Feature Extraction from Last Global Average Pooling Layer

I am trying to extract features from the last global average pooling layer. but the final tensor after

net = tf.nn.avg_pool3d(net, ksize=[1, 2, 7, 7, 1],
                             strides=[1, 1, 1, 1, 1], padding=snt.VALID)

is of size (1, 6, 1, 1, 1024) Is there a meaning in that ? am I doing something wrong ? I was hoping for only 1 feature vector of size 1x1024 not 6 of them

[mourad1081]

@Anirudh58 and @rishabh2301, I added the max_pool3d(...) line that estathop mentionned here to get the (1, 1, 1, 1, 1024) vector: (in i3d.py)

...
end_point = 'Logits'
with tf.variable_scope(end_point):
    net = tf.nn.avg_pool3d(net, ksize=[1, 2, 7, 7, 1], strides=[1, 1, 1, 1, 1], padding=snt.VALID)
    # To get the features, I add this line because I have a vector (1, x, 1, 1, 1024) otherwise..
    # cf. https://github.com/deepmind/kinetics-i3d/issues/40
    net = tf.nn.max_pool3d(net, ksize=[1, net.get_shape()[1], 1, 1, 1], strides=[1, 1, 1, 1, 1], padding=snt.VALID)
    ...

[Anirudh58]

hello @rishabh2301 I am working on a similar problem statement. Did you figure out how to use this code to extract features?