[label]
pad_mode = "constant"
pad_fill = 255
resize_mode = "nearest"
grayscale = False
filename = "{prefix}_{subset}-p{x_pad}x{y_pad}x{z_pad}-labeldata.idx"
use_channel_dim = False
"""
from __future__ import division, print_function, unicode_literals
import numpy as np
from scipy.misc import imresize
import datasets.pascal as pc
from mlizard import createExperiment
from infrastructure.idxconverter import write_idx_file
ex = createExperiment(config_string=__doc__)
@ex.stage
def get_image_path_list(subset):
return list({'train' : pc.get_seg_train_image_files,
'val' : pc.get_seg_val_image_files,
'all' : pc.get_seg_trainval_image_files}[subset]())
@ex.stage
def get_label_path_list(subset):
return list({'train' : pc.get_seg_train_class_label_files,
'val' : pc.get_seg_val_class_label_files,
'all' : pc.get_seg_trainval_class_label_files}[subset]())
get_classes_matrix = ex.stage(pc.get_classes_matrix)
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
"""
seed = 380434
ds_path = "/home/greff/Datasets/ImageNet/"
used_classes = [1, 2, 3, 4]
samples_per_class = 10
shuffle = True
"""
from __future__ import division, print_function, unicode_literals
from os.path import join
import numpy as np
from mlizard import createExperiment
ex = createExperiment("ImageNet", config_string=__doc__)
@ex.stage
def get_image_paths_and_classes(ds_path, logger):
path = join(ds_path, "trainfiles.txt" )
f = open(path, 'r')
lines = f.readlines()
lines = map(lambda s : s.split(None, 1), lines)
paths = np.array([join(ds_path, p) for p, c in lines])
targets = np.array([[int(c)] for p, c in lines])
logger.info("Opened path '%s' and got %d samples.", path, len(lines))
return paths, targets
@ex.stage
def get_subset_indices(targets, used_classes, samples_per_class, shuffle, rnd):
indices_for_class = []