keras predict_generator is shuffling its output when using a keras.utils.Sequence

I am using keras to build a model that inputs 720x1280 images and outputs a value.

I am having a problem with keras.models.Sequential.predict_generator when using the keras.utils.Sequence class to obtain the values corresponding to images on the validation/training sets. The values returned are shuffled, so I don't know which output corresponds to which image.

keras.models.Sequential.predict_generator

keras.utils.Sequence

This is how my generators are defined

from skimage.io import ImageCollection, imread
from keras.utils import Sequence

def load_images(f):
return imread(f).astype(np.float64)

class DataSetImageKeras(Sequence):
def __init__(self, image_collection, values, batch_size):
self.images = image_collection
self.hf = values
self.batch_size = batch_size
self.n = len(self.images)
self.x_scale = 250
self.y_scale = 1e4

def __len__(self):
return int(np.ceil(len(self.images) / float(self.batch_size)))

def __getitem__(self, idx):
# batch_x is a numpy.ndarray
batch_x = (
self.images[idx:min(idx + self.batch_size, self.n)]
.concatenate()
.reshape(self.batch_size, 720, 1280, 1)
)
batch_y = self.hf[idx:min(idx + self.batch_size, self.n)]


return batch_x/self.x_scale, batch_y/self.y_scale

images_train = ImageCollection(images_paths_train, load_func=load_images)
images_val = ImageCollection(images_paths_test, load_func=load_images)

data_train = DataSetImageKeras(images_train, values_train, n_batch)
data_val = DataSetImageKeras(images_val, values_val, n_batch)


from keras.models import load_model
model = load_model('model001') #this model is already trained


If I use the following code:

val_result =
val_hf =
for (batch_x, batch_y) in data_val:
val_result.append(model.predict_on_batch(batch_x))
val_hf.append(batch_y)

val_result = np.concatenate(val_result)
val_hf = np.concatenate(val_hf)

plt.plot(val_hf,
val_result,
marker='.',
linestyle='')


The correct result is obtained (as seen on this image where x is the desired value and y is the predicted value)

However if I use the predict_generator function, as below:

val_result = model.predict_generator(data_val, verbose=1,
workers=1,
max_queue_size=50,
use_multiprocessing=False)


The output is shuffled as can be seen here.

My problem is similar to
#5048 and
#6745,
which should be solved by
#6891 API, but I am using keras version 2.1.6 and it is still shuffling my predictions, even when using workers=1.

workers=1

It is also similar to this, but I didn't find anything that could reset the generators and this problem is still present if I define a new generator and try to run the predict_generator.

predict_generator

I also found something stating that it could have something to do with the number of batches not dividing exactly the number of samples, but this problem is still present if I use n_batch=1

n_batch=1

As a side note, it might be that predict_generator is not shuffling data, but only returning it with an index offset, since the input data on values and images_paths are already shuffled.

values

images_paths

1 Answer
1

predict_generator was not shuffling my predictions, after all. The problem was with the __getitem__ method. For instance, usingn_batch=32, the method would yield values from 1 to 32, then from 2 to 33 and so forth, instead of from 1 to 32, 33 to 64, etc.

predict_generator

__getitem__

n_batch=32

Changing the method as follows solves the problem

def __getitem__(self, idx):
# batch_x is a numpy.ndarray
idx_min = idx*self.batch_size
idx_max = min(idx_min + self.batch_size, self.n)
batch_x = (
self.images[idx_min:idx_max]
.concatenate()
.reshape(self.batch_size, 720, 1280, 1)
)
batch_y = self.hf[idx_min:idx_max]


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