I'm trying to implement batch hard triplet loss, as seen in Section 3.2 of https://arxiv.org/pdf/2004.06271.pdf .
I need to import my images so that each batch has exactly K instances of each ID in a particular batch . Therefore, each batch must be a multiple of K .
I have a directory of images too large to fit into memory and therefore I am using ImageDataGenerator.flow_from_directory()
to import the images, but I can't see any parameters for this function to allow the functionality I need.
How can I achieve this batch behaviour using Keras?
As of Tensorflow 2.4 I don't see a standard way of doing that with an ImageDataGenerator
.
So I think you need to write your own based on the tensorflow.keras.utils.Sequence
class, so you are free to define the batch contents yourself.
References:
https://www.tensorflow.org/api_docs/python/tf/keras/utils/Sequence
https://stanford.edu/~shervine/blog/keras-how-to-generate-data-on-the-fly
You can try merging several data streams together in a controlled manner.
Given you have K instances of tf.data.Dataset
(does not matter how you instantiate them) that are responsible for supplying training instances of particular IDs, you can concatenate them to get even distribution inside a mini-batch:
ds1 = ... # Training instances with ID == 1
ds2 = ... # Training instances with ID == 2
...
dsK = ... # Training instances with ID == K
train_dataset = tf.data.Dataset.zip((ds1, ds2, ..., dsK)).flat_map(concat_datasets).batch(batch_size=N * K)
where the concat_datasets
is the merge function:
def concat_datasets(*datasets):
ds = tf.data.Dataset.from_tensors(datasets[0])
for i in range(1, len(datasets)):
ds = ds.concatenate(tf.data.Dataset.from_tensors(datasets[i]))
return ds
The technical post webpages of this site follow the CC BY-SA 4.0 protocol. If you need to reprint, please indicate the site URL or the original address.Any question please contact:yoyou2525@163.com.