How to Deploy a Trainable Model on iPhone (Step 1: Generate model)

Deploying a trainable machine learning model on an iPhone involves a series of steps to ensure it operates efficiently within the device’s hardware and software constraints. Here’s a concise guide to get we started:

This code implements a hybrid model that combines ResNet50 for image feature extraction with a MobileBERT-like transformer architecture for sequence modeling. Here’s a detailed breakdown:

The code consists of three main components:

1. TransformerBlock Class:

• Implements a custom transformer block with multi-head self-attention and feed-forward layers
• Includes layer normalization and dropout for regularization
• Uses residual connections to help with gradient flow during training

1. MobileBERT-like Model Creation:

• The create_mobilebert_like_model function creates a lightweight transformer model
• Takes sequence of image embeddings as input (from ResNet50)
• Processes them through multiple transformer blocks
• Uses global average pooling and a dense layer for final prediction

1. Data Preparation and Model Training Pipeline:

• Uses ResNet50 (in TFLite format) to generate image embeddings
• Implements a training workflow with:
• Data preprocessing
• Model compilation and training
• Model conversion to TFLite format with different optimization levels
• Multiple quantization approaches (default and full integer quantization)

The main workflow demonstrates how to:

• Load and preprocess images
• Generate embeddings using ResNet50
• Create training sequences
• Train the MobileBERT-like model
• Convert and optimize the model for mobile deployment using TFLite

Key features include:

• Support for sequence modeling of image features
• Multiple optimization techniques for mobile deployment
• Representative dataset generation for quantization
• Both float32 and int8 quantization options

This architecture could be particularly useful for applications requiring sequential image understanding while maintaining efficiency for mobile deployment.

How to run:

Tow commands to run:

source /Volumes/997G/github/ISAT_with_segment_anything/venv/bin/activate
python models/chatgpt/create\ ResNet50\ +\ MobileBERT-Alike\ Model.py

Screen shot:

Generated files:

import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, LayerNormalization, Dropout, MultiHeadAttention, Layer, GlobalAveragePooling1D
from tensorflow.keras.models import Model

# Custom Transformer Block
class TransformerBlock(Layer):
    def __init__(self, embed_dim, num_heads, ff_dim, rate=0.1):
        super(TransformerBlock, self).__init__()
        self.att = MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim)
        self.ffn = tf.keras.Sequential(
            [Dense(ff_dim, activation="relu"), Dense(embed_dim)]
        )
        self.layernorm1 = LayerNormalization(epsilon=1e-6)
        self.layernorm2 = LayerNormalization(epsilon=1e-6)
        self.dropout1 = Dropout(rate)
        self.dropout2 = Dropout(rate)

    def call(self, inputs, training):
        attn_output = self.att(inputs, inputs)  # Self-attention
        attn_output = self.dropout1(attn_output, training=training)
        out1 = self.layernorm1(inputs + attn_output)  # Residual + Normalize
        ffn_output = self.ffn(out1)
        ffn_output = self.dropout2(ffn_output, training=training)
        return self.layernorm2(out1 + ffn_output)  # Residual + Normalize


# Define MobileBERT-like Model
def create_mobilebert_like_model(sequence_length, embedding_dim, num_heads=4, ff_dim=256, num_transformer_blocks=4):
    inputs = Input(shape=(sequence_length, embedding_dim))  # Input: Photo Embeddings
    x = inputs

    # Add Transformer Blocks
    for _ in range(num_transformer_blocks):
        x = TransformerBlock(embed_dim=embedding_dim, num_heads=num_heads, ff_dim=ff_dim)(x, training=True)

    # Pooling Layer
    x = GlobalAveragePooling1D()(x)

    # Final Output: Next Photo Embedding Prediction
    outputs = Dense(embedding_dim)(x)

    model = tf.keras.Model(inputs=inputs, outputs=outputs)
    return model


# Integration with ResNet50 Embeddings
def prepare_data_with_resnet50(image_paths, model_path="assets/models/resnet50_no_head_cannot_init.tflite", embedding_dim=2048):
    # Load TFLite model
    interpreter = tf.lite.Interpreter(model_path=model_path)
    interpreter.allocate_tensors()

    input_details = interpreter.get_input_details()
    output_details = interpreter.get_output_details()

    # Check the expected input shape
    expected_shape = input_details[0]['shape']
    print(f"Expected input shape: {expected_shape}")

    # Prepare embeddings
    embeddings = []
    for image_path in image_paths:
        # Preprocess image
        image = tf.keras.preprocessing.image.load_img(image_path, target_size=(expected_shape[2], expected_shape[3]))
        image = tf.keras.preprocessing.image.img_to_array(image)
        image = tf.expand_dims(image, axis=0)
        image = tf.keras.applications.resnet50.preprocess_input(image)

        # Reorder dimensions to match expected shape [batch_size, channels, height, width]
        image = tf.transpose(image, perm=[0, 3, 1, 2])

        # Ensure the image shape matches the expected input shape
        if image.shape != tuple(expected_shape):
            raise ValueError(f"Image shape {image.shape} does not match expected shape {expected_shape}")

        # Run inference
        interpreter.set_tensor(input_details[0]['index'], image)
        interpreter.invoke()
        embedding = interpreter.get_tensor(output_details[0]['index'])
        embeddings.append(embedding.flatten())

    return embeddings


# Example Workflow
if __name__ == "__main__":
    # Step 1: Load photo paths and prepare embeddings
    photo_paths = ["assets/images/1.jpg", "assets/images/2.jpg", "assets/images/3.jpg", "assets/images/4.jpg", "assets/images/5.jpg", "assets/images/6.jpg", "assets/images/7.jpg", "assets/images/8.jpg", "assets/images/9.jpg", "assets/images/10.jpg", "assets/images/11.jpg"]  # Replace with actual paths
    embeddings = prepare_data_with_resnet50(photo_paths)

    # Step 2: Generate training data
    sequence_length = 10  # Number of previous photos
    embedding_dim = 2048  # Dimension of ResNet50 embeddings

    # Ensure batch_size is non-negative
    if len(photo_paths) > sequence_length:
        batch_size = len(photo_paths) - sequence_length
    else:
        raise ValueError(f"Number of photo paths ({len(photo_paths)}) must be greater than sequence length ({sequence_length})")

    # Generate dummy sequences (for demo purposes, use real data in practice)
    X_train = tf.random.normal((batch_size, sequence_length, embedding_dim))
    y_train = tf.random.normal((batch_size, embedding_dim))

    # Step 3: Train MobileBERT-like Model
    model = create_mobilebert_like_model(sequence_length, embedding_dim)
    model.compile(
        optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
        loss="mean_squared_error",
        metrics=["mae"]
    )

    model.fit(X_train, y_train, epochs=1, batch_size=2)

    # Save Model for TFLite Conversion
    model.save("models/chatgpt/mobilebert_like_model.keras")

    # Convert the model to TensorFlow Lite format with training support
    converter = tf.lite.TFLiteConverter.from_keras_model(model)

    # Enable resource variables to allow training
    converter.experimental_enable_resource_variables = True

    # (Optional) Include TensorFlow ops if necessary
    converter.target_spec.supported_ops = [
        tf.lite.OpsSet.TFLITE_BUILTINS,   # Enable TensorFlow Lite ops
        tf.lite.OpsSet.SELECT_TF_OPS      # Enable TensorFlow ops (if your model uses them)
    ]

    # Convert the model
    tflite_model = converter.convert()

    # Save the TensorFlow Lite model
    with open("models/chatgpt/mobilebert_like_model.tflite", "wb") as f:
        f.write(tflite_model)

    # Convert the model to a TensorFlow Lite model with optimization
    converter = tf.lite.TFLiteConverter.from_keras_model(model)

    # Set the optimization flag to optimize for size
    converter.optimizations = [tf.lite.Optimize.DEFAULT]

    # Optionally, specify representative dataset for better quantization
    def representative_dataset_gen():
        for _ in range(100):
            data = tf.random.normal([1, sequence_length, embedding_dim])
            yield [data]

    converter.representative_dataset = representative_dataset_gen

    # Convert the model
    tflite_model = converter.convert()

    # Save the model to a file
    with open('models/chatgpt/mobilebert_like_quantized_model.tflite', 'wb') as f:
        f.write(tflite_model)

    print("Quantized model saved successfully!")

    # Load the quantized model
    interpreter = tf.lite.Interpreter(model_path="models/chatgpt/mobilebert_like_quantized_model.tflite")
    interpreter.allocate_tensors()

    # Print input and output details
    input_details = interpreter.get_input_details()
    output_details = interpreter.get_output_details()

    print("Input details:", input_details)
    print("Output details:", output_details)

    # Convert the model to TensorFlow Lite format
    converter = tf.lite.TFLiteConverter.from_keras_model(model)

    # Apply full integer quantization
    converter.optimizations = [tf.lite.Optimize.DEFAULT]

    # Specify representative dataset for better quantization
    def representative_dataset_gen():
        for _ in range(100):
            data = tf.random.normal([1, sequence_length, embedding_dim])
            yield [data]

    converter.representative_dataset = representative_dataset_gen

    # Ensure full integer quantization
    converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
    converter.inference_input_type = tf.int8  # or tf.uint8
    converter.inference_output_type = tf.int8  # or tf.uint8

    # Convert the model
    tflite_model = converter.convert()

    # Save the TensorFlow Lite model
    with open("models/chatgpt/mobilebert_like_quantized_model.tflite", "wb") as f:
        f.write(tflite_model)

    print("Quantized model saved successfully!")

    # Load the quantized model
    interpreter = tf.lite.Interpreter(model_path="models/chatgpt/mobilebert_like_quantized_model.tflite")
    interpreter.allocate_tensors()

    # Print input and output details
    input_details = interpreter.get_input_details()
    output_details = interpreter.get_output_details()

    print("Input details:", input_details)
    print("Output details:", output_details)