def run_cv_batch(cv_configs):
for job_settings in cv_configs:
results_file = job_settings['results']
data_file = job_settings['data']
if os.path.exists(results_file):
print 'Warning: results file %s already exists; aborting' % results_file
continue
if Condorizable.is_locked(results_file):
print 'WARNING: Results file %s is locked; another job may be writing to this file' % results_file
continue
if not os.path.exists(data_file):
print 'WARNING: arff file %s does not exist; aborting' % data_file
continue
if Condorizable.is_locked(data_file):
print 'WARNING: data file %s is locked; another job may be writing to this file' % data_file
continue
CrossValidationTask(kw=job_settings)
def run_cv_batch(cv_configs):
for job_settings in cv_configs:
results_file = job_settings['results']
data_file = job_settings['data']
if os.path.exists(results_file):
print 'Warning: results file %s already exists; aborting' % results_file
continue
if Condorizable.is_locked(results_file):
print 'WARNING: Results file %s is locked; another job may be writing to this file' % results_file
continue
if not os.path.exists(data_file):
print 'WARNING: arff file %s does not exist; aborting' % data_file
continue
if Condorizable.is_locked(data_file):
print 'WARNING: data file %s is locked; another job may be writing to this file' % data_file
continue
CrossValidationTask(kw=job_settings)
class MakeGistCorpusTask(Condorizable):
binary = Condorizable.path_to_script(__file__)
def check_args(self, argv):
parser = ArgumentParser()
parser.add_argument('file_list',
type=str,
help='File containing list of images to process')
parser.add_argument('dest_corpus',
type=str,
help='Path to write GIST corpus')
parser.add_argument('--labeler', type=str, help='Labeler to apply')
parser.add_argument('--color', action='store_true', help='Color GIST?')
options = parser.parse_args(argv[1:])
if options.labeler is None:
log.warning('no labeler provided')
elif options.labeler not in labelers.registry:
labeler_names = ', '.join(sorted(labelers.registry.keys()))
parser.error('Invalid labeler "%s"; available options are %s' %
(options.labeler, labeler_names))
if not os.path.exists(options.file_list):
parser.error('Input file %s does not exist!' % options.file_list)
self.add_output_file(options.dest_corpus)
return options
def run(self, options):
labeler = None if options.labeler is None else labelers.registry[
options.labeler]
# Wait to instantiate the corpus writer until we know the dimensionality of the descriptors we'll be writing
writer = None
log.info('Writing SAM corpus to %s' % options.dest_corpus)
filenames = open(options.file_list).readlines()
for i, filename in enumerate(filenames):
filename = filename.strip()
log.info('Processing image %d/%d' % (i + 1, len(filenames)))
descriptor = color_gist(
filename) if options.color else grayscale_gist(filename)
if writer is None:
dim = descriptor.size
writer = CorpusWriter(options.dest_corpus,
data_series='sam',
dim=dim)
normalized_descriptor = l2_normalize(descriptor)
doc_label = labeler(filename) if labeler else None
writer.write_doc(ascolvector(normalized_descriptor),
name=filename,
label=doc_label)
writer.close()
class MakeGistArffTask(Condorizable):
binary = Condorizable.path_to_script(__file__)
def check_args(self, argv):
parser = ArgumentParser()
parser.add_argument('file_list',
type=str,
help='File containing list of images to process')
parser.add_argument('dest', type=str, help='Destination ARFF file')
parser.add_argument('--labeler',
type=str,
required=True,
choices=labelers.registry.keys(),
help='Labeler to apply')
parser.add_argument('--color', action='store_true', help='Color GIST?')
parser.add_argument('--normalize',
action='store_true',
help='L2 normalize GIST data?')
options = parser.parse_args(argv[1:])
if not os.path.exists(options.file_list):
parser.error('Input file %s does not exist!' % options.file_list)
self.add_output_file(options.dest)
return options
def run(self, options):
labeler = labelers.registry[options.labeler]
# Wait to instantiate the corpus writer until we know the dimensionality of the descriptors we'll be writing
filenames = open(options.file_list).readlines()
labels = [labeler(each) for each in filenames]
class_list = sorted(set(labels))
writer = ArffWriter(options.dest, class_list=class_list)
log.info('Writing GIST data to %s' % options.dest)
for i, (filename, label) in enumerate(izip(filenames, labels)):
filename = filename.strip()
log.info('Processing image %d/%d' % (i + 1, len(filenames)))
descriptor = color_gist(
filename) if options.color else grayscale_gist(filename)
if options.normalize:
descriptor = l2_normalize(descriptor)
writer.write_example(descriptor, label)
writer.close()
def run_sam_batch(vem_configs):
"""
Runs SAM on every experimental configuration defined by 'config'. Jobs that have already been run or are
current running (i.e. for which the model file already exists, or for which a lock file exists) will be skipped.
"""
for job_settings in vem_configs:
model_file = job_settings['model']
if os.path.exists(model_file):
log.warning('Model %s already exists; skipping' % os.path.basename(model_file))
continue
if Condorizable.is_locked(model_file):
log.warning('Model %s is locked; check that another job isn''t writing to this path' %\
os.path.basename(model_file))
continue
VEMTask(kw=job_settings)
def run_sam_batch(vem_configs):
"""
Runs SAM on every experimental configuration defined by 'config'. Jobs that have already been run or are
current running (i.e. for which the model file already exists, or for which a lock file exists) will be skipped.
"""
for job_settings in vem_configs:
model_file = job_settings['model']
if os.path.exists(model_file):
log.warning('Model %s already exists; skipping' %
os.path.basename(model_file))
continue
if Condorizable.is_locked(model_file):
log.warning('Model %s is locked; check that another job isn''t writing to this path' %\
os.path.basename(model_file))
continue
VEMTask(kw=job_settings)
