We have trained a model using Microsoft’s Custom Vision. When we try to convert the .pb to .tflite, we run into errors. This is the python code we use is the following:
import tensorflow as tf
from tensorflow import data
from tensorflow.python.saved_model import tag_constants
from tensorflow.python.tools import freeze_graph
from tensorflow.python import ops
from tensorflow.tools.graph_transforms import TransformGraph
graph_def_file = 'model.pb'
input_arrays = ['Placeholder']
output_arrays = ['model_outputs']
transforms = [
'remove_nodes(op=Identity)',
'merge_duplicate_nodes',
'strip_unused_nodes',
'fold_constants(ignore_errors=true)',
'fold_batch_norms'
]
def get_graph_def_from_file(graph_filepath):
with ops.Graph().as_default():
with tf.gfile.GFile(graph_filepath, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
return graph_def
graph_def = get_graph_def_from_file(graph_def_file)
optimized_graph_def = TransformGraph(graph_def, input_arrays, output_arrays, transforms)
tf.train.write_graph(optimized_graph_def, logdir='', as_text=False, name='optimized_model.pb')
optimized_graph_def_file = 'optimized_model.pb'
converter = tf.lite.TFLiteConverter.from_frozen_graph(optimized_graph_def_file,
input_arrays,
output_arrays)
converter.target_ops = [tf.lite.OpsSet.TFLITE_BUILTINS, # We also tried with TF_OPS only and TFLITE_BUILTINS only
tf.lite.OpsSet.SELECT_TF_OPS]
converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]
tflite_model = converter.convert()
open('optimized_converted_model_2.tflite', 'wb').write(tflite_model)
This is the input of the network and it does a convolution fairly early.
As you can see, it appears TensorFlow does not appear to be recognizing this operation:
