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"""
Explanation: Copyright 2021 The TensorFlow Authors.
End of explanation
"""


! pip install -q tensorflow-model-optimization

import tensorflow as tf

import numpy as np
import tempfile
import zipfile
import os

"""
Explanation: Sparsity preserving clustering Keras example
<table class="tfo-notebook-buttons" align="left">
  <td>
    <a target="_blank" href="https://www.tensorflow.org/model_optimization/guide/combine/sparse_clustering_example"><img src="https://www.tensorflow.org/images/tf_logo_32px.png" />View on TensorFlow.org</a>
  </td>
  <td>
    <a target="_blank" href="https://colab.research.google.com/github/tensorflow/model-optimization/blob/master/tensorflow_model_optimization/g3doc/guide/combine/sparse_clustering_example.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" />Run in Google Colab</a>
  </td>
  <td>
    <a target="_blank" href="https://github.com/tensorflow/model-optimization/blob/master/tensorflow_model_optimization/g3doc/guide/combine/sparse_clustering_example.ipynb"><img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" />View on GitHub</a>
  </td>
  <td>
    <a href="https://storage.googleapis.com/tensorflow_docs/model-optimization/tensorflow_model_optimization/g3doc/guide/combine/sparse_clustering_example.ipynb"><img src="https://www.tensorflow.org/images/download_logo_32px.png" />Download notebook</a>
  </td>
</table>

Overview
This is an end to end example showing the usage of the sparsity preserving clustering API, part of the TensorFlow Model Optimization Toolkit's collaborative optimization pipeline.
Other pages
For an introduction to the pipeline and other available techniques, see the collaborative optimization overview page.
Contents
In the tutorial, you will:

Train a tf.keras model for the MNIST dataset from scratch.
Fine-tune the model with sparsity and see the accuracy and observe that the model was successfully pruned.
Apply weight clustering to the pruned model and observe the loss of sparsity.
Apply sparsity preserving clustering on the pruned model and observe that the sparsity applied earlier has been preserved.
Generate a TFLite model and check that the accuracy has been preserved in the pruned clustered model.
Compare the sizes of the different models to observe the compression benefits of applying sparsity followed by the collaborative optimization technique of sparsity preserving clustering.

Setup
You can run this Jupyter Notebook in your local virtualenv or colab. For details of setting up dependencies, please refer to the installation guide.
End of explanation
"""


# Load MNIST dataset
mnist = tf.keras.datasets.mnist
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

# Normalize the input image so that each pixel value is between 0 to 1.
train_images = train_images / 255.0
test_images  = test_images / 255.0

model = tf.keras.Sequential([
  tf.keras.layers.InputLayer(input_shape=(28, 28)),
  tf.keras.layers.Reshape(target_shape=(28, 28, 1)),
  tf.keras.layers.Conv2D(filters=12, kernel_size=(3, 3),
                         activation=tf.nn.relu),
  tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(10)
])

# Train the digit classification model
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

model.fit(
    train_images,
    train_labels,
    validation_split=0.1,
    epochs=10
)

"""
Explanation: Train a tf.keras model for MNIST to be pruned and clustered
End of explanation
"""


_, baseline_model_accuracy = model.evaluate(
    test_images, test_labels, verbose=0)

print('Baseline test accuracy:', baseline_model_accuracy)

_, keras_file = tempfile.mkstemp('.h5')
print('Saving model to: ', keras_file)
tf.keras.models.save_model(model, keras_file, include_optimizer=False)

"""
Explanation: Evaluate the baseline model and save it for later usage
End of explanation
"""


import tensorflow_model_optimization as tfmot

prune_low_magnitude = tfmot.sparsity.keras.prune_low_magnitude

pruning_params = {
      'pruning_schedule': tfmot.sparsity.keras.ConstantSparsity(0.5, begin_step=0, frequency=100)
  }

callbacks = [
  tfmot.sparsity.keras.UpdatePruningStep()
]

pruned_model = prune_low_magnitude(model, **pruning_params)

# Use smaller learning rate for fine-tuning
opt = tf.keras.optimizers.Adam(learning_rate=1e-5)

pruned_model.compile(
  loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
  optimizer=opt,
  metrics=['accuracy'])

pruned_model.summary()

"""
Explanation: Prune and fine-tune the model to 50% sparsity
Apply the prune_low_magnitude() API to prune the whole pre-trained model to achieve the model that is to be clustered in the next step. For how best to use the API to achieve the best compression rate while maintaining your target accuracy, refer to the pruning comprehensive guide.
Define the model and apply the sparsity API
Note that the pre-trained model is used.
End of explanation
"""


# Fine-tune model
pruned_model.fit(
  train_images,
  train_labels,
  epochs=3,
  validation_split=0.1,
  callbacks=callbacks)

"""
Explanation: Fine-tune the model, check sparsity, and evaluate the accuracy against baseline
Fine-tune the model with pruning for 3 epochs.
End of explanation
"""


def print_model_weights_sparsity(model):

    for layer in model.layers:
        if isinstance(layer, tf.keras.layers.Wrapper):
            weights = layer.trainable_weights
        else:
            weights = layer.weights
        for weight in weights:
            if "kernel" not in weight.name or "centroid" in weight.name:
                continue
            weight_size = weight.numpy().size
            zero_num = np.count_nonzero(weight == 0)
            print(
                f"{weight.name}: {zero_num/weight_size:.2%} sparsity ",
                f"({zero_num}/{weight_size})",
            )

"""
Explanation: Define helper functions to calculate and print the sparsity of the model.
End of explanation
"""


stripped_pruned_model = tfmot.sparsity.keras.strip_pruning(pruned_model)

print_model_weights_sparsity(stripped_pruned_model)

stripped_pruned_model_copy = tf.keras.models.clone_model(stripped_pruned_model)
stripped_pruned_model_copy.set_weights(stripped_pruned_model.get_weights())

"""
Explanation: Check that the model kernels was correctly pruned. We need to strip the pruning wrapper first. We also create a deep copy of the model to be used in the next step.
End of explanation
"""


# Clustering
cluster_weights = tfmot.clustering.keras.cluster_weights
CentroidInitialization = tfmot.clustering.keras.CentroidInitialization

clustering_params = {
  'number_of_clusters': 8,
  'cluster_centroids_init': CentroidInitialization.KMEANS_PLUS_PLUS
}

clustered_model = cluster_weights(stripped_pruned_model, **clustering_params)

clustered_model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

print('Train clustering model:')
clustered_model.fit(train_images, train_labels,epochs=3, validation_split=0.1)


stripped_pruned_model.save("stripped_pruned_model_clustered.h5")

# Sparsity preserving clustering
from tensorflow_model_optimization.python.core.clustering.keras.experimental import (
    cluster,
)

cluster_weights = cluster.cluster_weights

clustering_params = {
  'number_of_clusters': 8,
  'cluster_centroids_init': CentroidInitialization.KMEANS_PLUS_PLUS,
  'preserve_sparsity': True
}

sparsity_clustered_model = cluster_weights(stripped_pruned_model_copy, **clustering_params)

sparsity_clustered_model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

print('Train sparsity preserving clustering model:')
sparsity_clustered_model.fit(train_images, train_labels,epochs=3, validation_split=0.1)

"""
Explanation: Apply clustering and sparsity preserving clustering and check its effect on model sparsity in both cases
Next, we apply both clustering and sparsity preserving clustering on the pruned model and observe that the latter preserves sparsity on your pruned model. Note that we stripped pruning wrappers from the pruned model with tfmot.sparsity.keras.strip_pruning before applying the clustering API.
End of explanation
"""


print("Clustered Model sparsity:\n")
print_model_weights_sparsity(clustered_model)
print("\nSparsity preserved clustered Model sparsity:\n")
print_model_weights_sparsity(sparsity_clustered_model)

"""
Explanation: Check sparsity for both models.
End of explanation
"""


def get_gzipped_model_size(file):
  # It returns the size of the gzipped model in kilobytes.

  _, zipped_file = tempfile.mkstemp('.zip')
  with zipfile.ZipFile(zipped_file, 'w', compression=zipfile.ZIP_DEFLATED) as f:
    f.write(file)

  return os.path.getsize(zipped_file)/1000

# Clustered model
clustered_model_file = 'clustered_model.h5'

# Save the model.
clustered_model.save(clustered_model_file)
    
#Sparsity Preserve Clustered model
sparsity_clustered_model_file = 'sparsity_clustered_model.h5'

# Save the model.
sparsity_clustered_model.save(sparsity_clustered_model_file)
    
print("Clustered Model size: ", get_gzipped_model_size(clustered_model_file), ' KB')
print("Sparsity preserved clustered Model size: ", get_gzipped_model_size(sparsity_clustered_model_file), ' KB')

"""
Explanation: Create 1.6x smaller models from clustering
Define helper function to get zipped model file.
End of explanation
"""


stripped_sparsity_clustered_model = tfmot.clustering.keras.strip_clustering(sparsity_clustered_model)

converter = tf.lite.TFLiteConverter.from_keras_model(stripped_sparsity_clustered_model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
sparsity_clustered_quant_model = converter.convert()

_, pruned_and_clustered_tflite_file = tempfile.mkstemp('.tflite')

with open(pruned_and_clustered_tflite_file, 'wb') as f:
  f.write(sparsity_clustered_quant_model)

print("Sparsity preserved clustered Model size: ", get_gzipped_model_size(sparsity_clustered_model_file), ' KB')
print("Sparsity preserved clustered and quantized TFLite model size:",
       get_gzipped_model_size(pruned_and_clustered_tflite_file), ' KB')

"""
Explanation: Create a TFLite model from combining sparsity preserving weight clustering and post-training quantization
Strip clustering wrappers and convert to TFLite.
End of explanation
"""


def eval_model(interpreter):
  input_index = interpreter.get_input_details()[0]["index"]
  output_index = interpreter.get_output_details()[0]["index"]

  # Run predictions on every image in the "test" dataset.
  prediction_digits = []
  for i, test_image in enumerate(test_images):
    if i % 1000 == 0:
      print(f"Evaluated on {i} results so far.")
    # Pre-processing: add batch dimension and convert to float32 to match with
    # the model's input data format.
    test_image = np.expand_dims(test_image, axis=0).astype(np.float32)
    interpreter.set_tensor(input_index, test_image)

    # Run inference.
    interpreter.invoke()

    # Post-processing: remove batch dimension and find the digit with highest
    # probability.
    output = interpreter.tensor(output_index)
    digit = np.argmax(output()[0])
    prediction_digits.append(digit)

  print('\n')
  # Compare prediction results with ground truth labels to calculate accuracy.
  prediction_digits = np.array(prediction_digits)
  accuracy = (prediction_digits == test_labels).mean()
  return accuracy

"""
Explanation: See the persistence of accuracy from TF to TFLite
End of explanation
"""


# Keras model evaluation
stripped_sparsity_clustered_model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])
_, sparsity_clustered_keras_accuracy = stripped_sparsity_clustered_model.evaluate(
    test_images, test_labels, verbose=0)

# TFLite model evaluation
interpreter = tf.lite.Interpreter(pruned_and_clustered_tflite_file)
interpreter.allocate_tensors()

sparsity_clustered_tflite_accuracy = eval_model(interpreter)

print('Pruned, clustered and quantized Keras model accuracy:', sparsity_clustered_keras_accuracy)
print('Pruned, clustered and quantized TFLite model accuracy:', sparsity_clustered_tflite_accuracy)

"""
Explanation: You evaluate the model, which has been pruned, clustered and quantized, and then see that the accuracy from TensorFlow persists in the TFLite backend.
End of explanation
"""


