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KerasCodeExamples.txt

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Setup
import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
# Set seed for reproducibility.
tf.random.set_seed(42)
Convolution
Convolution remains the mainstay of deep neural networks for computer vision. To understand Involution, it is necessary to talk about the convolution operation.
Imgur
Consider an input tensor X with dimensions H, W and C_in. We take a collection of C_out convolution kernels each of shape K, K, C_in. With the multiply-add operation between the input tensor and the kernels we obtain an output tensor Y with dimensions H, W, C_out.
In the diagram above C_out=3. This makes the output tensor of shape H, W and 3. One can notice that the convoltuion kernel does not depend on the spatial position of the input tensor which makes it location-agnostic. On the other hand, each channel in the output tensor is based on a specific convolution filter which ma...
Involution
The idea is to have an operation that is both location-specific and channel-agnostic. Trying to implement these specific properties poses a challenge. With a fixed number of involution kernels (for each spatial position) we will not be able to process variable-resolution input tensors.
To solve this problem, the authors have considered generating each kernel conditioned on specific spatial positions. With this method, we should be able to process variable-resolution input tensors with ease. The diagram below provides an intuition on this kernel generation method.
Imgur
class Involution(keras.layers.Layer):
def __init__(
self, channel, group_number, kernel_size, stride, reduction_ratio, name
):
super().__init__(name=name)
# Initialize the parameters.
self.channel = channel
self.group_number = group_number
self.kernel_size = kernel_size
self.stride = stride
self.reduction_ratio = reduction_ratio
def build(self, input_shape):
# Get the shape of the input.
(_, height, width, num_channels) = input_shape
# Scale the height and width with respect to the strides.
height = height // self.stride
width = width // self.stride
# Define a layer that average pools the input tensor
# if stride is more than 1.
self.stride_layer = (
keras.layers.AveragePooling2D(
pool_size=self.stride, strides=self.stride, padding=\"same\"
)
if self.stride > 1
else tf.identity
)
# Define the kernel generation layer.
self.kernel_gen = keras.Sequential(
[
keras.layers.Conv2D(
filters=self.channel // self.reduction_ratio, kernel_size=1
),
keras.layers.BatchNormalization(),
keras.layers.ReLU(),
keras.layers.Conv2D(
filters=self.kernel_size * self.kernel_size * self.group_number,
kernel_size=1,
),
]
)
# Define reshape layers
self.kernel_reshape = keras.layers.Reshape(
target_shape=(
height,
width,
self.kernel_size * self.kernel_size,
1,
self.group_number,
)
)
self.input_patches_reshape = keras.layers.Reshape(
target_shape=(
height,
width,
self.kernel_size * self.kernel_size,
num_channels // self.group_number,
self.group_number,
)
)
self.output_reshape = keras.layers.Reshape(
target_shape=(height, width, num_channels)
)
def call(self, x):
# Generate the kernel with respect to the input tensor.
# B, H, W, K*K*G
kernel_input = self.stride_layer(x)
kernel = self.kernel_gen(kernel_input)
# reshape the kerenl
# B, H, W, K*K, 1, G
kernel = self.kernel_reshape(kernel)