Keras VGG16 predict speed slow

Question

I'm working on a feature extractor for this transfer learning personal project, and the predict function of Kera's VGG16 model seems pretty slow (31 seconds for a batch of 4 images). I do expect it to be slow, but not sure if the prediction function is slower than it should be.

data = DataGenerator() 
data = data.from_csv(csv_path=csv_file,
                     img_dir=img_folder,
                     batch_size=batch)

#####################################################
conv_base = VGG16(include_top=False, 
                  weights='imagenet', 
                  input_shape=(480, 640, 3))

model = Sequential()
model.add(conv_base)
model.add(MaxPooling2D(pool_size=(3, 4)))
model.add(Flatten())
######################################################

for inputs, y in data:
    feature_batch = model.predict(inputs)

    yield feature_batch, y

So, my hunch is that it is slow for these reasons:

• my input data is a bit large (loading in (480, 640, 3) size images)
• I am running on a weak CPU (M3-6Y30 @ 0.90GHz)
• I have a flatten operation at the end of the feature extractor.

Things I've tried:

• Other StackOverFlow posts suggested adding a max pooling layer to reduce the feature size / remove the extraneous zero's. I made I think a pretty large max pool window (thus reducing the feature size significantly, but my prediction time increased.
• Batch processing doesn't improve time which is probably obvious due to the use of my M3 CPU). A batch size of 1 image takes 8 seconds, a batch size of 4 takes 32.

Are there any ideas on how to speed up the prediction function? I need to run this through at least 10,000 images, and due to the nature of the project I would like to retain as much of the raw data as possible before going into the model (will be comparing it with other feature extraction models)

All my image files are saved locally, but I can try to setup a cloud computer and move my code over there to run with GPU support.

Is the issue simply I am running the VGG16 model on a dinky CPU?

Guidance would be much appreciated.

Answer 1

There are many issues with your model. The main issue is of course really slow machine, but as you cannot change that here I will state some pieces of advice on how you could speed up your computations:

1. VGG16 is relatively old architecture. The main issue here is that the so-called volume of tensors (area of feature maps times number of features) is decreased really slowly. I would advise you to use more modern architectures like e.g. ResNet50 or Inception v3 as they have the so-called stem which is making inside tensors much smaller really fast. Your speed should benefit thanks to that. There is also a really light architecture called MobileNet which seems perfect for your task.

2. Downsample your images - with a size of (480, 640) your image is 6 times bigger than default VGG input. This makes all computations 6 times slower. You could try to first downsample images and then use a feature extractor.