Hi all,
In my model for time series analyses, I get different results in the converted TF-lite model for a certain set parameters. I managed to identifiy three conv2d layers causing this problem. If the dilation parameters are set to (2, 1), the results become completely different to the original model, for (1, 1), results are equivalent (with small numerical differences.
The code below reproduces the problem, if the variable dilation == 2. Equivalent results for TFlite and TF can be produced for dilation == 1.
Reproduced with:
TF 2.4.1, also reproduced with TF 2.8.0.
Python 3.8
import tensorflow as tf
import matplotlib.pyplot as plt
class MyLayer(tf.keras.layers.Layer):
def init(self, filters, dilation):
super(MyLayer, self).init()
self.c0 = tf.keras.layers.Conv2D(filters=filters,
kernel_size=(3, 1),
padding='same',
activation=None)
self.c1 = tf.keras.layers.Conv2D(filters=filters,
kernel_size=(3, 1),
dilation_rate=(dilation, 1),
padding='same',
activation=None)
self.c2 = tf.keras.layers.Conv2D(filters=filters,
kernel_size=(3, 1),
padding='same',
activation=None)
def call(self, x):
# input: (B, time, slices, channels)
x = self.c0(x)
x = self.c1(x)
x = self.c2(x)
return x
def main():
# dilation = 1 # yields equivalent results
dilation = 2 # yields different results between tflite and tensorflow
filters = 32
# Create a model with a Layer containing three Conv2D Layers
input_shape = (128, 1, 1)
x = tf.keras.Input(shape=input_shape, name='input', dtype=tf.float32)
y = MyLayer(filters=filters, dilation=dilation)(x)
model = tf.keras.Model(inputs=[x], outputs=[y])
# Save tensorflow model
test_tensorflow_model_path = 'test_tensorflow_model/'
test_tflite_model_file = test_tensorflow_model_path + 'test_tflitemodel.tflite'
model.save(test_tensorflow_model_path)
# Load model from disk
model = tf.keras.models.load_model(test_tensorflow_model_path)
# create random test data and call model
input_data = tf.random.normal((1, ) + input_shape, dtype=tf.float32)
output_tf = model(input_data)
# Create a TFlite model
converter = tf.lite.TFLiteConverter.from_saved_model(test_tensorflow_model_path)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_model = converter.convert()
with open(test_tflite_model_file, 'wb') as f:
f.write(tflite_model)
print("TF lite export done")
# Load the TFLite model in TFLite Interpreter
interpreter = tf.lite.Interpreter(test_tflite_model_file)
interpreter.allocate_tensors()
# Call TFlite model with same input data as TF-model
input_details = interpreter.get_input_details()[0]
interpreter.set_tensor(input_details['index'], input_data)
interpreter.invoke()
out_tensor_index = interpreter.get_output_details()[0]['index']
output_tflite = interpreter.get_tensor(out_tensor_index)
# Compare results
print('tflite_output: ', output_tflite[0, :, :, 0])
print('tf_output: ', output_tf[0, :, :, 0])
print('relative error: ', tf.math.sqrt(2*tf.math.reduce_variance(output_tf-output_tflite) / tf.math.reduce_variance(output_tf+output_tflite)), '%')
plt.plot(output_tflite[0, :, :, 0])
plt.plot(output_tf[0, :, :, 0])
plt.show()
if name == ‘main’:
main()