问题
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
回答1:
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.
来源：https://stackoverflow.com/questions/51176661/keras-seems-to-hang-after-call-to-fit-generator