FLAGS = tf.app.flags.FLAGS

tf.app.flags.DEFINE_string('image_folder',
                           './VOCdevkit/VOC2012/JPEGImages',
                           'Folder containing images.')

tf.app.flags.DEFINE_string(
    'semantic_segmentation_folder',
    './VOCdevkit/VOC2012/SegmentationClassRaw',
    'Folder containing semantic segmentation annotations.')

tf.app.flags.DEFINE_string(
    'list_folder',
    './VOCdevkit/VOC2012/ImageSets/Segmentation',
    'Folder containing lists for training and validation')

tf.app.flags.DEFINE_string(
    'output_dir',
    './tfrecord',
    'Path to save converted SSTable of TensorFlow examples.')


_NUM_SHARDS = 4

# add -->>
FLAGS.image_folder = "./pascal_voc_seg/VOCdevkit/VOC2012/JPEGImages"
FLAGS.semantic_segmentation_folder = "./pascal_voc_seg/VOCdevkit/VOC2012/SegmentationClassRaw"
FLAGS.list_folder = "./pascal_voc_seg/VOCdevkit/VOC2012/ImageSets/Segmentation"
FLAGS.image_format = "png"
FLAGS.output_dir = "./pascal_voc_seg/tfrecord"
# add --<<

# add kani 20181115 -->>
_ORIGINAL_INFORMATION = DatasetDescriptor(
    splits_to_sizes={
        'train': 10,
        'trainval': 2,
        'val': 2,
    },
    num_classes=5,
    ignore_label=255,
)
#add kani 20181115 --<<

# mod kani 20181115 -->>
# _DATASETS_INFORMATION = {
#     'cityscapes': _CITYSCAPES_INFORMATION,
#     'pascal_voc_seg': _PASCAL_VOC_SEG_INFORMATION,
#     'ade20k': _ADE20K_INFORMATION,
# }

_DATASETS_INFORMATION = {
    'cityscapes': _CITYSCAPES_INFORMATION,
    'pascal_voc_seg': _PASCAL_VOC_SEG_INFORMATION,
    'ade20k': _ADE20K_INFORMATION,
    'original': _ORIGINAL_INFORMATION,
}
# mod kani 20181115 --<<

python train.py   --logtostderr   --train_split=trainval   --model_variant=xception_65   --atrous_rates=3   --atrous_rates=6   --atrous_rates=9   --output_stride=32   --decoder_output_stride=4   --train_crop_size=512   --train_crop_size=512   --train_batch_size=2   --training_number_of_steps=6000   --fine_tune_batch_norm=false   --tf_initial_checkpoint="./datasets/pascal_voc_seg/init_models/deeplabv3_pascal_train_aug/model.ckpt"  --train_logdir="./datasets/pascal_voc_seg/exp/train_on_trainval_set/train"  --dataset_dir="./datasets/pascal_voc_seg/tfrecord" --dataset=original

python vis.py   --logtostderr   --vis_split="val"   --model_variant="xception_65"   --atrous_rates=6   --atrous_rates=12   --atrous_rates=18   --output_stride=16   --decoder_output_stride=4   --vis_crop_size=513   --vis_crop_size=513   --checkpoint_dir="./datasets/pascal_voc_seg/exp/train_on_trainval_set/train"   --vis_logdir="./datasets/pascal_voc_seg/exp/train_on_trainval_set/vis"  --dataset_dir="./datasets/pascal_voc_seg/tfrecord"   --max_number_of_iterations=1   --dataset=original   --max_resize_value=512   --min_resize_value=128

/tmp/models/research/deeplab
-README.md
-common.py
-datasets/
--__init__.py
--build_data.py
--convert_cityscapes.sh
--pascal_voc_seg/
---VOCdevkit/
----VOC2012/
-----Annotations/
-----ImageSets/
-----JPEGImages/
-----SegmentationClass/
-----SegmentationObject/
---VOCtrainval_11-May-2012.tar
---exp/
----train_on_trainval_set/
-----train/
------train.py
-----vis/
------vis.py
---init_models/
----deeplabv3_pascal_train_aug/
-----frozen_inference_graph.pb
-----model.ckpt.data-00000-of-00001
-----model.ckpt.index
---tfrecord/
----build_voc_2012.py
--__pycache__
--build_data.pyc
--download_and_convert_ade20k.sh
--remove_gt_colormap.py
--build_ade20k_data.py
--build_voc2012_data.py
--download_and_convert_voc2012.sh
--segmentation_dataset.py
--build_cityscapes_data.py
--build_voc2012_data.py.org
-export_model.py
-local_test.sh
-model_test.py
-utils/
-__init__.py
-common_test.py
-deeplab_demo.ipynb
-g3doc/
-local_test_mobilenetv2.sh
-train.py
-vis.py
-__pycache__
-core/
-eval.py
-input_preprocess.py
-model.py
-train.py.bk