Opensource ML embedded evaluation kit
Change-Id: I12e807f19f5cacad7cef82572b6dd48252fd61fd
diff --git a/model_conditioning_examples/weight_pruning.py b/model_conditioning_examples/weight_pruning.py
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+# Copyright (c) 2021 Arm Limited. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# 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
+#
+# http://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.
+"""
+This script will provide you with a short example of how to perform magnitude-based weight pruning in TensorFlow
+using the TensorFlow Model Optimization Toolkit.
+
+The output from this example will be a TensorFlow Lite model file where ~75% percent of the weights have been 'pruned' to the
+value 0 during training - quantization has then been applied on top of this.
+
+By pruning the model we can improve compression of the model file. This can be essential for deploying certain models
+on systems with limited resources - such as embedded systems using Arm Ethos NPU. Also, if the pruned model is run
+on an Arm Ethos NPU then this pruning can improve the execution time of the model.
+
+After pruning is complete we do post-training quantization to quantize the model and then generate a TensorFlow Lite file.
+
+If you are targetting an Arm Ethos-U55 NPU then the output TensorFlow Lite file will also need to be passed through the Vela
+compiler for further optimizations before it can be used.
+
+For more information on using Vela see: https://git.mlplatform.org/ml/ethos-u/ethos-u-vela.git/about/
+For more information on weight pruning see: https://www.tensorflow.org/model_optimization/guide/pruning
+"""
+import pathlib
+
+import tensorflow as tf
+import tensorflow_model_optimization as tfmot
+
+from training_utils import get_data, create_model
+from post_training_quantization import post_training_quantize, evaluate_tflite_model
+
+
+def prepare_for_pruning(keras_model):
+ """Prepares a Keras model for pruning."""
+
+ # We use a constant sparsity schedule so the amount of sparsity in the model is kept at the same percent throughout
+ # training. An alternative is PolynomialDecay where sparsity can be gradually increased during training.
+ pruning_schedule = tfmot.sparsity.keras.ConstantSparsity(target_sparsity=0.75, begin_step=0)
+
+ # Apply the pruning wrapper to the whole model so weights in every layer will get pruned. You may find that to avoid
+ # too much accuracy loss only certain non-critical layers in your model should be pruned.
+ pruning_ready_model = tfmot.sparsity.keras.prune_low_magnitude(keras_model, pruning_schedule=pruning_schedule)
+
+ # We must recompile the model after making it ready for pruning.
+ pruning_ready_model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
+ loss=tf.keras.losses.sparse_categorical_crossentropy,
+ metrics=['accuracy'])
+
+ return pruning_ready_model
+
+
+def main():
+ x_train, y_train, x_test, y_test = get_data()
+ model = create_model()
+
+ # Compile and train the model first.
+ # In general it is easier to do pruning as a fine-tuning step after the model is fully trained.
+ model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
+ loss=tf.keras.losses.sparse_categorical_crossentropy,
+ metrics=['accuracy'])
+
+ model.fit(x=x_train, y=y_train, batch_size=128, epochs=5, verbose=1, shuffle=True)
+
+ # Test the trained model accuracy.
+ test_loss, test_acc = model.evaluate(x_test, y_test)
+ print(f"Test accuracy before pruning: {test_acc:.3f}")
+
+ # Prepare the model for pruning and add the pruning update callback needed in training.
+ pruned_model = prepare_for_pruning(model)
+ callbacks = [tfmot.sparsity.keras.UpdatePruningStep()]
+
+ # Continue training the model but now with pruning applied - remember to pass in the callbacks!
+ pruned_model.fit(x=x_train, y=y_train, batch_size=128, epochs=1, verbose=1, shuffle=True, callbacks=callbacks)
+ test_loss, test_acc = pruned_model.evaluate(x_test, y_test)
+ print(f"Test accuracy after pruning: {test_acc:.3f}")
+
+ # Remove all variables that pruning only needed in the training phase.
+ model_for_export = tfmot.sparsity.keras.strip_pruning(pruned_model)
+
+ # Apply post-training quantization on top of the pruning and save the resulting TensorFlow Lite model to file.
+ tflite_model = post_training_quantize(model_for_export, x_train)
+
+ tflite_models_dir = pathlib.Path('./conditioned_models/')
+ tflite_models_dir.mkdir(exist_ok=True, parents=True)
+
+ pruned_quant_model_save_path = tflite_models_dir / 'pruned_post_training_quant_model.tflite'
+ with open(pruned_quant_model_save_path, 'wb') as f:
+ f.write(tflite_model)
+
+ # Test the pruned quantized model accuracy. Save time by only testing a subset of the whole data.
+ num_test_samples = 1000
+ evaluate_tflite_model(pruned_quant_model_save_path, x_test[0:num_test_samples], y_test[0:num_test_samples])
+
+
+if __name__ == "__main__":
+ main()