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example_mnist_prune.py
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# Copyright 2019 Google LLC
#
#
# 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.
# ==============================================================================
"""Example of mnist model with pruning.
Adapted from TF model optimization example."""
import tempfile
import numpy as np
import tensorflow.keras.backend as K
from tensorflow.keras.datasets import mnist
from tensorflow.keras.layers import Activation
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Input
from tensorflow.keras.models import Model
from tensorflow.keras.models import Sequential
from tensorflow.keras.models import save_model
from tensorflow.keras.utils import to_categorical
from qkeras import QActivation
from qkeras import QDense
from qkeras import QConv2D
from qkeras import quantized_bits
from qkeras.utils import load_qmodel
from qkeras.utils import print_model_sparsity
from tensorflow_model_optimization.python.core.sparsity.keras import prune
from tensorflow_model_optimization.python.core.sparsity.keras import pruning_callbacks
from tensorflow_model_optimization.python.core.sparsity.keras import pruning_schedule
batch_size = 128
num_classes = 10
epochs = 12
prune_whole_model = True # Prune whole model or just specified layers
def build_model(input_shape):
x = x_in = Input(shape=input_shape, name="input")
x = QConv2D(
32, (2, 2), strides=(2,2),
kernel_quantizer=quantized_bits(4,0,1),
bias_quantizer=quantized_bits(4,0,1),
name="conv2d_0_m")(x)
x = QActivation("quantized_relu(4,0)", name="act0_m")(x)
x = QConv2D(
64, (3, 3), strides=(2,2),
kernel_quantizer=quantized_bits(4,0,1),
bias_quantizer=quantized_bits(4,0,1),
name="conv2d_1_m")(x)
x = QActivation("quantized_relu(4,0)", name="act1_m")(x)
x = QConv2D(
64, (2, 2), strides=(2,2),
kernel_quantizer=quantized_bits(4,0,1),
bias_quantizer=quantized_bits(4,0,1),
name="conv2d_2_m")(x)
x = QActivation("quantized_relu(4,0)", name="act2_m")(x)
x = Flatten()(x)
x = QDense(num_classes, kernel_quantizer=quantized_bits(4,0,1),
bias_quantizer=quantized_bits(4,0,1),
name="dense")(x)
x = Activation("softmax", name="softmax")(x)
model = Model(inputs=[x_in], outputs=[x])
return model
def build_layerwise_model(input_shape, **pruning_params):
return Sequential([
prune.prune_low_magnitude(
QConv2D(
32, (2, 2), strides=(2,2),
kernel_quantizer=quantized_bits(4,0,1),
bias_quantizer=quantized_bits(4,0,1),
name="conv2d_0_m"),
input_shape=input_shape,
**pruning_params),
QActivation("quantized_relu(4,0)", name="act0_m"),
prune.prune_low_magnitude(
QConv2D(
64, (3, 3), strides=(2,2),
kernel_quantizer=quantized_bits(4,0,1),
bias_quantizer=quantized_bits(4,0,1),
name="conv2d_1_m"),
**pruning_params),
QActivation("quantized_relu(4,0)", name="act1_m"),
prune.prune_low_magnitude(
QConv2D(
64, (2, 2), strides=(2,2),
kernel_quantizer=quantized_bits(4,0,1),
bias_quantizer=quantized_bits(4,0,1),
name="conv2d_2_m"),
**pruning_params),
QActivation("quantized_relu(4,0)", name="act2_m"),
Flatten(),
prune.prune_low_magnitude(
QDense(
num_classes, kernel_quantizer=quantized_bits(4,0,1),
bias_quantizer=quantized_bits(4,0,1),
name="dense"),
**pruning_params),
Activation("softmax", name="softmax")
])
def train_and_save(model, x_train, y_train, x_test, y_test):
model.compile(
loss="categorical_crossentropy",
optimizer="adam",
metrics=["accuracy"])
# Print the model summary.
model.summary()
# Add a pruning step callback to peg the pruning step to the optimizer's
# step. Also add a callback to add pruning summaries to tensorboard
callbacks = [
pruning_callbacks.UpdatePruningStep(),
#pruning_callbacks.PruningSummaries(log_dir=tempfile.mkdtemp())
pruning_callbacks.PruningSummaries(log_dir="/tmp/mnist_prune")
]
model.fit(
x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
callbacks=callbacks,
validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print("Test loss:", score[0])
print("Test accuracy:", score[1])
print_model_sparsity(model)
# Export and import the model. Check that accuracy persists.
_, keras_file = tempfile.mkstemp(".h5")
print("Saving model to: ", keras_file)
save_model(model, keras_file)
print("Reloading model")
with prune.prune_scope():
loaded_model = load_qmodel(keras_file)
score = loaded_model.evaluate(x_test, y_test, verbose=0)
print("Test loss:", score[0])
print("Test accuracy:", score[1])
def main():
# input image dimensions
img_rows, img_cols = 28, 28
# the data, shuffled and split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()
if K.image_data_format() == "channels_first":
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
x_train = x_train.astype("float32")
x_test = x_test.astype("float32")
x_train /= 255
x_test /= 255
print("x_train shape:", x_train.shape)
print(x_train.shape[0], "train samples")
print(x_test.shape[0], "test samples")
# convert class vectors to binary class matrices
y_train = to_categorical(y_train, num_classes)
y_test = to_categorical(y_test, num_classes)
pruning_params = {
"pruning_schedule":
pruning_schedule.ConstantSparsity(0.75, begin_step=2000, frequency=100)
}
if prune_whole_model:
model = build_model(input_shape)
model = prune.prune_low_magnitude(model, **pruning_params)
else:
model = build_layerwise_model(input_shape, **pruning_params)
train_and_save(model, x_train, y_train, x_test, y_test)
if __name__ == "__main__":
main()