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KerasCodeExamples.txt

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<tensorflow.python.keras.callbacks.History at 0x7f4433a79a50>
Visualize the outputs of the trained visualizer
plt.figure(figsize=(16, 10))
for i, image in enumerate(sample_images[:6]):
image = image / 255
ax = plt.subplot(3, 4, 2 * i + 1)
plt.title(\"Input Image\")
plt.imshow(image.numpy().squeeze())
plt.axis(\"off\")
ax = plt.subplot(3, 4, 2 * i + 2)
resized_image = learnable_resizer(image[None, ...])
plt.title(\"Resized Image\")
plt.imshow(resized_image.numpy().squeeze() / 10)
plt.axis(\"off\")
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
png
The plot shows that the visuals of the images have improved with training. The following table shows the benefits of using the resizing module in comparison to using the bilinear interpolation:
Model Number of parameters (Million) Top-1 accuracy
With the learnable resizer 7.051717 67.67%
Without the learnable resizer 7.039554 60.19%
For more details, you can check out this repository. Note the above-reported models were trained for 10 epochs on 90% of the training set of Cats and Dogs unlike this example. Also, note that the increase in the number of parameters due to the resizing module is very negligible. To ensure that the improvement in the pe...
Now, a question worth asking here is - isn't the improved accuracy simply a consequence of adding more layers (the resizer is a mini network after all) to the model, compared to the baseline?
To show that it is not the case, the authors conduct the following experiment:
Take a pre-trained model trained some size, say (224 x 224).
Now, first, use it to infer predictions on images resized to a lower resolution. Record the performance.
For the second experiment, plug in the resizer module at the top of the pre-trained model and warm-start the training. Record the performance.
Now, the authors argue that using the second option is better because it helps the model learn how to adjust the representations better with respect to the given resolution. Since the results purely are empirical, a few more experiments such as analyzing the cross-channel interaction would have been even better. It is ...
Notes
To impose shape bias inside the vision models, Geirhos et al. trained them with a combination of natural and stylized images. It might be interesting to investigate if this learnable resizing module could achieve something similar as the outputs seem to discard the texture information.
The resizer module can handle arbitrary resolutions and aspect ratios which is very important for tasks like object detection and segmentation.
There is another closely related topic on adaptive image resizing that attempts to resize images/feature maps adaptively during training. EfficientV2 uses this idea.
Implementing the MIRNet architecture for low-light image enhancement.
Introduction
With the goal of recovering high-quality image content from its degraded version, image restoration enjoys numerous applications, such as in photography, security, medical imaging, and remote sensing. In this example, we implement the MIRNet model for low-light image enhancement, a fully-convolutional architecture that...
References:
Learning Enriched Features for Real Image Restoration and Enhancement
The Retinex Theory of Color Vision
Two deterministic half-quadratic regularization algorithms for computed imaging
Downloading LOLDataset
The LoL Dataset has been created for low-light image enhancement. It provides 485 images for training and 15 for testing. Each image pair in the dataset consists of a low-light input image and its corresponding well-exposed reference image.
import os
import cv2
import random
import numpy as np
from glob import glob
from PIL import Image, ImageOps
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
!gdown https://drive.google.com/uc?id=1DdGIJ4PZPlF2ikl8mNM9V-PdVxVLbQi6
!unzip -q lol_dataset.zip
Downloading...
From: https://drive.google.com/uc?id=1DdGIJ4PZPlF2ikl8mNM9V-PdVxVLbQi6
To: /content/keras-io/scripts/tmp_2614641/lol_dataset.zip
347MB [00:03, 108MB/s]
Creating a TensorFlow Dataset
We use 300 image pairs from the LoL Dataset's training set for training, and we use the remaining 185 image pairs for validation. We generate random crops of size 128 x 128 from the image pairs to be used for both training and validation.
random.seed(10)
IMAGE_SIZE = 128
BATCH_SIZE = 4
MAX_TRAIN_IMAGES = 300
def read_image(image_path):
image = tf.io.read_file(image_path)
image = tf.image.decode_png(image, channels=3)
image.set_shape([None, None, 3])
image = tf.cast(image, dtype=tf.float32) / 255.0
return image
def random_crop(low_image, enhanced_image):
low_image_shape = tf.shape(low_image)[:2]
low_w = tf.random.uniform(
shape=(), maxval=low_image_shape[1] - IMAGE_SIZE + 1, dtype=tf.int32
)
low_h = tf.random.uniform(
shape=(), maxval=low_image_shape[0] - IMAGE_SIZE + 1, dtype=tf.int32