diff --git a/examples/keras_io/tensorflow/vision/cutmix.py b/examples/keras_io/tensorflow/vision/cutmix.py index e9685112a..2601fa693 100644 --- a/examples/keras_io/tensorflow/vision/cutmix.py +++ b/examples/keras_io/tensorflow/vision/cutmix.py @@ -3,7 +3,7 @@ Author: [Sayan Nath](https://twitter.com/sayannath2350) Converted to Keras Core By: [Piyush Thakur](https://github.com/cosmo3769) Date created: 2021/06/08 -Last modified: 2023/07/18 +Last modified: 2023/07/24 Description: Data augmentation with CutMix for image classification on CIFAR-10. Accelerator: GPU """ @@ -48,10 +48,16 @@ import numpy as np import pandas as pd +import keras_core as keras import matplotlib.pyplot as plt -import tensorflow as tf + from keras_core import layers -import keras_core as keras + +# TF imports related to tf.data preprocessing +from tensorflow import clip_by_value +from tensorflow import data as tf_data +from tensorflow import image as tf_image +from tensorflow.random import gamma as tf_random_gamma keras.utils.set_random_seed(42) @@ -88,7 +94,7 @@ ## Define hyperparameters """ -AUTO = tf.data.AUTOTUNE +AUTO = tf_data.AUTOTUNE BATCH_SIZE = 32 IMG_SIZE = 32 @@ -98,9 +104,9 @@ def preprocess_image(image, label): - image = tf.image.resize(image, (IMG_SIZE, IMG_SIZE)) - image = tf.image.convert_image_dtype(image, tf.float32) / 255.0 - label = tf.cast(label, tf.float32) + image = tf_image.resize(image, (IMG_SIZE, IMG_SIZE)) + image = tf_image.convert_image_dtype(image, "float32") / 255.0 + label = keras.backend.cast(label, dtype="float32") return image, label @@ -109,19 +115,19 @@ def preprocess_image(image, label): """ train_ds_one = ( - tf.data.Dataset.from_tensor_slices((x_train, y_train)) + tf_data.Dataset.from_tensor_slices((x_train, y_train)) .shuffle(1024) .map(preprocess_image, num_parallel_calls=AUTO) ) train_ds_two = ( - tf.data.Dataset.from_tensor_slices((x_train, y_train)) + tf_data.Dataset.from_tensor_slices((x_train, y_train)) .shuffle(1024) .map(preprocess_image, num_parallel_calls=AUTO) ) -train_ds_simple = tf.data.Dataset.from_tensor_slices((x_train, y_train)) +train_ds_simple = tf_data.Dataset.from_tensor_slices((x_train, y_train)) -test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)) +test_ds = tf_data.Dataset.from_tensor_slices((x_test, y_test)) train_ds_simple = ( train_ds_simple.map(preprocess_image, num_parallel_calls=AUTO) @@ -130,7 +136,7 @@ def preprocess_image(image, label): ) # Combine two shuffled datasets from the same training data. -train_ds = tf.data.Dataset.zip((train_ds_one, train_ds_two)) +train_ds = tf_data.Dataset.zip((train_ds_one, train_ds_two)) test_ds = ( test_ds.map(preprocess_image, num_parallel_calls=AUTO) @@ -146,28 +152,29 @@ def preprocess_image(image, label): def sample_beta_distribution(size, concentration_0=0.2, concentration_1=0.2): - gamma_1_sample = tf.random.gamma(shape=[size], alpha=concentration_1) - gamma_2_sample = tf.random.gamma(shape=[size], alpha=concentration_0) + gamma_1_sample = tf_random_gamma(shape=[size], alpha=concentration_1) + gamma_2_sample = tf_random_gamma(shape=[size], alpha=concentration_0) return gamma_1_sample / (gamma_1_sample + gamma_2_sample) -@tf.function def get_box(lambda_value): - cut_rat = tf.math.sqrt(1.0 - lambda_value) + cut_rat = keras.ops.sqrt(1.0 - lambda_value) cut_w = IMG_SIZE * cut_rat # rw - cut_w = tf.cast(cut_w, tf.int32) + cut_w = keras.backend.cast(cut_w, "int32") cut_h = IMG_SIZE * cut_rat # rh - cut_h = tf.cast(cut_h, tf.int32) + cut_h = keras.backend.cast(cut_h, "int32") - cut_x = tf.random.uniform((1,), minval=0, maxval=IMG_SIZE, dtype=tf.int32) # rx - cut_y = tf.random.uniform((1,), minval=0, maxval=IMG_SIZE, dtype=tf.int32) # ry + cut_x = keras.random.random.uniform((1,), minval=0, maxval=IMG_SIZE) # rx + cut_x = keras.backend.cast(cut_x, "int32") + cut_y = keras.random.random.uniform((1,), minval=0, maxval=IMG_SIZE) # ry + cut_y = keras.backend.cast(cut_y, "int32") - boundaryx1 = tf.clip_by_value(cut_x[0] - cut_w // 2, 0, IMG_SIZE) - boundaryy1 = tf.clip_by_value(cut_y[0] - cut_h // 2, 0, IMG_SIZE) - bbx2 = tf.clip_by_value(cut_x[0] + cut_w // 2, 0, IMG_SIZE) - bby2 = tf.clip_by_value(cut_y[0] + cut_h // 2, 0, IMG_SIZE) + boundaryx1 = clip_by_value(cut_x[0] - cut_w // 2, 0, IMG_SIZE) + boundaryy1 = clip_by_value(cut_y[0] - cut_h // 2, 0, IMG_SIZE) + bbx2 = clip_by_value(cut_x[0] + cut_w // 2, 0, IMG_SIZE) + bby2 = clip_by_value(cut_y[0] + cut_h // 2, 0, IMG_SIZE) target_h = bby2 - boundaryy1 if target_h == 0: @@ -180,7 +187,6 @@ def get_box(lambda_value): return boundaryx1, boundaryy1, target_h, target_w -@tf.function def cutmix(train_ds_one, train_ds_two): (image1, label1), (image2, label2) = train_ds_one, train_ds_two @@ -197,19 +203,19 @@ def cutmix(train_ds_one, train_ds_two): boundaryx1, boundaryy1, target_h, target_w = get_box(lambda_value) # Get a patch from the second image (`image2`) - crop2 = tf.image.crop_to_bounding_box( + crop2 = tf_image.crop_to_bounding_box( image2, boundaryy1, boundaryx1, target_h, target_w ) # Pad the `image2` patch (`crop2`) with the same offset - image2 = tf.image.pad_to_bounding_box( + image2 = tf_image.pad_to_bounding_box( crop2, boundaryy1, boundaryx1, IMG_SIZE, IMG_SIZE ) # Get a patch from the first image (`image1`) - crop1 = tf.image.crop_to_bounding_box( + crop1 = tf_image.crop_to_bounding_box( image1, boundaryy1, boundaryx1, target_h, target_w ) # Pad the `image1` patch (`crop1`) with the same offset - img1 = tf.image.pad_to_bounding_box( + img1 = tf_image.pad_to_bounding_box( crop1, boundaryy1, boundaryx1, IMG_SIZE, IMG_SIZE ) @@ -221,7 +227,7 @@ def cutmix(train_ds_one, train_ds_two): # Adjust Lambda in accordance to the pixel ration lambda_value = 1 - (target_w * target_h) / (IMG_SIZE * IMG_SIZE) - lambda_value = tf.cast(lambda_value, tf.float32) + lambda_value = keras.backend.cast(lambda_value, "float32") # Combine the labels of both images label = lambda_value * label1 + (1 - lambda_value) * label2 @@ -371,7 +377,7 @@ def training_model(): In this example, we trained our model for 15 epochs. In our experiment, the model with CutMix achieves a better accuracy on the CIFAR-10 dataset -(76.92% in our experiment) compared to the model that doesn't use the augmentation (72.23%). +(77.34% in our experiment) compared to the model that doesn't use the augmentation (66.90%). You may notice it takes less time to train the model with the CutMix augmentation. You can experiment further with the CutMix technique by following the diff --git a/examples/keras_io/tensorflow/vision/mixup.py b/examples/keras_io/tensorflow/vision/mixup.py index 34cf72727..bb4018f39 100644 --- a/examples/keras_io/tensorflow/vision/mixup.py +++ b/examples/keras_io/tensorflow/vision/mixup.py @@ -2,7 +2,7 @@ Title: MixUp augmentation for image classification Author: [Sayak Paul](https://twitter.com/RisingSayak) Date created: 2021/03/06 -Last modified: 2021/03/06 +Last modified: 2023/07/24 Description: Data augmentation using the mixup technique for image classification. Accelerator: GPU """ @@ -37,10 +37,15 @@ """ import numpy as np -import tensorflow as tf +import keras_core as keras import matplotlib.pyplot as plt + from keras_core import layers -import keras_core as keras + +# TF imports related to tf.data preprocessing +from tensorflow import data as tf_data +from tensorflow import image as tf_image +from tensorflow.random import gamma as tf_random_gamma """ ## Prepare the dataset @@ -53,17 +58,17 @@ x_train = x_train.astype("float32") / 255.0 x_train = np.reshape(x_train, (-1, 28, 28, 1)) -y_train = tf.one_hot(y_train, 10) +y_train = keras.ops.one_hot(y_train, 10) x_test = x_test.astype("float32") / 255.0 x_test = np.reshape(x_test, (-1, 28, 28, 1)) -y_test = tf.one_hot(y_test, 10) +y_test = keras.ops.one_hot(y_test, 10) """ ## Define hyperparameters """ -AUTO = tf.data.AUTOTUNE +AUTO = tf_data.AUTOTUNE BATCH_SIZE = 64 EPOCHS = 10 @@ -77,22 +82,22 @@ new_x_train, new_y_train = x_train[val_samples:], y_train[val_samples:] train_ds_one = ( - tf.data.Dataset.from_tensor_slices((new_x_train, new_y_train)) + tf_data.Dataset.from_tensor_slices((new_x_train, new_y_train)) .shuffle(BATCH_SIZE * 100) .batch(BATCH_SIZE) ) train_ds_two = ( - tf.data.Dataset.from_tensor_slices((new_x_train, new_y_train)) + tf_data.Dataset.from_tensor_slices((new_x_train, new_y_train)) .shuffle(BATCH_SIZE * 100) .batch(BATCH_SIZE) ) # Because we will be mixing up the images and their corresponding labels, we will be # combining two shuffled datasets from the same training data. -train_ds = tf.data.Dataset.zip((train_ds_one, train_ds_two)) +train_ds = tf_data.Dataset.zip((train_ds_one, train_ds_two)) -val_ds = tf.data.Dataset.from_tensor_slices((x_val, y_val)).batch(BATCH_SIZE) +val_ds = tf_data.Dataset.from_tensor_slices((x_val, y_val)).batch(BATCH_SIZE) -test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(BATCH_SIZE) +test_ds = tf_data.Dataset.from_tensor_slices((x_test, y_test)).batch(BATCH_SIZE) """ ## Define the mixup technique function @@ -105,8 +110,8 @@ def sample_beta_distribution(size, concentration_0=0.2, concentration_1=0.2): - gamma_1_sample = tf.random.gamma(shape=[size], alpha=concentration_1) - gamma_2_sample = tf.random.gamma(shape=[size], alpha=concentration_0) + gamma_1_sample = tf_random_gamma(shape=[size], alpha=concentration_1) + gamma_2_sample = tf_random_gamma(shape=[size], alpha=concentration_0) return gamma_1_sample / (gamma_1_sample + gamma_2_sample) @@ -114,12 +119,12 @@ def mix_up(ds_one, ds_two, alpha=0.2): # Unpack two datasets images_one, labels_one = ds_one images_two, labels_two = ds_two - batch_size = tf.shape(images_one)[0] + batch_size = keras.backend.shape(images_one)[0] # Sample lambda and reshape it to do the mixup l = sample_beta_distribution(batch_size, alpha, alpha) - x_l = tf.reshape(l, (batch_size, 1, 1, 1)) - y_l = tf.reshape(l, (batch_size, 1)) + x_l = keras.ops.reshape(l, (batch_size, 1, 1, 1)) + y_l = keras.ops.reshape(l, (batch_size, 1)) # Perform mixup on both images and labels by combining a pair of images/labels # (one from each dataset) into one image/label