def write_batches(args, macrodir, datadir, val_pct):
if os.path.exists(macrodir):
return
print('Writing batches to %s' % macrodir)
bw = BatchWriter(out_dir=macrodir,
image_dir=os.path.join(args.data_dir, datadir),
target_size=32, macro_size=1024,
file_pattern='*.jpg', validation_pct=val_pct)
bw.run()
def load(self, backend=None, experiment=None):
'''
Imageset only supports nervanagpu based backends
'''
if not hasattr(self.backend, 'ng'):
raise DeprecationWarning("Only nervanagpu-based backends "
"supported. For using cudanet backend, "
"revert to neon 0.8.2 ")
bdir = os.path.expanduser(self.save_dir)
cachefile = os.path.join(bdir, 'dataset_cache.pkl')
if not os.path.exists(cachefile):
logger.error("Batch dir cache not found in %s:", cachefile)
response = raw_input("Press Y to create, otherwise exit: ")
if response == 'Y':
from neon.util.batch_writer import (BatchWriter,
BatchWriterImagenet)
if self.imageset.startswith('I1K'):
self.bw = BatchWriterImagenet(**self.__dict__)
else:
self.bw = BatchWriter(**self.__dict__)
self.bw.run()
logger.error('Done writing batches - please rerun to train.')
else:
logger.error('Exiting...')
sys.exit()
cstats = deserialize(cachefile, verbose=False)
if cstats['macro_size'] != self.macro_size:
raise NotImplementedError("Cached macro size %d different from "
"specified %d, delete save_dir %s "
"and try again.",
cstats['macro_size'],
self.macro_size,
self.save_dir)
# Set the max indexes of batches for each from the cache file
self.maxval = cstats['nval'] + cstats['val_start'] - 1
self.maxtrain = cstats['ntrain'] + cstats['train_start'] - 1
# Make sure only those properties not by yaml are updated
cstats.update(self.__dict__)
self.__dict__.update(cstats)
# Should also put (in addition to nclass), number of train/val images
req_param(self, ['ntrain', 'nval', 'train_start', 'val_start',
'train_mean', 'val_mean', 'labels_dict'])
