num_classes = 12 # should probably draw this directly from the dataset.
# FLAGS = None
if __name__ == '__main__':
fname = 'quant_cal_idxs.txt'
num_files_per_label = 10
Flags, unparsed = kws_util.parse_command()
np.random.seed(2)
tf.random.set_seed(2)
print('We will download data to {:}'.format(Flags.data_dir))
ds_train, ds_test, ds_val = kws_data.get_training_data(Flags)
print("Done getting data")
labels = np.array([])
for _, batch_labels in ds_val:
labels = np.hstack((labels, batch_labels))
cal_idxs = np.array([], dtype=int)
for l in np.unique(labels):
all_label_idxs = np.nonzero(labels == l)[
0] # nonzero => tuple of arrays; get the first/only one
sel_label_idxs = np.random.choice(all_label_idxs,
size=num_files_per_label,
replace=False)
cal_idxs = np.concatenate((cal_idxs, sel_label_idxs))
Flags, unparsed = kws_util.parse_command()
print(
f"Converting trained model {Flags.saved_model_path} to TFL model at {Flags.tfl_file_name}"
)
model = tf.keras.models.load_model(Flags.saved_model_path)
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
with open("quant_cal_idxs.txt") as fpi:
cal_indices = [int(line) for line in fpi]
cal_indices.sort()
num_calibration_steps = len(cal_indices)
_, _, ds_val = kws_data.get_training_data(Flags, val_cal_subset=True)
ds_val = ds_val.unbatch().batch(1)
if True: # enable if you want to check the distribution of labels in the calibration set
label_counts = {}
for label in range(12):
label_counts[label] = 0
for _, label in ds_val.as_numpy_iterator():
label_counts[label[0]] += 1
for label in range(12):
print(f"Cal set has {label_counts[label]} of label {label}")
ds_iter = ds_val.as_numpy_iterator()
def representative_dataset_gen():
for _ in range(num_calibration_steps):
import numpy as np
import os
import argparse
import keras_model as models
import get_dataset as aww_data
import aww_util
num_classes = 12 # should probably draw this directly from the dataset.
# FLAGS = None
if __name__ == '__main__':
Flags, unparsed = aww_util.parse_command()
print('We will download data to {:}'.format(Flags.data_dir))
print('We will train for {:} epochs'.format(Flags.epochs))
ds_train, ds_test, ds_val = aww_data.get_training_data(Flags)
print("Done getting data")
model = models.get_model(model_name=Flags.model_architecture)
model.summary()
train_hist = model.fit(ds_train,
validation_data=ds_val,
epochs=Flags.epochs)
model.save(Flags.saved_model_path)
if Flags.run_test_set:
test_scores = model.evaluate(ds_test)
print("Test loss:", test_scores[0])
print("Test accuracy:", test_scores[1])
import os
import numpy as np
import argparse
import get_dataset as aww_data
import aww_util
if __name__ == '__main__':
Flags, unparsed = aww_util.parse_command()
num_calibration_steps = 10
converter = tf.lite.TFLiteConverter.from_saved_model(
Flags.saved_model_path)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
_, _, ds_val = aww_data.get_training_data(Flags)
# ds_val = ds_val.batch(1) # can we use a larger batch?
def representative_dataset_gen():
for _ in range(num_calibration_steps):
next_input = np.expand_dims(next(ds_val.as_numpy_iterator())[0], 3)
yield [next_input]
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = representative_dataset_gen
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = tf.int8 # or tf.uint8
converter.inference_output_type = tf.int8 # or tf.uint8
tflite_quant_model = converter.convert()
