Data-efficient GANs with Adaptive Discriminator Augmentation to keras 3.0 (Tensorflow backend only) (#2035)

* keras3 migration

* Keras3 migration - Add generated files

Author: chunduriv

examples/generative/gan_ada.py

@@ -2,7 +2,7 @@
 Title: Data-efficient GANs with Adaptive Discriminator Augmentation
 Author: [András Béres](https://www.linkedin.com/in/andras-beres-789190210)
 Date created: 2021/10/28
-Last modified: 2021/10/28
+Last modified: 2025/01/23
 Description: Generating images from limited data using the Caltech Birds dataset.
 Accelerator: GPU
 """
@@ -62,12 +62,17 @@ class of generative deep learning models, commonly used for image generation. Th
 ## Setup
 """
 
+import os
+
+os.environ["KERAS_BACKEND"] = "tensorflow"
+
 import matplotlib.pyplot as plt
 import tensorflow as tf
 import tensorflow_datasets as tfds
 
-from tensorflow import keras
-from tensorflow.keras import layers
+import keras
+from keras import ops
+from keras import layers
 
 """
 ## Hyperparameterers
@@ -115,46 +120,47 @@ class of generative deep learning models, commonly used for image generation. Th
 
 
 def round_to_int(float_value):
-    return tf.cast(tf.math.round(float_value), dtype=tf.int32)
+    return ops.cast(ops.round(float_value), "int32")
 
 
 def preprocess_image(data):
     # unnormalize bounding box coordinates
-    height = tf.cast(tf.shape(data["image"])[0], dtype=tf.float32)
-    width = tf.cast(tf.shape(data["image"])[1], dtype=tf.float32)
-    bounding_box = data["bbox"] * tf.stack([height, width, height, width])
+    height = ops.cast(ops.shape(data["image"])[0], "float32")
+    width = ops.cast(ops.shape(data["image"])[1], "float32")
+    bounding_box = data["bbox"] * ops.stack([height, width, height, width])
 
     # calculate center and length of longer side, add padding
     target_center_y = 0.5 * (bounding_box[0] + bounding_box[2])
     target_center_x = 0.5 * (bounding_box[1] + bounding_box[3])
-    target_size = tf.maximum(
+    target_size = ops.maximum(
         (1.0 + padding) * (bounding_box[2] - bounding_box[0]),
         (1.0 + padding) * (bounding_box[3] - bounding_box[1]),
     )
 
     # modify crop size to fit into image
-    target_height = tf.reduce_min(
+    target_height = ops.min(
         [target_size, 2.0 * target_center_y, 2.0 * (height - target_center_y)]
     )
-    target_width = tf.reduce_min(
+    target_width = ops.min(
         [target_size, 2.0 * target_center_x, 2.0 * (width - target_center_x)]
     )
 
-    # crop image
-    image = tf.image.crop_to_bounding_box(
+    # crop image, `ops.image.crop_images` only works with non-tensor croppings
+    image = ops.slice(
         data["image"],
-        offset_height=round_to_int(target_center_y - 0.5 * target_height),
-        offset_width=round_to_int(target_center_x - 0.5 * target_width),
-        target_height=round_to_int(target_height),
-        target_width=round_to_int(target_width),
+        start_indices=(
+            round_to_int(target_center_y - 0.5 * target_height),
+            round_to_int(target_center_x - 0.5 * target_width),
+            0,
+        ),
+        shape=(round_to_int(target_height), round_to_int(target_width), 3),
     )
 
     # resize and clip
-    # for image downsampling, area interpolation is the preferred method
-    image = tf.image.resize(
-        image, size=[image_size, image_size], method=tf.image.ResizeMethod.AREA
-    )
-    return tf.clip_by_value(image / 255.0, 0.0, 1.0)
+    image = ops.cast(image, "float32")
+    image = ops.image.resize(image, [image_size, image_size])
+
+    return ops.clip(image / 255.0, 0.0, 1.0)
 
 
 def prepare_dataset(split):
@@ -231,8 +237,10 @@ def __init__(self, name="kid", **kwargs):
         )
 
     def polynomial_kernel(self, features_1, features_2):
-        feature_dimensions = tf.cast(tf.shape(features_1)[1], dtype=tf.float32)
-        return (features_1 @ tf.transpose(features_2) / feature_dimensions + 1.0) ** 3.0
+        feature_dimensions = ops.cast(ops.shape(features_1)[1], "float32")
+        return (
+            features_1 @ ops.transpose(features_2) / feature_dimensions + 1.0
+        ) ** 3.0
 
     def update_state(self, real_images, generated_images, sample_weight=None):
         real_features = self.encoder(real_images, training=False)
@@ -246,15 +254,15 @@ def update_state(self, real_images, generated_images, sample_weight=None):
         kernel_cross = self.polynomial_kernel(real_features, generated_features)
 
         # estimate the squared maximum mean discrepancy using the average kernel values
-        batch_size = tf.shape(real_features)[0]
-        batch_size_f = tf.cast(batch_size, dtype=tf.float32)
-        mean_kernel_real = tf.reduce_sum(kernel_real * (1.0 - tf.eye(batch_size))) / (
+        batch_size = ops.shape(real_features)[0]
+        batch_size_f = ops.cast(batch_size, "float32")
+        mean_kernel_real = ops.sum(kernel_real * (1.0 - ops.eye(batch_size))) / (
             batch_size_f * (batch_size_f - 1.0)
         )
-        mean_kernel_generated = tf.reduce_sum(
-            kernel_generated * (1.0 - tf.eye(batch_size))
+        mean_kernel_generated = ops.sum(
+            kernel_generated * (1.0 - ops.eye(batch_size))
         ) / (batch_size_f * (batch_size_f - 1.0))
-        mean_kernel_cross = tf.reduce_mean(kernel_cross)
+        mean_kernel_cross = ops.mean(kernel_cross)
         kid = mean_kernel_real + mean_kernel_generated - 2.0 * mean_kernel_cross
 
         # update the average KID estimate
@@ -299,7 +307,7 @@ def reset_state(self):
 # "hard sigmoid", useful for binary accuracy calculation from logits
 def step(values):
     # negative values -> 0.0, positive values -> 1.0
-    return 0.5 * (1.0 + tf.sign(values))
+    return 0.5 * (1.0 + ops.sign(values))
 
 
 # augments images with a probability that is dynamically updated during training
@@ -308,7 +316,8 @@ def __init__(self):
         super().__init__()
 
         # stores the current probability of an image being augmented
-        self.probability = tf.Variable(0.0)
+        self.probability = keras.Variable(0.0)
+        self.seed_generator = keras.random.SeedGenerator(42)
 
         # the corresponding augmentation names from the paper are shown above each layer
         # the authors show (see figure 4), that the blitting and geometric augmentations
@@ -336,28 +345,26 @@ def __init__(self):
 
     def call(self, images, training):
         if training:
-            augmented_images = self.augmenter(images, training)
+            augmented_images = self.augmenter(images, training=training)
 
             # during training either the original or the augmented images are selected
             # based on self.probability
-            augmentation_values = tf.random.uniform(
-                shape=(batch_size, 1, 1, 1), minval=0.0, maxval=1.0
+            augmentation_values = keras.random.uniform(
+                shape=(batch_size, 1, 1, 1), seed=self.seed_generator
             )
-            augmentation_bools = tf.math.less(augmentation_values, self.probability)
+            augmentation_bools = ops.less(augmentation_values, self.probability)
 
-            images = tf.where(augmentation_bools, augmented_images, images)
+            images = ops.where(augmentation_bools, augmented_images, images)
         return images
 
     def update(self, real_logits):
-        current_accuracy = tf.reduce_mean(step(real_logits))
+        current_accuracy = ops.mean(step(real_logits))
 
         # the augmentation probability is updated based on the discriminator's
         # accuracy on real images
         accuracy_error = current_accuracy - target_accuracy
         self.probability.assign(
-            tf.clip_by_value(
-                self.probability + accuracy_error / integration_steps, 0.0, 1.0
-            )
+            ops.clip(self.probability + accuracy_error / integration_steps, 0.0, 1.0)
         )
 
 
@@ -445,13 +452,17 @@ class GAN_ADA(keras.Model):
     def __init__(self):
         super().__init__()
 
+        self.seed_generator = keras.random.SeedGenerator(seed=42)
         self.augmenter = AdaptiveAugmenter()
         self.generator = get_generator()
         self.ema_generator = keras.models.clone_model(self.generator)
         self.discriminator = get_discriminator()
 
         self.generator.summary()
         self.discriminator.summary()
+        # we have created all layers at this point, so we can mark the model
+        # as having been built
+        self.built = True
 
     def compile(self, generator_optimizer, discriminator_optimizer, **kwargs):
         super().compile(**kwargs)
@@ -479,32 +490,34 @@ def metrics(self):
         ]
 
     def generate(self, batch_size, training):
-        latent_samples = tf.random.normal(shape=(batch_size, noise_size))
+        latent_samples = keras.random.normal(
+            shape=(batch_size, noise_size), seed=self.seed_generator
+        )
         # use ema_generator during inference
         if training:
-            generated_images = self.generator(latent_samples, training)
+            generated_images = self.generator(latent_samples, training=training)
         else:
-            generated_images = self.ema_generator(latent_samples, training)
+            generated_images = self.ema_generator(latent_samples, training=training)
         return generated_images
 
     def adversarial_loss(self, real_logits, generated_logits):
         # this is usually called the non-saturating GAN loss
 
-        real_labels = tf.ones(shape=(batch_size, 1))
-        generated_labels = tf.zeros(shape=(batch_size, 1))
+        real_labels = ops.ones(shape=(batch_size, 1))
+        generated_labels = ops.zeros(shape=(batch_size, 1))
 
         # the generator tries to produce images that the discriminator considers as real
         generator_loss = keras.losses.binary_crossentropy(
             real_labels, generated_logits, from_logits=True
         )
         # the discriminator tries to determine if images are real or generated
         discriminator_loss = keras.losses.binary_crossentropy(
-            tf.concat([real_labels, generated_labels], axis=0),
-            tf.concat([real_logits, generated_logits], axis=0),
+            ops.concatenate([real_labels, generated_labels], axis=0),
+            ops.concatenate([real_logits, generated_logits], axis=0),
             from_logits=True,
         )
 
-        return tf.reduce_mean(generator_loss), tf.reduce_mean(discriminator_loss)
+        return ops.mean(generator_loss), ops.mean(discriminator_loss)
 
     def train_step(self, real_images):
         real_images = self.augmenter(real_images, training=True)
@@ -604,8 +617,8 @@ def plot_images(self, epoch=None, logs=None, num_rows=3, num_cols=6, interval=5)
 )
 
 # save the best model based on the validation KID metric
-checkpoint_path = "gan_model"
-checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
+checkpoint_path = "gan_model.weights.h5"
+checkpoint_callback = keras.callbacks.ModelCheckpoint(
     filepath=checkpoint_path,
     save_weights_only=True,
     monitor="val_kid",