使用Keras API，如何在给定批次中导入每个ID恰好K个实例的批次图像?

问题描述

我正在尝试实现批量三重损失，如 https://的第3.2节所示arxiv.org/pdf/2004.06271.pdf .

I'm trying to implement batch hard triplet loss, as seen in Section 3.2 of https://arxiv.org/pdf/2004.06271.pdf.

我需要导入图像，以使每个批次在特定批次中每个ID恰好具有K个实例.因此，每个批次必须是K的倍数.

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.

我的图像目录太大，无法容纳到内存中，因此我正在使用 ImageDataGenerator.flow_from_directory()导入图像，但是我看不到该函数的任何参数允许我需要的功能.

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.

如何使用Keras实现这种批处理行为?

推荐答案

您可以尝试以受控方式将多个数据流合并在一起.

You can try merging several data streams together in a controlled manner.

鉴于您有K个 tf.data.Dataset 实例(与实例化方式无关)，它们负责提供特定ID的训练实例，您可以将它们连接起来以在内部均匀分配迷你批处理:

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)

其中 concat_datasets 是合并功能:

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

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