neural_models.py

from datasets import load_dataset
from transformers import AutoImageProcessor, create_optimizer, TFAutoModelForImageClassification, KerasMetricCallback, \
    PushToHubCallback, pipeline
import tensorflow as tf
from tensorflow.python import keras
from keras import layers, losses
import numpy as np
from PIL import Image
from transformers import DefaultDataCollator
import evaluate


def convert_to_tf_tensor(image: Image):
    np_image = np.array(image)
    tf_image = tf.convert_to_tensor(np_image)

    # `expand_dims()` is used to add a batch dimension since
    # the TF augmentation layers operates on batched inputs.
    return tf.expand_dims(tf_image, 0)


def preprocess_train(example_batch):
    """Apply train_transforms across a batch."""
    images = [
        train_data_augmentation(convert_to_tf_tensor(image.convert("RGB"))) for image in example_batch["image"]
    ]
    example_batch["pixel_values"] = [tf.transpose(tf.squeeze(image)) for image in images]
    return example_batch


def preprocess_val(example_batch):
    """Apply val_transforms across a batch."""
    images = [
        val_data_augmentation(convert_to_tf_tensor(image.convert("RGB"))) for image in example_batch["image"]
    ]
    example_batch["pixel_values"] = [tf.transpose(tf.squeeze(image)) for image in images]
    return example_batch


def compute_metrics(eval_pred):
    predictions, labels = eval_pred
    predictions = np.argmax(predictions, axis=1)
    return accuracy.compute(predictions=predictions, references=labels)


# load dataset
food = load_dataset("food101", split="train[:5000]")
# Split into train/test sets
food = food.train_test_split(test_size=0.2)
# an example
print(food["train"][0])

# Map label names to an integer and vice-versa
labels = food["train"].features["label"].names
label2id, id2label = dict(), dict()
for i, label in enumerate(labels):
    label2id[label] = str(i)
    id2label[str(i)] = label

# Should convert label id into a name
print(id2label[str(79)])

# Pre-processing with ViT
# Load image processor to process image into tensor
checkpoint = "google/vit-base-patch16-224-in21k"
image_processor = AutoImageProcessor.from_pretrained(checkpoint)

# To avoid overfitting and make the model more robust, add data augmentation to the training set.
# User Keras preprocessing layers to define transformations for the training set.
size = (image_processor.size["height"], image_processor.size["width"])

train_data_augmentation = keras.Sequential(
    [
        layers.RandomCrop(size[0], size[1]),
        layers.Rescaling(scale=1.0 / 127.5, offset=-1),
        layers.RandomFlip("horizontal"),
        layers.RandomRotation(factor=0.02),
        layers.RandomZoom(height_factor=0.2, width_factor=0.2),
    ],
    name="train_data_augmentation",
)

val_data_augmentation = keras.Sequential(
    [
        layers.CenterCrop(size[0], size[1]),
        layers.Rescaling(scale=1.0 / 127.5, offset=-1),
    ],
    name="val_data_augmentation",
)

food["train"].set_transform(preprocess_train)
food["test"].set_transform(preprocess_val)

data_collator = DefaultDataCollator(return_tensors="tf")

accuracy = evaluate.load("accuracy")

# Set hyperparameters
batch_size = 16
num_epochs = 5
num_train_steps = len(food["train"]) * num_epochs
learning_rate = 3e-5
weight_decay_rate = 0.01

# define optimizer, learning rate schedule
optimizer, lr_schedule = create_optimizer(
    init_lr=learning_rate,
    num_train_steps=num_train_steps,
    weight_decay_rate=weight_decay_rate,
    num_warmup_steps=0,
)

# Load ViT along with label mappings
model = TFAutoModelForImageClassification.from_pretrained(
    checkpoint,
    id2label=id2label,
    label2id=label2id,
)

# converting datasets to tf.data.Dataset
tf_train_dataset = food["train"].to_tf_dataset(
    columns="pixel_values", label_cols="label", shuffle=True, batch_size=batch_size, collate_fn=data_collator
)

tf_eval_dataset = food["test"].to_tf_dataset(
    columns="pixel_values", label_cols="label", shuffle=True, batch_size=batch_size, collate_fn=data_collator
)

# Configure model for training
loss = losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(optimizer=optimizer, loss=loss)


metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_eval_dataset)
push_to_hub_callback = PushToHubCallback(
    output_dir="../food_classifier",
    tokenizer=image_processor,
    save_strategy="no",
)

callbacks = [metric_callback, push_to_hub_callback]

model.fit(tf_train_dataset, validation_data=tf_eval_dataset, epochs=num_epochs) #, callback=callbacks)

ds = load_dataset("food101", split="validation[:10]")
image = ds["image"][0]
classifier = pipeline("image-classification", model="my_awesome_food_model")
print(classifier(image))