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KerasCodeExamples.txt

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# Extract input patches.
# B, H, W, K*K*C
input_patches = tf.image.extract_patches(
images=x,
sizes=[1, self.kernel_size, self.kernel_size, 1],
strides=[1, self.stride, self.stride, 1],
rates=[1, 1, 1, 1],
padding=\"SAME\",
)
# Reshape the input patches to align with later operations.
# B, H, W, K*K, C//G, G
input_patches = self.input_patches_reshape(input_patches)
# Compute the multiply-add operation of kernels and patches.
# B, H, W, K*K, C//G, G
output = tf.multiply(kernel, input_patches)
# B, H, W, C//G, G
output = tf.reduce_sum(output, axis=3)
# Reshape the output kernel.
# B, H, W, C
output = self.output_reshape(output)
# Return the output tensor and the kernel.
return output, kernel
Testing the Involution layer
# Define the input tensor.
input_tensor = tf.random.normal((32, 256, 256, 3))
# Compute involution with stride 1.
output_tensor, _ = Involution(
channel=3, group_number=1, kernel_size=5, stride=1, reduction_ratio=1, name=\"inv_1\"
)(input_tensor)
print(f\"with stride 1 ouput shape: {output_tensor.shape}\")
# Compute involution with stride 2.
output_tensor, _ = Involution(
channel=3, group_number=1, kernel_size=5, stride=2, reduction_ratio=1, name=\"inv_2\"
)(input_tensor)
print(f\"with stride 2 ouput shape: {output_tensor.shape}\")
# Compute involution with stride 1, channel 16 and reduction ratio 2.
output_tensor, _ = Involution(
channel=16, group_number=1, kernel_size=5, stride=1, reduction_ratio=2, name=\"inv_3\"
)(input_tensor)
print(
\"with channel 16 and reduction ratio 2 ouput shape: {}\".format(output_tensor.shape)
)
with stride 1 ouput shape: (32, 256, 256, 3)
with stride 2 ouput shape: (32, 128, 128, 3)
with channel 16 and reduction ratio 2 ouput shape: (32, 256, 256, 3)
Image Classification
In this section, we will build an image-classifier model. There will be two models one with convolutions and the other with involutions.
The image-classification model is heavily inspired by this Convolutional Neural Network (CNN) tutorial from Google.
Get the CIFAR10 Dataset
# Load the CIFAR10 dataset.
print(\"loading the CIFAR10 dataset...\")
(train_images, train_labels), (
test_images,
test_labels,
) = keras.datasets.cifar10.load_data()
# Normalize pixel values to be between 0 and 1.
(train_images, test_images) = (train_images / 255.0, test_images / 255.0)
# Shuffle and batch the dataset.
train_ds = (
tf.data.Dataset.from_tensor_slices((train_images, train_labels))
.shuffle(256)
.batch(256)
)
test_ds = tf.data.Dataset.from_tensor_slices((test_images, test_labels)).batch(256)
loading the CIFAR10 dataset...
Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
170500096/170498071 [==============================] - 3s 0us/step
Visualise the data
class_names = [
\"airplane\",
\"automobile\",
\"bird\",
\"cat\",
\"deer\",
\"dog\",
\"frog\",
\"horse\",
\"ship\",
\"truck\",
]
plt.figure(figsize=(10, 10))
for i in range(25):
plt.subplot(5, 5, i + 1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
plt.imshow(train_images[i])