def main(_):
FLAGS.model_classifier_layer = False # We only need the features
# Train the model and save the feature extractor
saved_model_main.train_and_save()
tf_accelerator, _ = saved_model_main.tf_accelerator_and_tolerances()
feature_model_dir = saved_model_main.savedmodel_dir()
# With Keras, we use the tf.distribute.OneDeviceStrategy as the high-level
# analogue of the tf.device(...) placement seen above.
# It works on CPU, GPU and TPU.
# Actual high-performance training would use the appropriately replicated
# TF Distribution Strategy.
strategy = tf.distribute.OneDeviceStrategy(tf_accelerator)
with strategy.scope():
images = tf.keras.layers.Input(mnist_lib.input_shape,
batch_size=mnist_lib.train_batch_size)
# We do not yet train the SavedModel, due to b/123499169.
keras_feature_extractor = hub.KerasLayer(feature_model_dir,
trainable=False)
features = keras_feature_extractor(images)
predictor = tf.keras.layers.Dense(10, activation="softmax")
predictions = predictor(features)
keras_model = tf.keras.Model(images, predictions)
keras_model.compile(loss=tf.keras.losses.categorical_crossentropy,
optimizer=tf.keras.optimizers.SGD(learning_rate=0.01),
metrics=["accuracy"])
logging.info(keras_model.summary())
train_ds = mnist_lib.load_mnist(tfds.Split.TRAIN,
batch_size=mnist_lib.train_batch_size)
test_ds = mnist_lib.load_mnist(tfds.Split.TEST,
batch_size=mnist_lib.test_batch_size)
keras_model.fit(train_ds, epochs=FLAGS.num_epochs, validation_data=test_ds)
if FLAGS.show_images:
mnist_lib.plot_images(
test_ds,
1,
5,
f"Keras inference with reuse of {saved_model_main.model_description()}",
inference_fn=lambda images: keras_model(
tf.convert_to_tensor(images)))
def main(_):
if FLAGS.count_images % FLAGS.serving_batch_size != 0:
raise ValueError(f"The count_images ({FLAGS.count_images}) must be a "
"multiple of "
f"serving_batch_size ({FLAGS.serving_batch_size})")
test_ds = mnist_lib.load_mnist(tfds.Split.TEST,
batch_size=FLAGS.serving_batch_size)
images_and_labels = tfds.as_numpy(
test_ds.take(FLAGS.count_images // FLAGS.serving_batch_size))
accurate_count = 0
for batch_idx, (images, labels) in enumerate(images_and_labels):
predictions_one_hot = serving_call_mnist(images)
predictions_digit = np.argmax(predictions_one_hot, axis=1)
labels_digit = np.argmax(labels, axis=1)
accurate_count += np.sum(labels_digit == predictions_digit)
running_accuracy = (100. * accurate_count / (1 + batch_idx) /
FLAGS.serving_batch_size)
logging.info(
f" predicted digits = {predictions_digit} labels {labels_digit}. "
f"Running accuracy {running_accuracy:.3f}%")
def main(_):
logging.info('Loading the MNIST TensorFlow dataset')
train_ds = mnist_lib.load_mnist(
tfds.Split.TRAIN, batch_size=mnist_lib.train_batch_size)
test_ds = mnist_lib.load_mnist(
tfds.Split.TEST, batch_size=FLAGS.serving_batch_size)
(flax_predict,
flax_params) = mnist_lib.FlaxMNIST.train(train_ds, test_ds, FLAGS.num_epochs)
def predict(image):
return flax_predict(flax_params, image)
# Convert Flax model to TF function.
tf_predict = tf.function(
jax2tf.convert(predict, enable_xla=False),
input_signature=[
tf.TensorSpec(
shape=[FLAGS.serving_batch_size, 28, 28, 1],
dtype=tf.float32,
name='input')
],
autograph=False)
# Convert TF function to TF Lite format.
converter = tf.lite.TFLiteConverter.from_concrete_functions(
[tf_predict.get_concrete_function()])
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS, # enable TensorFlow Lite ops.
tf.lite.OpsSet.SELECT_TF_OPS # enable TensorFlow ops.
]
tflite_float_model = converter.convert()
# Show model size in KBs.
float_model_size = len(tflite_float_model) / 1024
print('Float model size = %dKBs.' % float_model_size)
# Re-convert the model to TF Lite using quantization.
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_quantized_model = converter.convert()
# Show model size in KBs.
quantized_model_size = len(tflite_quantized_model) / 1024
print('Quantized model size = %dKBs,' % quantized_model_size)
print('which is about %d%% of the float model size.' %
(quantized_model_size * 100 / float_model_size))
# Evaluate the TF Lite float model. You'll find that its accurary is identical
# to the original Flax model because they are essentially the same model
# stored in different format.
float_accuracy = evaluate_tflite_model(tflite_float_model, test_ds)
print('Float model accuracy = %.4f' % float_accuracy)
# Evalualte the TF Lite quantized model.
# Don't be surprised if you see quantized model accuracy is higher than
# the original float model. It happens sometimes :)
quantized_accuracy = evaluate_tflite_model(tflite_quantized_model, test_ds)
print('Quantized model accuracy = %.4f' % quantized_accuracy)
print('Accuracy drop = %.4f' % (float_accuracy - quantized_accuracy))
f = open(FLAGS.tflite_file_path, 'wb')
f.write(tflite_quantized_model)
f.close()
def train_and_save():
logging.info("Loading the MNIST TensorFlow dataset")
train_ds = mnist_lib.load_mnist(
tfds.Split.TRAIN, batch_size=mnist_lib.train_batch_size)
test_ds = mnist_lib.load_mnist(
tfds.Split.TEST, batch_size=mnist_lib.test_batch_size)
if FLAGS.show_images:
mnist_lib.plot_images(train_ds, 1, 5, "Training images", inference_fn=None)
the_model_class = pick_model_class()
model_dir = savedmodel_dir(with_version=True)
if FLAGS.generate_model:
model_descr = model_description()
logging.info(f"Generating model for {model_descr}")
(predict_fn, predict_params) = the_model_class.train(
train_ds,
test_ds,
FLAGS.num_epochs,
with_classifier=FLAGS.model_classifier_layer)
input_signatures = [
# The first one will be the serving signature
tf.TensorSpec((FLAGS.serving_batch_size,) + mnist_lib.input_shape,
tf.float32),
tf.TensorSpec((mnist_lib.train_batch_size,) + mnist_lib.input_shape,
tf.float32),
tf.TensorSpec((mnist_lib.test_batch_size,) + mnist_lib.input_shape,
tf.float32),
]
logging.info(f"Saving model for {model_descr}")
saved_model_lib.convert_and_save_model(
predict_fn,
predict_params,
model_dir,
input_signatures=input_signatures,
compile_model=FLAGS.compile_model)
if FLAGS.test_savedmodel:
tf_accelerator, tolerances = tf_accelerator_and_tolerances()
with tf.device(tf_accelerator):
logging.info("Testing savedmodel")
pure_restored_model = tf.saved_model.load(model_dir)
if FLAGS.show_images and FLAGS.model_classifier_layer:
mnist_lib.plot_images(
test_ds,
1,
5,
f"Inference results for {model_descr}",
inference_fn=pure_restored_model)
test_input = np.ones(
(mnist_lib.test_batch_size,) + mnist_lib.input_shape,
dtype=np.float32)
np.testing.assert_allclose(
pure_restored_model(tf.convert_to_tensor(test_input)),
predict_fn(predict_params, test_input), **tolerances)
if FLAGS.show_model:
def print_model(model_dir: str):
cmd = f"saved_model_cli show --all --dir {model_dir}"
print(cmd)
os.system(cmd)
print_model(model_dir)