Comments (12)
I'm not completely sure, but I believe the problem in the image size you used for training is different from what you used for inference. The error was complaining that the padding size is different from the network created in training.
Notice the --train_image_size=4
flag you used for training and the default tf.flags.DEFINE_integer('image_hw', 256, 'height and width of the input image.')
during inference. I think that might be the cause. Try changing the two to be the same and let me know if it works!
If that doesn't work please copy/paste the exact command you used. Thanks.
from twingan.
@jerryli27
I changed --train_image_size to 256, it doesn't work...
python image_generation.py --dataset_name="image_only" --dataset_dir="./test/train-B" --dataset_use_target=True --train_dir="./checkpoints/test/" --dataset_split_name=train --learning_rate=0.0001 --learning_rate_decay_type=fixed --is_training=True --train_image_size=256 --preprocessing_name="danbooru" --generator_network="pggan" --ignore_missing_vars=True --max_number_of_steps=1000
(P.S. --train_image_size=4 is working well)
2018-07-25 14:43:02.680615: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Starting Session.
INFO:tensorflow:Saving checkpoint to path ./checkpoints/test/model.ckpt
INFO:tensorflow:Starting Queues.
INFO:tensorflow:global_step/sec: 0
INFO:tensorflow:Error reported to Coordinator: <type 'exceptions.AttributeError'>, 'NoneType' object has no attribute 'endswith'
INFO:tensorflow:Recording summary at step 0.
Traceback (most recent call last):
File "image_generation.py", line 1063, in <module>
tf.app.run()
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 125, in run
_sys.exit(main(argv))
File "image_generation.py", line 1059, in main
model.main()
File "image_generation.py", line 1054, in main
super(GanModel, self).main()
File "/home/itsc/Desktop/TwinGAN/model/model_inheritor.py", line 1127, in main
session_config=session_config)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/slim/python/slim/learning.py", line 770, in train
sess, train_op, global_step, train_step_kwargs)
File "/home/itsc/Desktop/TwinGAN/model/model_inheritor.py", line 1107, in train_step_fn
self.do_extra_train_step(session, end_points, global_step)
File "image_generation.py", line 765, in do_extra_train_step
GanModel._calc_swd(session, end_points, current_step=current_step)
File "image_generation.py", line 878, in _calc_swd
util_io.touch_folder(FLAGS.eval_dir)
File "/home/itsc/Desktop/TwinGAN/util_io.py", line 36, in touch_folder
if not file_path.endswith('/'):
AttributeError: 'NoneType' object has no attribute 'endswith'
from twingan.
@jerryli27 image_translation_infer.py also not working.
when i use my current checkpoints, (--train_image_size=4 checkpoints)
tf.flags.DEFINE_string("model_path", "checkpoints/test", "Path containing a checkpoint.")
tf.flags.DEFINE_integer('image_hw', 4, 'height and width of the input image.')
tf.flags.DEFINE_string('input_tensor_name', "custom_input_ph", 'Optional input tensor name. e.g. sources_ph.')
tf.flags.DEFINE_string('output_tensor_name', "custom_generated_targets:0", 'e.g. custom_generated_t_style_source:0')
Model directory: checkpoints/test
Metagraph file: model.ckpt-50000.meta
Checkpoint file: model.ckpt-50000
Traceback (most recent call last):
File "image_translation_infer.py", line 95, in <module>
tf.app.run()
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 125, in run
_sys.exit(main(argv))
File "image_translation_infer.py", line 81, in main
inferer = ImageInferer()
File "image_translation_infer.py", line 33, in __init__
util_io.load_model(FLAGS.model_path, input_map=input_map)
File "/home/itsc/Desktop/TwinGAN-master/util_io.py", line 168, in load_model
saver = tf.train.import_meta_graph(os.path.join(model_exp, meta_file), input_map=input_map)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 1960, in import_meta_graph
**kwargs)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/meta_graph.py", line 744, in import_scoped_meta_graph
producer_op_list=producer_op_list)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/util/deprecation.py", line 432, in new_func
return func(*args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/importer.py", line 422, in import_graph_def
raise ValueError(str(e))
ValueError: Node 'generator_1/latent_vector/Pad' has an _output_shapes attribute inconsistent with the GraphDef for output #0: Dimension 1 in both shapes must be equal, but are 10 and 7. Shapes are [1,10,10,?] and [?,7,7,256].
ValueError: Node 'generator_1/latent_vector/Pad' has an _output_shapes attribute inconsistent with the GraphDef for output #0: Dimension 1 in both shapes must be equal, but are 10 and 7. Shapes are [1,10,10,?] and [?,7,7,256].
from twingan.
You have a misunderstanding of what is PGGAN vs TwinGAN. PGGAN is not an image translation model so it can't translate human faces. Try to use TwinGAN following the training guide.
Also on a side note, I tried to use Danbooru datasets as well. The quality was quite poor because there are much more variation in that dataset. Not all faces are forward facing etc.
from twingan.
@jerryli27
Yes. I think I'm misunderstood about PGGAN and TwinGAN.
Now, I need to know how to training TwinGAN, so I visited the training guide page, but it makes error like this.
WARNING:tensorflow:IOError [Errno 2] No such file or directory: './data/celeba/sample_source_validation_100.npy'. Not outputting custom generated images.
INFO:tensorflow:global_step/sec: 0
so I understand i need to convert the celeba dataset. I used this command to run convert_celeba.py
python datasets/convert_celeba.py --partition_fn /home/itsc/Desktop/list_eval_partition.txt --fn_root /home/itsc/Desktop/img_align_celeba/
- list_eval_partition.txt is a text file that i downloaded in CelebA official site.
- img_align_celeba is a jpg file that i downloaded in CelebA official site.
/usr/local/lib/python2.7/dist-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
from ._conv import register_converters as _register_converters
2018-07-26 10:48:14.370525: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
Saving results to None
Traceback (most recent call last):
File "datasets/convert_celeba.py", line 99, in <module>
tf.app.run()
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 125, in run
_sys.exit(main(argv))
File "datasets/convert_celeba.py", line 95, in main
converter.main()
File "datasets/convert_celeba.py", line 85, in main
util_io.touch_folder(FLAGS.output_directory)
File "/home/itsc/Desktop/TwinGAN/util_io.py", line 18, in touch_folder
if not file_path.endswith('/'):
AttributeError: 'NoneType' object has no attribute 'endswith'
How to solve AttributeError: 'NoneType' object has no attribute 'endswith'?
please answer to my question!
from twingan.
@jerryli27 I want to make Celeba dataset like Getchu preprocessed dataset.
CelebA dataset formating.
Adapted from https://github.com/tensorflow/models/blob/master/research/real_nvp/celeba_formatting.py .
Download img_align_celeba.zip from
http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html under the
link "Align&Cropped Images" in the "Img" directory and list_eval_partition.txt
under the link "Train/Val/Test Partitions" in the "Eval" directory. Then do:
unzip img_align_celeba.zip
Use the script as follow:
python celeba_formatting.py \
--partition_fn [PARTITION_FILE_PATH] \
--output_directory [OUTPUT_FILE_PATH_PREFIX] \
--fn_root [CELEBA_FOLDER]
I proceeded through this process and i get three .tfrecord file. (train / test / validation)
So, what should I do next? how can i make celebA dataset???
from twingan.
WARNING:tensorflow:IOError [Errno 2] No such file or directory: './data/celeba/sample_source_validation_100.npy'. Not outputting custom generated images. INFO:tensorflow:global_step/sec: 0
The first one you pointed out was a warning, not an error. It does not affect the code. It simply does not output the image translation of a fixed set of images. (Which is useful to see how training progresses, but is totally optional).
The second one I think you figured out already. When you train, just point your dataset_dir
flag at the directory containing the tfrecord files. The tfrecord files contains the celeba dataset. You can google what is tfrecord and why it is used.
--dataset_name="celeba" --dataset_dir="./data/celeba/or/wherever/your/dataset/is"
from twingan.
If that gives you an error, try to set --dataset_name=image_only
, which only reads the encoded image in the dataset and nothing else.
from twingan.
@jerryli27 I executed the command like this.
python pggan_runner.py --program_name=twingan --dataset_name="celeba" --dataset_dir="./test/" --unpaired_target_dataset_name="anime_faces" --unpaired_target_dataset_dir="./test/" --train_dir="./checkpoints/pggan/" --dataset_split_name=train --preprocessing_name="danbooru" --resize_mode=RANDOM_CROP --do_random_cropping=True --learning_rate=0.0001 --learning_rate_decay_type=fixed --is_training=True --generator_network="pggan" --num_images_per_resolution=50000 --loss_architecture=dragan --pggan_max_num_channels=256 --generator_norm_type=batch_renorm --use_ttur=True
python pggan_runner.py --program_name=twingan --dataset_name=image_only --dataset_dir="./test/" --unpaired_target_dataset_name="anime_faces" --unpaired_target_dataset_dir="./test/" --train_dir="./checkpoints/pggan/" --dataset_split_name=train --preprocessing_name="danbooru" --resize_mode=RANDOM_CROP --do_random_cropping=True --learning_rate=0.0001 --learning_rate_decay_type=fixed --is_training=True --generator_network="pggan" --num_images_per_resolution=50000 --loss_architecture=dragan --pggan_max_num_channels=256 --generator_norm_type=batch_renorm --use_ttur=True
python pggan_runner.py --program_name=twingan --dataset_name="image_only" --dataset_dir="./test/" --unpaired_target_dataset_name="anime_faces" --unpaired_target_dataset_dir="./test/" --train_dir="./checkpoints/pggan/" --dataset_split_name=train --preprocessing_name="danbooru" --resize_mode=RANDOM_CROP --do_random_cropping=True --learning_rate=0.0001 --learning_rate_decay_type=fixed --is_training=True --generator_network="pggan" --num_images_per_resolution=50000 --loss_architecture=dragan --pggan_max_num_channels=256 --generator_norm_type=batch_renorm --use_ttur=True
(Three Command Makes same error...)
INFO:tensorflow:Error reported to Coordinator: <class 'tensorflow.python.framework.errors_impl.FailedPreconditionError'>, test/train-B; Is a directory
OutOfRangeError (see above for traceback): FIFOQueue '_5_prefetch_queue/fifo_queue' is closed and has insufficient elements (requested 1, current size 0)
[[Node: fifo_queue_Dequeue = QueueDequeueV2component_types=[DT_STRING, DT_STRING, DT_STRING, DT_FLOAT, DT_FLOAT, DT_FLOAT], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/device:CPU:0"]]
[Folder Structure]
./test/train-A : result.tflearn, result1.tflearn, result2.tflearn
./test/train-B : Getchu Preprocessed Dataset (train-00000-of-00008, vaildation-00001-of-00002, etc...)
INFO:tensorflow:Error reported to Coordinator: <class 'tensorflow.python.framework.errors_impl.FailedPreconditionError'>, test/train-B; Is a directory
[[Node: parallel_read/ReaderReadV2_2 = ReaderReadV2[_device="/job:localhost/replica:0/task:0/device:CPU:0"](parallel_read/TFRecordReaderV2_2, parallel_read/filenames)]]
WARNING:tensorflow:IOError [Errno 2] No such file or directory: './test/sample_source_validation_100.npy'. Not outputting custom generated images.
2018-07-26 15:46:47.800863: W tensorflow/core/kernels/queue_base.cc:285] _4_parallel_read/common_queue: Skipping cancelled dequeue attempt with queue not closed
2018-07-26 15:46:47.801005: W tensorflow/core/kernels/queue_base.cc:285] _5_prefetch_queue/fifo_queue: Skipping cancelled dequeue attempt with queue not closed
2018-07-26 15:46:47.801261: W tensorflow/core/kernels/queue_base.cc:285] _4_parallel_read/common_queue: Skipping cancelled dequeue attempt with queue not closed
2018-07-26 15:46:47.801409: W tensorflow/core/kernels/queue_base.cc:285] _4_parallel_read/common_queue: Skipping cancelled dequeue attempt with queue not closed
2018-07-26 15:46:47.801436: W tensorflow/core/kernels/queue_base.cc:285] _4_parallel_read/common_queue: Skipping cancelled dequeue attempt with queue not closed
2018-07-26 15:46:47.801506: W tensorflow/core/kernels/queue_base.cc:285] _4_parallel_read/common_queue: Skipping cancelled dequeue attempt with queue not closed
INFO:tensorflow:Caught OutOfRangeError. Stopping Training. FIFOQueue '_5_prefetch_queue/fifo_queue' is closed and has insufficient elements (requested 1, current size 0)
[[Node: fifo_queue_Dequeue = QueueDequeueV2[component_types=[DT_STRING, DT_STRING, DT_STRING, DT_FLOAT, DT_FLOAT, DT_FLOAT], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/device:CPU:0"](prefetch_queue/fifo_queue)]]
Caused by op u'fifo_queue_Dequeue', defined at:
File "pggan_runner.py", line 164, in <module>
tf.app.run()
File "/data/anaconda2/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 126, in run
_sys.exit(main(argv))
File "pggan_runner.py", line 154, in main
model.main()
File "/data/TwinGAN/twingan.py", line 894, in main
super(GanModel, self).main()
File "/data/TwinGAN/image_generation.py", line 1054, in main
super(GanModel, self).main()
File "/data/TwinGAN/model/model_inheritor.py", line 1044, in main
'is_training': FLAGS.is_training})
File "/data/TwinGAN/deployment/model_deploy.py", line 237, in create_clones
outputs = model_fn(*args, **kwargs)
File "/data/TwinGAN/twingan.py", line 150, in _clone_fn
data_batched = super(GanModel, GanModel)._get_data_batched(batch_queue, batch_names, data_batched)
File "/data/TwinGAN/model/model_inheritor.py", line 433, in _get_data_batched
dequeued = batch_queue.dequeue()
File "/data/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/data_flow_ops.py", line 440, in dequeue
self._queue_ref, self._dtypes, name=name)
File "/data/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/gen_data_flow_ops.py", line 3734, in queue_dequeue_v2
timeout_ms=timeout_ms, name=name)
File "/data/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/data/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3392, in create_op
op_def=op_def)
File "/data/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1718, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
OutOfRangeError (see above for traceback): FIFOQueue '_5_prefetch_queue/fifo_queue' is closed and has insufficient elements (requested 1, current size 0)
[[Node: fifo_queue_Dequeue = QueueDequeueV2[component_types=[DT_STRING, DT_STRING, DT_STRING, DT_FLOAT, DT_FLOAT, DT_FLOAT], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/device:CPU:0"](prefetch_queue/fifo_queue)]]
INFO:tensorflow:Finished training! Saving model to disk.
Traceback (most recent call last):
File "pggan_runner.py", line 164, in <module>
tf.app.run()
File "/data/anaconda2/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 126, in run
_sys.exit(main(argv))
File "pggan_runner.py", line 154, in main
model.main()
File "/data/TwinGAN/twingan.py", line 894, in main
super(GanModel, self).main()
File "/data/TwinGAN/image_generation.py", line 1054, in main
super(GanModel, self).main()
File "/data/TwinGAN/model/model_inheritor.py", line 1127, in main
session_config=session_config)
File "/data/anaconda2/lib/python2.7/site-packages/tensorflow/contrib/slim/python/slim/learning.py", line 784, in train
ignore_live_threads=ignore_live_threads)
File "/data/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/supervisor.py", line 828, in stop
ignore_live_threads=ignore_live_threads)
File "/data/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/coordinator.py", line 389, in join
six.reraise(*self._exc_info_to_raise)
File "/data/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/queue_runner_impl.py", line 252, in _run
enqueue_callable()
File "/data/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1244, in _single_operation_run
self._call_tf_sessionrun(None, {}, [], target_list, None)
File "/data/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1409, in _call_tf_sessionrun
run_metadata)
tensorflow.python.framework.errors_impl.FailedPreconditionError: test/train-B; Is a directory
[[Node: parallel_read/ReaderReadV2_2 = ReaderReadV2[_device="/job:localhost/replica:0/task:0/device:CPU:0"](parallel_read/TFRecordReaderV2_2, parallel_read/filenames)]]
root@bc5b90e77883:/data/TwinGAN#
How can i solve this error? Please answer to my question.
Thank You!
from twingan.
--dataset_dir="./test/"
is incorrect. It should be where your tfrecord files are "./test/train-A". Similarly for --unpaired_target_dataset_dir
from twingan.
@jerryli27
[Folder Structure]
./test/train-A : result.tflearn, result1.tflearn, result2.tflearn
./test/train-B : Getchu Preprocessed Dataset (train-00000-of-00008, vaildation-00001-of-00002, etc...)
What should i do? i executed the command like this, but it makes error.
python pggan_runner.py --program_name=twingan --dataset_name="image_only" --dataset_dir="./test/train-A" --unpaired_target_dataset_name="anime_faces" --unpaired_target_dataset_dir="./test/train-B" --train_dir="./checkpoints/pggan/" --dataset_split_name=train --preprocessing_name="danbooru" --resize_mode=RANDOM_CROP --do_random_cropping=True --learning_rate=0.0001 --learning_rate_decay_type=fixed --is_training=True --generator_network="pggan" --num_images_per_resolution=50000 --loss_architecture=dragan --pggan_max_num_channels=256 --generator_norm_type=batch_renorm --use_ttur=True
WARNING:tensorflow:IOError [Errno 2] No such file or directory: './test/train-A/sample_source_validation_100.npy'. Not outputting custom generated images.
2018-07-28 02:47:33.716050: W tensorflow/core/kernels/queue_base.cc:285] _1_prefetch_queue/fifo_queue: Skipping cancelled dequeue attempt with queue not closed
INFO:tensorflow:Error reported to Coordinator: <class 'tensorflow.python.framework.errors_impl.InvalidArgumentError'>, assertion failed: [Unable to decode bytes as JPEG, PNG, GIF, or BMP]
[[Node: case/cond/cond_jpeg/decode_image/cond_jpeg/cond_png/cond_gif/Assert_1/Assert = Assert[T=[DT_STRING], summarize=3, _device="/job:localhost/replica:0/task:0/device:CPU:0"](case/cond/cond_jpeg/decode_image/cond_jpeg/cond_png/cond_gif/is_bmp, case/cond/cond_jpeg/decode_image/cond_jpeg/cond_png/cond_gif/Assert_1/Assert/data_0)]]
INFO:tensorflow:Caught OutOfRangeError. Stopping Training. FIFOQueue '_1_prefetch_queue/fifo_queue' is closed and has insufficient elements (requested 1, current size 0)
[[Node: fifo_queue_Dequeue = QueueDequeueV2[component_types=[DT_STRING, DT_STRING, DT_STRING, DT_FLOAT, DT_FLOAT, DT_FLOAT], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/device:CPU:0"](prefetch_queue/fifo_queue)]]
Caused by op u'fifo_queue_Dequeue', defined at:
File "pggan_runner.py", line 164, in <module>
tf.app.run()
File "/data/anaconda2/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 126, in run
_sys.exit(main(argv))
File "pggan_runner.py", line 154, in main
model.main()
File "/data/TwinGAN/twingan.py", line 894, in main
super(GanModel, self).main()
File "/data/TwinGAN/image_generation.py", line 1054, in main
super(GanModel, self).main()
File "/data/TwinGAN/model/model_inheritor.py", line 1044, in main
'is_training': FLAGS.is_training})
File "/data/TwinGAN/deployment/model_deploy.py", line 237, in create_clones
outputs = model_fn(*args, **kwargs)
File "/data/TwinGAN/twingan.py", line 150, in _clone_fn
data_batched = super(GanModel, GanModel)._get_data_batched(batch_queue, batch_names, data_batched)
File "/data/TwinGAN/model/model_inheritor.py", line 433, in _get_data_batched
dequeued = batch_queue.dequeue()
File "/data/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/data_flow_ops.py", line 440, in dequeue
self._queue_ref, self._dtypes, name=name)
File "/data/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/gen_data_flow_ops.py", line 3734, in queue_dequeue_v2
timeout_ms=timeout_ms, name=name)
File "/data/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/data/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3392, in create_op
op_def=op_def)
File "/data/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1718, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
OutOfRangeError (see above for traceback): FIFOQueue '_1_prefetch_queue/fifo_queue' is closed and has insufficient elements (requested 1, current size 0)
[[Node: fifo_queue_Dequeue = QueueDequeueV2[component_types=[DT_STRING, DT_STRING, DT_STRING, DT_FLOAT, DT_FLOAT, DT_FLOAT], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/device:CPU:0"](prefetch_queue/fifo_queue)]]
INFO:tensorflow:Finished training! Saving model to disk.
Traceback (most recent call last):
File "pggan_runner.py", line 164, in <module>
tf.app.run()
File "/data/anaconda2/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 126, in run
_sys.exit(main(argv))
File "pggan_runner.py", line 154, in main
model.main()
File "/data/TwinGAN/twingan.py", line 894, in main
super(GanModel, self).main()
File "/data/TwinGAN/image_generation.py", line 1054, in main
super(GanModel, self).main()
File "/data/TwinGAN/model/model_inheritor.py", line 1127, in main
session_config=session_config)
File "/data/anaconda2/lib/python2.7/site-packages/tensorflow/contrib/slim/python/slim/learning.py", line 784, in train
ignore_live_threads=ignore_live_threads)
File "/data/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/supervisor.py", line 828, in stop
ignore_live_threads=ignore_live_threads)
File "/data/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/coordinator.py", line 389, in join
six.reraise(*self._exc_info_to_raise)
File "/data/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/queue_runner_impl.py", line 252, in _run
enqueue_callable()
File "/data/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1244, in _single_operation_run
self._call_tf_sessionrun(None, {}, [], target_list, None)
File "/data/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1409, in _call_tf_sessionrun
run_metadata)
tensorflow.python.framework.errors_impl.InvalidArgumentError: assertion failed: [Unable to decode bytes as JPEG, PNG, GIF, or BMP]
[[Node: case/cond/cond_jpeg/decode_image/cond_jpeg/cond_png/cond_gif/Assert_1/Assert = Assert[T=[DT_STRING], summarize=3, _device="/job:localhost/replica:0/task:0/device:CPU:0"](case/cond/cond_jpeg/decode_image/cond_jpeg/cond_png/cond_gif/is_bmp, case/cond/cond_jpeg/decode_image/cond_jpeg/cond_png/cond_gif/Assert_1/Assert/data_0)]]
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The error message was " [Unable to decode bytes as JPEG, PNG, GIF, or BMP]"
as displayed. Probably your image encoding was somehow wrong or corrupted.
I will double check that everything provided on this repo works out of the box. Sorry for the inconvenience, but it will take some time. In the mean time, please try to debug yourself as it is probably the fastest way of getting things done and I can't provide 24/7 debugging support.
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Related Issues (20)
- Dataset size HOT 1
- The result of my own training, using the image_translation_infer.py test, looks very bad. HOT 4
- identity preserving? HOT 1
- How to use the corresponding encoder to extract the latent embeddings? HOT 1
- How to use inference/image_translation_infer.py in my trained model?
- AttributeError: 'NoneType' object has no attribute 'shape'
- Inference model trained on Multiple GPU HOT 1
- i have problem in _get_batch function
- face crop HOT 1
- Image synthesis through illust2vec. HOT 2
- Why the channel changed after first downsample HOT 2
- Name of dataset unknown error on CelebA and Getchu Datasets HOT 3
- Implement in a chat bot HOT 1
- Issue ERROR:tensorflow:Cannot parse hostport HOT 2
- How many pictures is the training dataset required for the model? HOT 2
- How many encoders do you use? HOT 2
- Cosine Distance HOT 4
- Trying to train dataset but getting: [Errno 21] Is a directory: '/'
- conditional batch normalization
- cuDNN version HOT 1
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