Why do all images resulting by Deeplabv 3+ become black only?

1

I tried semanticsegmentation with deeplab v3+ but I got results all black out.

I deleted the original file and put original datas in ImageSets/,JPEGImages/ and SegmentationClass/ corresponding to each.

I prepared SegmentationClassRaw image according to rule of PASCAL VOC 2012 color.

And I edited build_voc2012_data.py and segmentation_dataset.py

[build_voc2012_data.py]

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 --<<

[segmentation_dataset.pu]

# 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 --<<

I run train.py and vis.py like this.

[train.py command]

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

[vis.py command]

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

Both ended without a problem but I confirmed pictures in datasets/pascal_voc_seg/exp/train_on_trainval_set/vis/raw_segmentation_results/, these are black out all. why?

Is this because of train datas are bigger than 512x512?
(Train datas size are so big about 15000x13500)

[structure my directory]

/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  

tensorflow deep-learning segmentation-fault semantic-segmentation deeplab

share|improve this question

asked Nov 21 '18 at 4:11

y_ kaniy_ kani

61

add a comment | 

1

I tried semanticsegmentation with deeplab v3+ but I got results all black out.

I deleted the original file and put original datas in ImageSets/,JPEGImages/ and SegmentationClass/ corresponding to each.

I prepared SegmentationClassRaw image according to rule of PASCAL VOC 2012 color.

And I edited build_voc2012_data.py and segmentation_dataset.py

[build_voc2012_data.py]

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 --<<

[segmentation_dataset.pu]

# 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 --<<

I run train.py and vis.py like this.

[train.py command]

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

[vis.py command]

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

Both ended without a problem but I confirmed pictures in datasets/pascal_voc_seg/exp/train_on_trainval_set/vis/raw_segmentation_results/, these are black out all. why?

Is this because of train datas are bigger than 512x512?
(Train datas size are so big about 15000x13500)

[structure my directory]

/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  

tensorflow deep-learning segmentation-fault semantic-segmentation deeplab

share|improve this question

asked Nov 21 '18 at 4:11

y_ kaniy_ kani

61

add a comment | 

1

1

1

I tried semanticsegmentation with deeplab v3+ but I got results all black out.

I deleted the original file and put original datas in ImageSets/,JPEGImages/ and SegmentationClass/ corresponding to each.

I prepared SegmentationClassRaw image according to rule of PASCAL VOC 2012 color.

And I edited build_voc2012_data.py and segmentation_dataset.py

[build_voc2012_data.py]

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 --<<

[segmentation_dataset.pu]

# 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 --<<

I run train.py and vis.py like this.

[train.py command]

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

[vis.py command]

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

Both ended without a problem but I confirmed pictures in datasets/pascal_voc_seg/exp/train_on_trainval_set/vis/raw_segmentation_results/, these are black out all. why?

Is this because of train datas are bigger than 512x512?
(Train datas size are so big about 15000x13500)

[structure my directory]

/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  

tensorflow deep-learning segmentation-fault semantic-segmentation deeplab

share|improve this question

asked Nov 21 '18 at 4:11

y_ kaniy_ kani

61

I tried semanticsegmentation with deeplab v3+ but I got results all black out.

I deleted the original file and put original datas in ImageSets/,JPEGImages/ and SegmentationClass/ corresponding to each.

I prepared SegmentationClassRaw image according to rule of PASCAL VOC 2012 color.

And I edited build_voc2012_data.py and segmentation_dataset.py

[build_voc2012_data.py]

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 --<<

[segmentation_dataset.pu]

# 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 --<<

I run train.py and vis.py like this.

[train.py command]

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

[vis.py command]

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

Both ended without a problem but I confirmed pictures in datasets/pascal_voc_seg/exp/train_on_trainval_set/vis/raw_segmentation_results/, these are black out all. why?

Is this because of train datas are bigger than 512x512?
(Train datas size are so big about 15000x13500)

[structure my directory]

/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  

tensorflow deep-learning segmentation-fault semantic-segmentation deeplab

tensorflow deep-learning segmentation-fault semantic-segmentation deeplab

share|improve this question

asked Nov 21 '18 at 4:11

y_ kaniy_ kani

61

share|improve this question

asked Nov 21 '18 at 4:11

y_ kaniy_ kani

61

share|improve this question

share|improve this question

asked Nov 21 '18 at 4:11

y_ kaniy_ kani

61

asked Nov 21 '18 at 4:11

y_ kaniy_ kani

61

asked Nov 21 '18 at 4:11

y_ kaniy_ kani

61

61

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