pipeline.items.append(preprocessing.ExtractPatches(patch_shape=(14,14), num_patches=5*1000*1000))
#### Build full-sized image dataset. ####
print "Preparing output directory for unlabeled patches..."
outdir = data_dir + '/tfd_lcn_v1'
serial.mkdir(outdir)
README = open('README','w')
README.write("""
File generated from hossrbm/scripts/tfd/make_tfd_lcn.py.
""")
README.close()
print 'Loading TFD unlabeled dataset...'
print "Preprocessing the data..."
data = TFD('unlabeled')
data.apply_preprocessor(preprocessor = pipeline, can_fit = True)
data.use_design_loc(outdir + '/unlabeled_patches.npy')
serial.save(outdir + '/unlabeled_patches.pkl',data)
#### For supervised dataset, we work on the full-image dataset ####
pipeline.items.pop()
#### Build supervised-training datasets ####
print "Preparing output directory for supervised data..."
for fold_i in xrange(0,5):
path = '%s/fold%i' % (outdir, fold_i)
serial.mkdir(path)
train_data = TFD('train', fold=fold_i, center=False, shuffle=True, seed=37192)
train_data.apply_preprocessor(preprocessor = pipeline, can_fit = False)
