10(lambd)))) # Just keep two significant figures
params['randSeed'] = int(random.uniform(0,9999))
#params['dataWidth'] = random.choice((2, 4)) # just quick
#params['dataWidth'] = random.choice((2, 3, 4, 6, 10, 15, 20, 25, 28))
params['dataWidth'] = random.choice((2, 3, 4, 6, 10, 15, 20)) # 25 and 28 are incomplete
params['nColors'] = random.choice((1, 3))
else:
params['hiddenISize'] = 15
params['hiddenJSize'] = params['hiddenISize']
params['neighborhoodSize'] = 1.0
params['lambd'] = .026
params['randSeed'] = 22
#params['dataWidth'] = random.choice((2, 4)) # just quick
#params['dataWidth'] = random.choice((2, 3, 4, 6, 10, 15, 20, 25, 28))
params['dataWidth'] = 10
params['nColors'] = 3
params['isColor'] = (params['nColors'] == 3)
params['imgShape'] = ((params['dataWidth'], params['dataWidth'], 3)
if params['isColor'] else
(params['dataWidth'], params['dataWidth']))
params['maxFuncCalls'] = 300
params['whiten'] = True # Just false for Space Invaders dataset...
params['dataCrop'] = None # Set to None to not crop data...
paramsRand = params.copy()
paramsRand['dataLoader'] = 'loadRandomData'
paramsRand['dataPath'] = ('../data/random/randomu01_train_%02d_50000_%dc.pkl.gz'
% (paramsRand['dataWidth'], paramsRand['nColors']))
paramsData = params.copy()
#paramsData['dataLoader'] = 'loadAtariData'
#paramsData['dataPath'] = ('../data/atari/space_invaders_train_%02d_50000_%dc.pkl.gz'
# % (paramsData['dataWidth'], paramsData['nColors']))
paramsData['dataLoader'] = 'loadUpsonData3'
paramsData['dataPath'] = ('../data/upson_rovio_3/train_%02d_50000_%dc.pkl.gz'
% (paramsData['dataWidth'], paramsData['nColors']))
doRand = False
if doRand:
#resultsRand = reliablyRunTest((0, resman.rundir, '00000_rand', paramsRand, os.getcwd(), os.getenv('DISPLAY','')))
randResultsDir = os.makedirs(resman.rundir, 'rand')
runTest(randResultsDir, paramsRand)
#resultsData = reliablyRunTest((0, resman.rundir, '00000_data', paramsData, os.getcwd(), os.getenv('DISPLAY','')))
runTest(resman.rundir, paramsData)
resman.stop()
def main():
parser = argparse.ArgumentParser(description='Trains a StackedLayers model.')
parser.add_argument('layerFilename', type = str,
help = 'File defining layers, something like tica-10-15.layers')
parser.add_argument('trainParamsFilename', type = str,
help = 'File defining training parameters, something like tica-10-15.trainparams')
parser.add_argument('--name', type = str, default = 'junk',
help = 'Name for GitResultsManager results directory (default: junk)')
parser.add_argument('--load', type = str, default = '',
help = ('Load a previously created StackedLayers object. This can ' +
'be used to resume training a previously checkpointed, ' +
'partially trained StackedLayers object (default: none)'))
parser.add_argument('--maxlayer', type = int, default = -1,
help = ('Maximum layer to train, -1 to train all. Can be used to train' +
'only a subset of layers in an otherwise deeper model. Default: -1.'))
parser.add_argument('--quick', action='store_true', help = 'Enable quick mode (default: off)')
parser.add_argument('--nodiary', action='store_true', help = 'Disable diary (default: diary is on)')
args = parser.parse_args()
resman.start(args.name, diary = not args.nodiary)
saveDir = resman.rundir
layerDefinitions = importFromFile(args.layerFilename, 'layers')
trainParams = importFromFile(args.trainParamsFilename, 'trainParams')
shutil.copyfile(args.layerFilename, os.path.join(saveDir, 'params.layers'))
shutil.copyfile(args.trainParamsFilename, os.path.join(saveDir, 'params.trainparams'))
# Experiment: train a few Stacked Layers with different ticas
assert not args.load, 'Loading does not make sense here.'
sls = []
for ii, hiddenWidth in enumerate((8,12,16,20,24,28,32,36,40)):
treatmentDir = os.path.join(saveDir, 'treatment_%02d_%d' % (ii, hiddenWidth))
os.mkdir(treatmentDir)
print '\n' * 4 + '*' * 40
print 'Treatment %d, width %d (results in %s)' % (ii, hiddenWidth, treatmentDir)
print '*' * 40
assert layerDefinitions[2]['type'] == 'tica'
layerDefinitions[2]['hiddenSize'] = (hiddenWidth, hiddenWidth)
print 'Creating new StackedLayers object'
sl = StackedLayers(layerDefinitions)
sl.printStatus()
sl.train(trainParams, saveDir = treatmentDir, quick = args.quick, maxlayer = args.maxlayer)
sls.append(sl)
fileFinal = os.path.join(saveDir, 'multiStackedLayers.pkl.gz')
saveToFile(fileFinal, sls)
resman.stop()
def main():
dirs = [name for name in os.listdir('results') if os.path.isdir(os.path.join('results', name))]
print 'last few results:'
for dir in sorted(dirs)[-10:]:
print ' ' + dir
resman.start('junk', diary = False)
saveDir = resman.rundir
embed()
resman.stop()
def main():
resman.start('junk', diary = False)
#l1tica = loadFromPklGz('results/130402_033310_44cc757_master_psearchTica_UP/00022_data/tica.pkl.gz')
l1tica = loadFromPklGz('results/130406_184751_3d90386_rapidhacks_upson3_1c_l1/tica.pkl.gz') # 1c Upson3
#layer1Whitener = loadFromPklGz('../data/upson_rovio_2/white/train_10_50000_1c.whitener.pkl.gz')
layer1Whitener = loadFromPklGz('../data/upson_rovio_3/white/train_10_50000_1c.whitener.pkl.gz')
layerSizePlan = [10, 15, 23, 35, 53, 80, 120, 180]
stackedTica = StackedTICA(l1tica, layer1Whitener, '../data/upson_rovio_3/imgfiles/',
layerSizePlan = layerSizePlan,
isColor = False,
saveDir = resman.rundir)
if False:
print 'JUST DEBUG...'
pdb.set_trace()
tica = stackedTica.ticas[0]
pdb.set_trace()
return
#data,labels,strings = loadUpsonData3('../data/upson_rovio_3/train_10_50000_1c.pkl.gz')
#data = loadFromPklGz('../data/upson_rovio_3/white/train_10_50000_1c.white.pkl.gz')
#stackedTica.plotResults(resman.rundir, stackedTica.ticas[0], data, (10,10), (15,15))
#pdb.set_trace()
params = {}
params['hiddenISize'] = 15
params['hiddenJSize'] = params['hiddenISize']
params['neighborhoodSize'] = 1.0
params['lambd'] = .026
params['randSeed'] = 0
#params['dataWidth'] = 10
#params['nColors'] = 1
#params['isColor'] = (params['nColors'] == 3)
#params['imgShape'] = ((params['dataWidth'], params['dataWidth'], 3)
# if params['isColor'] else
# (params['dataWidth'], params['dataWidth']))
params['maxFuncCalls'] = 3
#params['whiten'] = True # Just false for Space Invaders dataset...
params['dataCrop'] = None # Set to None to not crop data...
#params['dataCrop'] = 10000 # Set to None to not crop data...
stackedTica.learnNextLayer(params)
#print 'HACK FOR DEBUG'
saveToFile(os.path.join(resman.rundir, 'stackedTica.pkl.gz'), stackedTica) # save learned model
resman.stop()
def main():
print('This is not logged')
resman.start('demo-GRM-module-run')
for ii in range(3):
print('This is logged', ii)
print('This is logged (to stderr)', ii, file=sys.stderr)
sleep(1)
with open(resman.rundir + '/output_file_1.txt', 'w') as ff:
ff.write('test output to file in results directory\n')
resman.stop()
print('Run finished')
def main():
print('This is not logged')
resman.start('demo-GRM-module-run')
for ii in range(3):
print('This is logged', ii)
print('This is logged (to stderr)', ii, file=sys.stderr)
sleep(1)
with open(resman.rundir + '/output_file_1.txt', 'w') as ff:
ff.write('test output to file in results directory\n')
resman.stop()
print('Run finished')
def main():
dirs = [
name for name in os.listdir('results')
if os.path.isdir(os.path.join('results', name))
]
print 'last few results:'
for dir in sorted(dirs)[-10:]:
print ' ' + dir
resman.start('junk', diary=False)
saveDir = resman.rundir
embed()
resman.stop()
def main():
parser = argparse.ArgumentParser(
description='Visualizes a trained StackedLayers model.')
parser.add_argument(
'--name',
type=str,
default='junk',
help='Name for GitResultsManager results directory (default: junk)')
#parser.add_argument('--quick', action='store_true', help = 'Enable quick mode (default: off)')
parser.add_argument('--nodiary',
action='store_true',
help='Disable diary (default: diary is on)')
parser.add_argument(
'stackedLayersFilename',
type=str,
help=
'File where a StackedLayers model was stored, something like stackedLayers.pkl.gz'
)
parser.add_argument(
'command',
type=str,
default='embed',
choices=['visall', 'embed'],
nargs='?',
help=
'What to do: one of {visall (save all plots), embed (drop into shell)}. Default: embed.'
)
args = parser.parse_args()
resman.start(args.name, diary=not args.nodiary)
saveDir = resman.rundir
print 'Loading StackedLayers from %s' % args.stackedLayersFilename
sl = loadFromPklGz(args.stackedLayersFilename)
print 'Loaded these StackedLayers:'
sl.printStatus()
if args.command == 'embed':
embed()
elif args.command == 'visall':
sl.visAll(saveDir)
else:
print 'Unknown command:', args.command
resman.stop()
def main():
parser = argparse.ArgumentParser(description='Visualize performance of different human-activity-detection runs using the generated avg_pr.model.c0.1.e0.01.w3 files (or similar)')
parser.add_argument('--name', type = str, default = 'junk',
help = 'Name for GitResultsManager results directory (default: junk)')
parser.add_argument('--quick', action='store_true', help = 'Enable quick mode (default: off)')
parser.add_argument('--nodiary', action='store_true', help = 'Disable diary (default: diary is on)')
parser.add_argument('avg_pr_model_file', type = str, nargs = '+',
help = 'Which avg_pr.model.c0.1.e0.01.w3 (or similar) files to load. Must give at least one.')
args = parser.parse_args()
resman.start(args.name, diary = not args.nodiary)
visPerf(saveDir = resman.rundir, modelFiles = args.avg_pr_model_file)
resman.stop()
def main():
parser = argparse.ArgumentParser(description='Makes features for the human activity deteciton dataset. Example usage:\n./humanObjectFeatures.py /path/to/data_obj_feats.txt')
parser.add_argument('--name', type = str, default = 'junk',
help = 'Name for GitResultsManager results directory (default: junk)')
parser.add_argument('--quick', action='store_true', help = 'Enable quick mode (default: off)')
parser.add_argument('--nodiary', action='store_true', help = 'Disable diary (default: diary is on)')
#parser.add_argument('stackedLayersFilename', type = str,
# help = 'File where a StackedLayers model was stored, something like stackedLayers.pkl.gz')
#parser.add_argument('command', type = str, default = 'embed', choices = ['visall', 'embed'], nargs='?',
# help = 'What to do: one of {visall (save all plots), embed (drop into shell)}. Default: embed.')
parser.add_argument('--stackedlayers', type = str, default = '',
help = 'Path to a *.pkl.gz file containing a pickeled StackedLayers object to load (default: None)')
parser.add_argument('--fdata', type = str, default = '',
help = 'Path to a "..../formatted/data/method_name" directory containing files like 0510175829_1.txt. Needed only if adding truth labels. (default: None)')
parser.add_argument('--outfile', type = str, default = 'data_objs_feats_plus.txt',
help = 'What to name the output file (default: data_objs_feats_plus.txt)')
parser.add_argument('dataDir', type = str, default = 'data',
help = 'Where to look for the "by_id" directory')
parser.add_argument('data_obj_feats_file', type = str,
help = 'Which data_objs_feats.txt file to load')
args = parser.parse_args()
resman.start(args.name, diary = not args.nodiary)
saveDir = resman.rundir
#print 'Loading StackedLayers from %s' % args.stackedLayersFilename
#sl = loadFromPklGz(args.stackedLayersFilename)
#print 'Loaded these StackedLayers:'
#sl.printStatus()
stackedLayers = None
if args.stackedlayers:
stackedLayers = loadFromPklGz(args.stackedlayers)
makeFeats(dataDir = args.dataDir, featsFilename = args.data_obj_feats_file,
saveDir = saveDir, outputFilename = args.outfile, quick = args.quick,
stackedLayers = stackedLayers, formattedDataDir = args.fdata,
featsBase = True, featsConst = 0, featsRand = 0, featsSL = False, featsTruth = False)
resman.stop()
def main():
resman.start('junk', diary = False)
stica = loadFromPklGz('results/130407_132841_76b6586_rapidhacks_upson3_1c_l2_first/stackedTica_mod.pkl.gz')
layer1Whitener = loadFromPklGz('../data/upson_rovio_3/white/train_10_50000_1c.whitener.pkl.gz')
layerSizePlan = [10, 15, 23, 35, 53, 80, 120, 180]
visLayer = 1
largeSampleMatrix, labelMatrix, labelStrings = cached(randomSampleMatrixWithLabels, trainFilter,
seed = 0, color = False,
Nw = layerSizePlan[visLayer], Nsamples = 50000)
seed = 0
Nw = layerSizePlan[visLayer-1] # e.g. 10
Nwbig = layerSizePlan[visLayer] # e.g. 15
Nwshift = Nwbig - Nw # e.g. 15 - 10 = 5
Nsamples = 1000
temp = getBigAndSmallerSamples(trainFilter, layer1Whitener, seed, False, Nw, Nwshift, Nsamples)
largeSampleMatrix, stackedSmall, stackedSmallWhite, labelMatrix, labelStrings = temp
pooled = stica.getRepresentation(largeSampleMatrix)
plotTopActivations(pooled, largeSampleMatrix, (Nwbig,Nwbig), resman.rundir, nActivations = 50, nSamples = 20)
pl = (pooled.T - pooled.mean(1)).T
for ii in range(len(labelStrings)):
print 'finding top for', labelStrings[ii]
if labelMatrix[ii,:].sum() == 0:
print ' skipping, no examples'
continue
avgActivationForClass = (pl * labelMatrix[ii,:]).mean(1)
sortIdx = argsort(avgActivationForClass)
topNeurons = sortIdx[-1:-(50+1):-1]
plotTopActivations(pooled[topNeurons,:], largeSampleMatrix, (Nwbig,Nwbig), resman.rundir,
nActivations = 50, nSamples = 20, prefix = 'topfor_%s' % labelStrings[ii])
resman.stop()
def main():
parser = argparse.ArgumentParser(description='Trains a StackedLayers model.')
parser.add_argument('layerFilename', type = str,
help = 'File defining layers, something like tica-10-15.layers')
parser.add_argument('trainParamsFilename', type = str,
help = 'File defining training parameters, something like tica-10-15.trainparams')
parser.add_argument('--name', type = str, default = 'junk',
help = 'Name for GitResultsManager results directory (default: junk)')
parser.add_argument('--load', type = str, default = '',
help = ('Load a previously created StackedLayers object. This can ' +
'be used to resume training a previously checkpointed, ' +
'partially trained StackedLayers object (default: none)'))
parser.add_argument('--quick', action='store_true', help = 'Enable quick mode (default: off)')
parser.add_argument('--nodiary', action='store_true', help = 'Disable diary (default: diary is on)')
args = parser.parse_args()
resman.start(args.name, diary = not args.nodiary)
saveDir = resman.rundir
layerDefinitions = importFromFile(args.layerFilename, 'layers')
trainParams = importFromFile(args.trainParamsFilename, 'trainParams')
shutil.copyfile(args.layerFilename, os.path.join(saveDir, 'params.layers'))
shutil.copyfile(args.trainParamsFilename, os.path.join(saveDir, 'params.trainparams'))
if args.load:
print 'Loading StackedLayers object from %s' % args.load
sl = loadFromPklGz(args.load)
else:
print 'Creating new StackedLayers object'
sl = StackedLayers(layerDefinitions)
sl.printStatus()
sl.train(trainParams, saveDir = saveDir, quick = args.quick)
resman.stop()
from util.plotting import tile_raster_images
from util.dataLoaders import loadFromPklGz, saveToFile
if __name__ == '__main__':
resman.start('junk', diary = False)
data = loadFromPklGz('../data/upson_rovio_2/train_10_50000_1c.pkl.gz')
data = data.T # Make into one example per column
#data = data[:,:5000] # HACK!!!!!!!!!
nFeatures = 100
lambd = .05
neighborhoodSize = 1.5
print '\nChosen TICA parameters'
for key in ['nFeatures', 'lambd']:
print ' %20s: %s' % (key, locals()[key])
random.seed(0)
rica = RICA(imgShape = (10, 10),
nFeatures = nFeatures,
lambd = lambd,
epsilon = 1e-5,
saveDir = resman.rundir)
rica.run(data, maxFun = 300, whiten = True, plotEvery = 50)
resman.stop()
def main():
resman.start('junk', diary=False)
useIpython = True
if useIpython:
client = Client(profile='ssh')
#client = Client()
print 'IPython worker ids:', client.ids
balview = client.load_balanced_view()
resultsFilename = os.path.join(resman.rundir, 'allResults.pkl.gz')
NN = 1000
allResults = [[None, None] for ii in range(NN)]
experiments = []
cwd = os.getcwd()
disp = os.environ['DISPLAY']
for ii in range(NN):
params = {}
random.seed(ii)
params['hiddenISize'] = random.choice((2, 4, 6, 8, 10, 15, 20))
params['hiddenJSize'] = params['hiddenISize']
params['neighborhoodSize'] = random.choice(
(.1, .3, .5, .7, 1.0, 1.5, 2.0, 2.5, 3.5, 5.0))
lambd = exp(random.uniform(log(.0001),
log(10))) # Uniform in log space
params['lambd'] = round(
lambd,
1 - int(floor(log10(lambd)))) # Just keep two significant figures
params['randSeed'] = ii
params['maxFuncCalls'] = 300
#params['dataWidth'] = random.choice((2, 4)) # just quick
#params['dataWidth'] = random.choice((2, 3, 4, 6, 10, 15, 20, 25, 28))
params['dataWidth'] = random.choice(
(2, 3, 4, 6, 10, 15, 20)) # 25 and 28 are incomplete
params['nColors'] = random.choice((1, 3))
params['isColor'] = (params['nColors'] == 3)
params['imgShape'] = ((params['dataWidth'], params['dataWidth'],
3) if params['isColor'] else
(params['dataWidth'], params['dataWidth']))
params['whiten'] = False # Just false for Space Invaders dataset...
params['dataCrop'] = None # Set to None to not crop data...
paramsRand = params.copy()
paramsRand['dataLoader'] = 'loadRandomData'
paramsRand['dataPath'] = (
'../data/random/randomu01_train_%02d_50000_%dc.pkl.gz' %
(paramsRand['dataWidth'], paramsRand['nColors']))
paramsData = params.copy()
paramsData['dataLoader'] = 'loadAtariData'
paramsData['dataPath'] = (
'../data/atari/space_invaders_train_%02d_50000_%dc.pkl.gz' %
(paramsData['dataWidth'], paramsData['nColors']))
#paramsData['dataLoader'] = 'loadUpsonData'
#paramsData['dataPath'] = ('../data/upson_rovio_2/train_%02d_50000_%dc.pkl.gz'
# % (paramsData['dataWidth'], paramsData['nColors']))
if not useIpython:
resultsRand = reliablyRunTest(resman.rundir, '%05d_rand' % ii,
paramsRand)
allResults[ii][0] = {'params': paramsRand, 'results': resultsRand}
tmpFilename = os.path.join(resman.rundir,
'.tmp.%f.pkl.gz' % time.time())
saveToFile(tmpFilename, allResults)
os.rename(tmpFilename, resultsFilename)
resultsData = reliablyRunTest(resman.rundir, '%05d_data' % ii,
paramsData)
allResults[ii][1] = {'params': paramsData, 'results': resultsData}
tmpFilename = os.path.join(resman.rundir,
'.tmp.%f.pkl.gz' % time.time())
saveToFile(tmpFilename, allResults)
os.rename(tmpFilename, resultsFilename)
else:
experiments.append(((ii, 0), resman.rundir, '%05d_rand' % ii,
paramsRand, cwd, disp))
experiments.append(((ii, 1), resman.rundir, '%05d_data' % ii,
paramsData, cwd, disp))
# Start all jobs
jobMap = balview.map_async(reliablyRunTest, experiments, ordered=False)
#jobMap = balview.map_async(reliablyRunTest, range(10), ordered = False)
for ii, returnValues in enumerate(jobMap):
testId, params, results = returnValues
print ii, 'Job', testId, 'finished.'
allResults[testId[0]][testId[1]] = {
'params': params,
'results': results
}
tmpFilename = os.path.join(resman.rundir,
'.tmp.%f.pkl.gz' % time.time())
saveToFile(tmpFilename, allResults)
os.rename(tmpFilename, resultsFilename)
#pdb.set_trace()
print 'Finished all jobs.'
resman.stop()
def main():
parser = argparse.ArgumentParser(
description='Trains a StackedLayers model.')
parser.add_argument(
'layerFilename',
type=str,
help='File defining layers, something like tica-10-15.layers')
parser.add_argument(
'trainParamsFilename',
type=str,
help=
'File defining training parameters, something like tica-10-15.trainparams'
)
parser.add_argument(
'--name',
type=str,
default='junk',
help='Name for GitResultsManager results directory (default: junk)')
parser.add_argument(
'--load',
type=str,
default='',
help=('Load a previously created StackedLayers object. This can ' +
'be used to resume training a previously checkpointed, ' +
'partially trained StackedLayers object (default: none)'))
parser.add_argument(
'--maxlayer',
type=int,
default=-1,
help=(
'Maximum layer to train, -1 to train all. Can be used to train' +
'only a subset of layers in an otherwise deeper model. Default: -1.'
))
parser.add_argument('--quick',
action='store_true',
help='Enable quick mode (default: off)')
parser.add_argument('--nodiary',
action='store_true',
help='Disable diary (default: diary is on)')
args = parser.parse_args()
resman.start(args.name, diary=not args.nodiary)
saveDir = resman.rundir
layerDefinitions = importFromFile(args.layerFilename, 'layers')
trainParams = importFromFile(args.trainParamsFilename, 'trainParams')
shutil.copyfile(args.layerFilename, os.path.join(saveDir, 'params.layers'))
shutil.copyfile(args.trainParamsFilename,
os.path.join(saveDir, 'params.trainparams'))
# Experiment: train a few Stacked Layers with different ticas
assert not args.load, 'Loading does not make sense here.'
sls = []
for ii, hiddenWidth in enumerate((8, 12, 16, 20, 24, 28, 32, 36, 40)):
treatmentDir = os.path.join(saveDir,
'treatment_%02d_%d' % (ii, hiddenWidth))
os.mkdir(treatmentDir)
print '\n' * 4 + '*' * 40
print 'Treatment %d, width %d (results in %s)' % (ii, hiddenWidth,
treatmentDir)
print '*' * 40
assert layerDefinitions[2]['type'] == 'tica'
layerDefinitions[2]['hiddenSize'] = (hiddenWidth, hiddenWidth)
print 'Creating new StackedLayers object'
sl = StackedLayers(layerDefinitions)
sl.printStatus()
sl.train(trainParams,
saveDir=treatmentDir,
quick=args.quick,
maxlayer=args.maxlayer)
sls.append(sl)
fileFinal = os.path.join(saveDir, 'multiStackedLayers.pkl.gz')
saveToFile(fileFinal, sls)
resman.stop()
def main():
parser = argparse.ArgumentParser(
description=
'Makes features for the human activity deteciton dataset. Example usage:\n./humanObjectFeatures.py /path/to/data_obj_feats.txt'
)
parser.add_argument(
'--name',
type=str,
default='junk',
help='Name for GitResultsManager results directory (default: junk)')
parser.add_argument('--quick',
action='store_true',
help='Enable quick mode (default: off)')
parser.add_argument('--nodiary',
action='store_true',
help='Disable diary (default: diary is on)')
#parser.add_argument('stackedLayersFilename', type = str,
# help = 'File where a StackedLayers model was stored, something like stackedLayers.pkl.gz')
#parser.add_argument('command', type = str, default = 'embed', choices = ['visall', 'embed'], nargs='?',
# help = 'What to do: one of {visall (save all plots), embed (drop into shell)}. Default: embed.')
parser.add_argument(
'--stackedlayers',
type=str,
default='',
help=
'Path to a *.pkl.gz file containing a pickeled StackedLayers object to load (default: None)'
)
parser.add_argument(
'--fdata',
type=str,
default='',
help=
'Path to a "..../formatted/data/method_name" directory containing files like 0510175829_1.txt. Needed only if adding truth labels. (default: None)'
)
parser.add_argument(
'--outfile',
type=str,
default='data_objs_feats_plus.txt',
help='What to name the output file (default: data_objs_feats_plus.txt)'
)
parser.add_argument('dataDir',
type=str,
default='data',
help='Where to look for the "by_id" directory')
parser.add_argument('data_obj_feats_file',
type=str,
help='Which data_objs_feats.txt file to load')
args = parser.parse_args()
resman.start(args.name, diary=not args.nodiary)
saveDir = resman.rundir
#print 'Loading StackedLayers from %s' % args.stackedLayersFilename
#sl = loadFromPklGz(args.stackedLayersFilename)
#print 'Loaded these StackedLayers:'
#sl.printStatus()
stackedLayers = None
if args.stackedlayers:
stackedLayers = loadFromPklGz(args.stackedlayers)
makeFeats(dataDir=args.dataDir,
featsFilename=args.data_obj_feats_file,
saveDir=saveDir,
outputFilename=args.outfile,
quick=args.quick,
stackedLayers=stackedLayers,
formattedDataDir=args.fdata,
featsBase=True,
featsConst=0,
featsRand=0,
featsSL=False,
featsTruth=False)
resman.stop()
