CODEWITHSUNDEEP
pythonpython-3.xtensorflowkeras
Barker
Barker

Reputation: 2094

Keras seems to hang after call to fit_generator

I am trying to fit the Keras implementation of the SqueezeDet model to a new dataset. After making the appropriate changes to my config file, I tried to run the train script, but it seems to hang after the call to fit_generator(). As I get the following output:

/anaconda/envs/py35/lib/python3.5/site-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
Using TensorFlow backend.
Number of images: 536
Number of epochs: 100
Number of batches: 53
Batch size: 10
2018-07-04 14:18:49.711606: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2018-07-04 14:18:54.080912: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1356] Found device 0 with properties:
name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235
pciBusID: 52a9:00:00.0
totalMemory: 11.17GiB freeMemory: 11.10GiB
2018-07-04 14:18:54.080958: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1435] Adding visible gpu devices: 0
2018-07-04 14:18:54.333214: I tensorflow/core/common_runtime/gpu/gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-07-04 14:18:54.333270: I tensorflow/core/common_runtime/gpu/gpu_device.cc:929]      0
2018-07-04 14:18:54.333290: I tensorflow/core/common_runtime/gpu/gpu_device.cc:942] 0:   N
2018-07-04 14:18:54.333559: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10764 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 52a9:00:00.0, compute capability: 3.7)
Learning rate: 0.01
Weights initialized by name from ../main/model/imagenet.h5
Using single GPU
Backend Qt5Agg is interactive backend. Turning interactive mode on.
Epoch 1/100

And then nothing happens even if it leave it alone for a day. The call that it seems to freeze on is:

squeeze.model.fit_generator(train_generator, epochs=EPOCHS, verbose=1,
                            steps_per_epoch=nbatches_train, callbacks=cb)

Where the parameters are:

train_generator = generator_from_data_path(img_names, gt_names, config=cfg)
EPOCHS = 100
nbatches_train  = 53
callbacks = [# TensorBoard object, ReduceLROnPlateau object, ModelCheckpoint object #]

My versions:

Python 3.5.4 :: Anaconda custom (64-bit)
tensorflow-gpu : 1.8.0
tensorflow : 1.8.0
Keras : 2.2.0

Upvotes: 5

Views: 7460

Answers (1)

wl2776
wl2776

Reputation: 4327

Formatting conversation in comments to answer.

The culprit was train_generator.

I have looked into sources of model.fit_generator in Keras some time ago. It just retrieves some data from the generator and submits it to the backend, nothing magical :)

So, my hypothesis was that it cannot retrieve data from the generator because the generator does not generate anything.

@Barker has confirmed it, stating that call to next(train_generator) hangs.

I personally have moved to keras.utils.Sequence that supports indexing and length and is much more convenient than ordinary generators. Though this note is not related to the current problem.

Upvotes: 12

Related Questions