#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

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


! pip install -q tensorflow
! pip install -q tensorflow-model-optimization

import tempfile

import tensorflow as tf
import numpy as np

from tensorflow import keras
import tensorflow_datasets as tfds
import tensorflow_model_optimization as tfmot

%load_ext tensorboard

"""
Explanation: Pruning for on-device inference w/ XNNPACK
<table class="tfo-notebook-buttons" align="left">
  <td>
    <a target="_blank" href="https://www.tensorflow.org/model_optimization/guide/pruning/pruning_for_on_device_inference"><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/pruning/pruning_for_on_device_inference.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/pruning/pruning_for_on_device_inference.ipynb"><img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" />View source on GitHub</a>
  </td>
  <td>
    <a href="https://storage.googleapis.com/tensorflow_docs/model-optimization/tensorflow_model_optimization/g3doc/guide/pruning/pruning_for_on_device_inference.ipynb"><img src="https://www.tensorflow.org/images/download_logo_32px.png" />Download notebook</a>
  </td>
</table>

Welcome to the guide on Keras weights pruning for improving latency of on-device inference via XNNPACK.
This guide presents the usage of the newly introduced tfmot.sparsity.keras.PruningPolicy API and demonstrates how it could be used for accelerating mostly convolutional models on modern CPUs using XNNPACK Sparse inference.
The guide covers the following steps of the model creation process:
* Build and train the dense baseline
* Fine-tune model with pruning
* Convert to TFLite
* On-device benchmark
The guide doesn't cover the best practices for the fine-tuning with pruning. For more detailed information on this topic, please check out our comprehensive guide.
Setup
End of explanation
"""


# Load CIFAR10 dataset.
(ds_train, ds_val, ds_test), ds_info = tfds.load(
    'cifar10',
    split=['train[:90%]', 'train[90%:]', 'test'],
    as_supervised=True,
    with_info=True,
)

# Normalize the input image so that each pixel value is between 0 and 1.
def normalize_img(image, label):
  """Normalizes images: `uint8` -> `float32`."""
  return tf.image.convert_image_dtype(image, tf.float32), label

# Load the data in batches of 128 images.
batch_size = 128
def prepare_dataset(ds, buffer_size=None):
  ds = ds.map(normalize_img, num_parallel_calls=tf.data.experimental.AUTOTUNE)
  ds = ds.cache()
  if buffer_size:
    ds = ds.shuffle(buffer_size)
  ds = ds.batch(batch_size)
  ds = ds.prefetch(tf.data.experimental.AUTOTUNE)
  return ds

ds_train = prepare_dataset(ds_train,
                           buffer_size=ds_info.splits['train'].num_examples)
ds_val = prepare_dataset(ds_val)
ds_test = prepare_dataset(ds_test)

# Build the dense baseline model.
dense_model = keras.Sequential([
    keras.layers.InputLayer(input_shape=(32, 32, 3)),
    keras.layers.ZeroPadding2D(padding=1),
    keras.layers.Conv2D(
        filters=8,
        kernel_size=(3, 3),
        strides=(2, 2),
        padding='valid'),
    keras.layers.BatchNormalization(),
    keras.layers.ReLU(),
    keras.layers.DepthwiseConv2D(kernel_size=(3, 3), padding='same'),
    keras.layers.BatchNormalization(),
    keras.layers.ReLU(),
    keras.layers.Conv2D(filters=16, kernel_size=(1, 1)),
    keras.layers.BatchNormalization(),
    keras.layers.ReLU(),
    keras.layers.ZeroPadding2D(padding=1),
    keras.layers.DepthwiseConv2D(
        kernel_size=(3, 3), strides=(2, 2), padding='valid'),
    keras.layers.BatchNormalization(),
    keras.layers.ReLU(),
    keras.layers.Conv2D(filters=32, kernel_size=(1, 1)),
    keras.layers.BatchNormalization(),
    keras.layers.ReLU(),
    keras.layers.GlobalAveragePooling2D(),
    keras.layers.Flatten(),
    keras.layers.Dense(10)
])

# Compile and train the dense model for 10 epochs.
dense_model.compile(
    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer='adam',
    metrics=['accuracy'])

dense_model.fit(
  ds_train,
  epochs=10,
  validation_data=ds_val)

# Evaluate the dense model.
_, dense_model_accuracy = dense_model.evaluate(ds_test, verbose=0)

"""
Explanation: Build and train the dense model
We build and train a simple baseline CNN for classification task on CIFAR10 dataset.
End of explanation
"""


prune_low_magnitude = tfmot.sparsity.keras.prune_low_magnitude

# Compute end step to finish pruning after after 5 epochs.
end_epoch = 5

num_iterations_per_epoch = len(ds_train)
end_step =  num_iterations_per_epoch * end_epoch

# Define parameters for pruning.
pruning_params = {
      'pruning_schedule': tfmot.sparsity.keras.PolynomialDecay(initial_sparsity=0.25,
                                                               final_sparsity=0.75,
                                                               begin_step=0,
                                                               end_step=end_step),
      'pruning_policy': tfmot.sparsity.keras.PruneForLatencyOnXNNPack()
}

# Try to apply pruning wrapper with pruning policy parameter.
try:
  model_for_pruning = prune_low_magnitude(dense_model, **pruning_params)
except ValueError as e:
  print(e)

"""
Explanation: Build the sparse model
Using the instructions from the comprehensive guide, we apply tfmot.sparsity.keras.prune_low_magnitude function with parameters that target on-device acceleration via pruning i.e. tfmot.sparsity.keras.PruneForLatencyOnXNNPack policy.
End of explanation
"""


fixed_dense_model = keras.Sequential([
    keras.layers.InputLayer(input_shape=(32, 32, 3)),
    keras.layers.ZeroPadding2D(padding=1),
    keras.layers.Conv2D(
        filters=8,
        kernel_size=(3, 3),
        strides=(2, 2),
        padding='valid'),
    keras.layers.BatchNormalization(),
    keras.layers.ReLU(),
    keras.layers.DepthwiseConv2D(kernel_size=(3, 3), padding='same'),
    keras.layers.BatchNormalization(),
    keras.layers.ReLU(),
    keras.layers.Conv2D(filters=16, kernel_size=(1, 1)),
    keras.layers.BatchNormalization(),
    keras.layers.ReLU(),
    keras.layers.ZeroPadding2D(padding=1),
    keras.layers.DepthwiseConv2D(
        kernel_size=(3, 3), strides=(2, 2), padding='valid'),
    keras.layers.BatchNormalization(),
    keras.layers.ReLU(),
    keras.layers.Conv2D(filters=32, kernel_size=(1, 1)),
    keras.layers.BatchNormalization(),
    keras.layers.ReLU(),
    keras.layers.GlobalAveragePooling2D(keepdims=True),
    keras.layers.Flatten(),
    keras.layers.Dense(10)
])

# Use the pretrained model for pruning instead of training from scratch.
fixed_dense_model.set_weights(dense_model.get_weights())

# Try to reapply pruning wrapper.
model_for_pruning = prune_low_magnitude(fixed_dense_model, **pruning_params)

"""
Explanation: The call prune_low_magnitude results in ValueError with the message Could not find a GlobalAveragePooling2D layer with keepdims = True in all output branches. The message indicates that the model isn't supported for pruning with policy tfmot.sparsity.keras.PruneForLatencyOnXNNPack and specifically the layer GlobalAveragePooling2D requires the parameter keepdims = True. Let's fix that and reapply prune_low_magnitude function.
End of explanation
"""


logdir = tempfile.mkdtemp()

callbacks = [
  tfmot.sparsity.keras.UpdatePruningStep(),
  tfmot.sparsity.keras.PruningSummaries(log_dir=logdir),
]

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

model_for_pruning.fit(
  ds_train,
  epochs=15,
  validation_data=ds_val,
  callbacks=callbacks)

# Evaluate the dense model.
_, pruned_model_accuracy = model_for_pruning.evaluate(ds_test, verbose=0)

print('Dense model test accuracy:', dense_model_accuracy)
print('Pruned model test accuracy:', pruned_model_accuracy)

"""
Explanation: Invocation of prune_low_magnitude has finished without any errors meaning that the model is fully supported for the tfmot.sparsity.keras.PruneForLatencyOnXNNPack policy and can be accelerated using XNNPACK Sparse inference.
Fine-tune the sparse model
Following the pruning example, we fine-tune the sparse model using the weights of the dense model. We start fine-tuning of the model with 25% sparsity (25% of the weights are set to zero) and end with 75% sparsity.
End of explanation
"""


#docs_infra: no_execute
%tensorboard --logdir={logdir}

"""
Explanation: The logs show the progression of sparsity on a per-layer basis.
End of explanation
"""


converter = tf.lite.TFLiteConverter.from_keras_model(dense_model)
dense_tflite_model = converter.convert()

_, dense_tflite_file = tempfile.mkstemp('.tflite')
with open(dense_tflite_file, 'wb') as f:
  f.write(dense_tflite_model)

model_for_export = tfmot.sparsity.keras.strip_pruning(model_for_pruning)

converter = tf.lite.TFLiteConverter.from_keras_model(model_for_export)
converter.optimizations = [tf.lite.Optimize.EXPERIMENTAL_SPARSITY]
pruned_tflite_model = converter.convert()

_, pruned_tflite_file = tempfile.mkstemp('.tflite')
with open(pruned_tflite_file, 'wb') as f:
  f.write(pruned_tflite_model)

"""
Explanation: After the fine-tuning with pruning, test accuracy demonstrates a modest improvement (43% to 44%) compared to the dense model. Let's compare on-device latency using TFLite benchmark.
Model conversion and benchmarking
To convert the pruned model into TFLite, we need replace the PruneLowMagnitude wrappers with original layers via the strip_pruning function. Also, since the weights of the pruned model (model_for_pruning) are mostly zeros, we may apply an optimization tf.lite.Optimize.EXPERIMENTAL_SPARSITY to efficiently store the resulted TFLite model. This optimization flag is not required for the dense model.
End of explanation
"""


! adb shell /data/local/tmp/benchmark_model \
    --graph=/data/local/tmp/dense_model.tflite \
    --use_xnnpack=true \
    --num_runs=100 \
    --num_threads=1

! adb shell /data/local/tmp/benchmark_model \
    --graph=/data/local/tmp/pruned_model.tflite \
    --use_xnnpack=true \
    --num_runs=100 \
    --num_threads=1

"""
Explanation: Following the instructions of TFLite Model Benchmarking Tool, we build the tool, upload it to the Android device together with dense and pruned TFLite models, and benchmark both models on the device.
End of explanation
"""


