def representative_dataset_generator():
train_data, train_filenames, train_labels, test_data, test_filenames, test_labels, label_names = \
train.load_cifar_10_data(cifar_10_dir)
_idx = np.load('calibration_samples_idxs.npy')
for i in _idx:
sample_img = np.expand_dims(np.array(test_data[i], dtype=np.float32),
axis=0)
yield [sample_img]
def representative_dataset_generator():
train_data, train_filenames, train_labels, test_data, test_filenames, test_labels, label_names = \
train.load_cifar_10_data(cifar_10_dir)
_idx = np.array(range(10000))
np.random.shuffle(_idx)
for i in _idx[:500]:
print(i)
sample_img = np.expand_dims(np.array(test_data[i], dtype=np.float32),
axis=0)
yield [sample_img]
_name = keras_model.get_quant_model_name()
model_path = 'trained_models/' + _name + '.tflite'
if __name__ == '__main__':
# Load the TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_path=model_path)
interpreter.allocate_tensors()
# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
cifar_10_dir = 'cifar-10-batches-py'
train_data, train_filenames, train_labels, test_data, test_filenames, test_labels, label_names = \
train.load_cifar_10_data(cifar_10_dir)
label_classes = np.argmax(test_labels, axis=1)
print("Label classes: ", label_classes.shape)
x = []
y = []
for i in range(len(test_data)):
x.append(test_data[i])
y.append(test_labels[i])
if QUANT_MODEL:
test_imgs = np.array(x, dtype=np.int64)
test_imgs = test_imgs - 128
test_imgs = test_imgs.astype(np.int8)