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I'm trying to resize some images to use them with Inception. I want to do it as a separate preprocessing step to speed things up later. Running tf.image.resize on all of the images at once crashes, as does a loop.

I'd like to do it in batches, but I don't know how to do that with making it part of my neural network model - I want to keep it outside as a separate preprocessing step.

I made this:

inception = tf.keras.applications.inception_v3.InceptionV3(include_top=True, input_shape=(299, 299, 3))
inception = tf.keras.Model([inception.input], [inception.layers[-2].output])

for layer in inception.layers:
layer.trainable = False

resize_incept = tf.keras.Sequential([
    tf.keras.layers.Resizing(299, 299),
    inception])

resize_incept.compile()

So can I just call it on my images? But then how do I batch it? When I call it like resize_incept(images) it crashes (too big), but if I call resize_incept(images, batch_size = 25), it doesn't work (TypeError: call() got an unexpected keyword argument 'batch_size').

EDIT: I'm trying to figure out if I can use tf.data.dataset for this EDIT2: I've put my data (which was an array of (batch, 32, 32, 3) into tf.data.dataset so I can try this:

train_dataset = train_dataset.map(lambda x, y: (resize_incept(x), y))

But when I try it, it give me this error:

ValueError: Input 0 is incompatible with layer model_1: expected shape=(None, 299, 299, 3), found shape=(299, 299, 3)

The problem seems to be that whatever is coming out of the resize layer is somehow wrong for going into the inception layer (because what I'm putting in at first is (32,32,3) and there are no complaints about those dimensions)? But the inception layer, already has input_shape=(299, 299, 3) so I would think that's what shape it would take?

midnightradi0
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1 Answers1

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If you want it as a seperate preprocessing step. I would recommend to use the image_dataset_from_directory() function included in keras preprocesssing.

tf.keras.preprocessing.image_dataset_from_directory(
directory,
labels="inferred",
label_mode="int",
class_names=None,
color_mode="rgb",
batch_size=32,
image_size=(256, 256),
shuffle=True,
seed=None,
validation_split=None,
subset=None,
interpolation="bilinear",
follow_links=False,
crop_to_aspect_ratio=False,
**kwargs)

You can manipulate the batch size and the wanted image size and choose wether to split the data or not. Also, be carefull with crop_to_aspect_ratio as you may lose important features from the cropped areas. If you want to know more about it's parameters and a code example you could check the image_dataset_from_directory documentation. Good luck!

Omar Yasser
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