I'm trying to get a Mobilenetv2 model (retrained last layers to my data) to run on the Google edge TPU Coral.
I've followed this tuturial https://www.tensorflow.org/lite/performance/post_training_quantization?hl=en to do the post-training quantization. The relevant code is:
...
train = tf.convert_to_tensor(np.array(train, dtype='float32'))
my_ds = tf.data.Dataset.from_tensor_slices(train).batch(1)
# POST TRAINING QUANTIZATION
def representative_dataset_gen():
for input_value in my_ds.take(30):
yield [input_value]
converter = tf.lite.TFLiteConverter.from_keras_model_file(saved_model_dir)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = representative_dataset_gen
converter.target_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
tflite_quant_model = converter.convert()
I've successfully generated the tflite quantized model but when I run the edgetpu_compiler (followed this page https://coral.withgoogle.com/docs/edgetpu/compiler/#usage) I get this output:
edgetpu_compiler Notebooks/MobileNetv2_3class_visit_split_best-val-
acc.h5.quant.tflite
Edge TPU Compiler version 2.0.258810407
INFO: Initialized TensorFlow Lite runtime.
ERROR: quantized_dimension must be in range [0, 1). Was 3.
ERROR: quantized_dimension must be in range [0, 1). Was 3.
ERROR: quantized_dimension must be in range [0, 1). Was 3.
ERROR: quantized_dimension must be in range [0, 1). Was 3.
ERROR: quantized_dimension must be in range [0, 1). Was 3.
ERROR: quantized_dimension must be in range [0, 1). Was 3.
ERROR: quantized_dimension must be in range [0, 1). Was 3.
ERROR: quantized_dimension must be in range [0, 1). Was 3.
ERROR: quantized_dimension must be in range [0, 1). Was 3.
ERROR: quantized_dimension must be in range [0, 1). Was 3.
ERROR: quantized_dimension must be in range [0, 1). Was 3.
ERROR: quantized_dimension must be in range [0, 1). Was 3.
ERROR: quantized_dimension must be in range [0, 1). Was 3.
ERROR: quantized_dimension must be in range [0, 1). Was 3.
ERROR: quantized_dimension must be in range [0, 1). Was 3.
ERROR: quantized_dimension must be in range [0, 1). Was 3.
ERROR: quantized_dimension must be in range [0, 1). Was 3.
Invalid model: Notebooks/MobileNetv2_3class_visit_split_best-val-
acc.h5.quant.tflite
Model could not be parsed
The input shape of the model is a 3 channel RGB image. Is possible to do full integer quantization on 3 channel images? I couldn't find anything saying that you can't either on TensorFlow and Google Coral documentation.