def read_examples(filename, sparm):
"""Reads and returns x,y example pairs from a file.
This reads the examples contained at the file at path filename and
returns them as a sequence. Each element of the sequence should
be an object 'e' where e[0] and e[1] is the pattern (x) and label
(y) respectively. Specifically, the intention is that the element
be a two-element tuple containing an x-y pair."""
# We actually ignore the filename passed to us
print '\n'
# Check that we're not about to overwrite a previous experiment
if os.path.exists(OutputPath):
print 'Error: Experiment dir already exists: ', OutputPath
exit(-1)
# Load the dataset
training_instances,test_instances = \
training_helpers.load_dataset(Mgr,
Dataset.TrainingSet,
Dataset.TestSet)
assert (Mgr.NumInstances() == len(training_instances) +
len(test_instances))
# Compute features
for inst in itertools.chain(training_instances, test_instances):
compute_features(inst, FtrMgr, FeatureSet)
FtrMgr.CommitFeatures()
Mgr.NormalizeFeatures(FtrMgr)
# Compute loss terms
for inst in itertools.chain(training_instances, test_instances):
configure_loss(inst, LossFunction)
# Set up the reporter
global Reporter
Reporter = training_helpers.Reporter(training_instances, test_instances,
FtrMgr, OutputPath)
# Create a structure suitable for SVM-struct
return training_helpers.prepare_svm_data(training_instances)
def read_examples(filename, sparm):
"""Reads and returns x,y example pairs from a file.
This reads the examples contained at the file at path filename and
returns them as a sequence. Each element of the sequence should
be an object 'e' where e[0] and e[1] is the pattern (x) and label
(y) respectively. Specifically, the intention is that the element
be a two-element tuple containing an x-y pair."""
# We actually ignore the filename passed to us
print "\n"
# Check that we're not about to overwrite a previous experiment
if os.path.exists(OutputPath):
print "Error: Experiment dir already exists: ", OutputPath
exit(-1)
# Load the dataset
training_instances, test_instances = training_helpers.load_dataset(Mgr, Dataset.TrainingSet, Dataset.TestSet)
assert Mgr.NumInstances() == len(training_instances) + len(test_instances)
# Compute features
for inst in itertools.chain(training_instances, test_instances):
compute_features(inst, FtrMgr, FeatureSet)
FtrMgr.CommitFeatures()
Mgr.NormalizeFeatures(FtrMgr)
# Compute loss terms
for inst in itertools.chain(training_instances, test_instances):
configure_loss(inst, LossFunction)
# Set up the reporter
global Reporter
Reporter = training_helpers.Reporter(training_instances, test_instances, FtrMgr, OutputPath)
# Create a structure suitable for SVM-struct
return training_helpers.prepare_svm_data(training_instances)
# Compute the average per-sequence performance
import sys
import py_indoor_context
import training_params
import training_helpers
dataset = training_params.Datasets.Large.TestSet
mgr = py_indoor_context.TrainingManager()
a,instances = training_helpers.load_dataset(mgr, [], dataset)
pattern = 'out/%s_frame%03d_gt.png'
for inst in instances:
inst.OutputGroundTruthViz(pattern % (inst.GetSequenceName(),inst.GetFrameId()))
