TensorFlow 1 Workflow

Use the frozen graph format for conversion from TensorFlow 1. After training, always export the model for inference to this format using the tensorflow.python.tools.freeze_graph method. TensorFlow 1 pre-trained models are also generally available in the frozen .pb file format.

Export as a Frozen Graph and Convert

The following example demonstrates how to export a model to the frozen graph format and convert it to a Core ML program model. Follow these steps:

1. Define a simple model with random weights:

Python

import tensorflow as tf

graph = tf.Graph()
with graph.as_default():
  x = tf.placeholder(tf.float32, shape=[None, 20], name="input")
  W = tf.Variable(tf.truncated_normal([20, 10], stddev=0.1))
  b = tf.Variable(tf.ones([10]))
  y = tf.matmul(x, W) + b
  output_names = [y.op.name]

2. Export the graph as a frozen graph:

Python

import tempfile
import os 
from tensorflow.python.tools.freeze_graph import freeze_graph

model_dir = tempfile.mkdtemp()
graph_def_file = os.path.join(model_dir, 'tf_graph.pb')
checkpoint_file = os.path.join(model_dir, 'tf_model.ckpt')
frozen_graph_file = os.path.join(model_dir, 'tf_frozen.pb')

with tf.Session(graph=graph) as sess:
  # initialize variables
  sess.run(tf.global_variables_initializer())
  # save graph definition somewhere
  tf.train.write_graph(sess.graph, model_dir, graph_def_file, as_text=False)
  # save the weights
  saver = tf.train.Saver()
  saver.save(sess, checkpoint_file)

  # take the graph definition and weights 
  # and freeze into a single .pb frozen graph file
  freeze_graph(input_graph=graph_def_file,
               input_saver="",
               input_binary=True,
               input_checkpoint=checkpoint_file,
               output_node_names=",".join(output_names),
               restore_op_name="save/restore_all",
               filename_tensor_name="save/Const:0",
               output_graph=frozen_graph_file,
               clear_devices=True,
               initializer_nodes="")
  
print("TensorFlow frozen graph saved at {}".format(frozen_graph_file))

3. Convert the model to an ML program and save it as a Core ML model package:

Python

import coremltools as ct

mlmodel = ct.convert(frozen_graph_file, convert_to="mlprogram")
mlmodel.save(frozen_graph_file.replace("pb","mlpackage")))

Convert a Pre-trained Model

The following example demonstrates how to use a downloaded pre-trained model, load it as a tf.Graph object, and then convert and compare predictions with Core ML. This example also compares predictions after conversion to verify the numerical accuracy.

1. Download the float_v2_1.0_224 MobileNet V2 model from TensorFlow models.

2. Load the model in TensorFlow as a tf.graph object.

Python

import tensorflow as tf

path = "~/Downloads/mobilenet_v2_1.0_224/mobilenet_v2_1.0_224_frozen.pb"

# Load the protobuf file from the disk and parse it to retrieve the
# graph_def
with tf.io.gfile.GFile(path, "rb") as f:
    graph_def = tf.compat.v1.GraphDef()
    graph_def.ParseFromString(f.read())

# Import the graph_def into a new Graph
with tf.Graph().as_default() as graph:
    tf.import_graph_def(graph_def, name="")

3. Run the TensorFlow graph to get a prediction on a random input.

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Note

We recommend running the original TensorFlow model once to verify the model, before invoking the Core ML converter.

To run this TensorFlow graph, you need to know its input and output names. Since there is no simple TensorFlow API that can directly provide this information, inspect the operations in the graph:

Python

ops = graph.get_operations()
N = len(ops)
# print all the placeholder ops, these would be the inputs
print("Inputs ops: ")
for op in ops:
    if op.type == "Placeholder":
        print("op name: {}, output shape : {}".
              format(op.name, op.outputs[0].get_shape()))


# print all the tensors that are the first output of an op
# and do not feed into any other op
# these are prospective outputs
print("\nProspective output tensor(s): ", )
sink_ops = []
input_tensors = set()
for op in ops:
    for x in op.inputs:
        if x.name not in input_tensors:
            input_tensors.add(x.name)
for op in ops:
    if len(op.outputs) > 0:
        x = op.outputs[0]
        if x.name not in input_tensors:
            print("tensor name: {}, tensor shape : {}, parent op type: {}"
                  .format(x.name, x.get_shape(), op.type))

The following Out snippet shows what results are printed. Use this information to run the TensorFlow graph.

Out

Inputs ops: 
op name: input, output shape : <unknown>

Prospective output tensor(s): 
tensor name: MobilenetV2/Predictions/Reshape_1:0, tensor shape : (?, 1001), parent op type: Reshape

In the above example, the input shape is missing from the TensorFlow graph. This means it can take a variety of shapes. For this example, use (1, 224, 224, 3), since this is typically used with MobileNet models.

Python

import numpy as np

x = np.random.rand(1, 224, 224, 3)

with tf.Session(graph = graph) as sess:
    tf_out = sess.run('MobilenetV2/Predictions/Reshape_1:0',
                      feed_dict={'input:0': x})

4. Convert the model to a Core ML neural network, and compare predictions. Since the TensorFlow graph lacks shape information, provide it to the converter using the inputs argument.

The following example provides a fixed input shape, but you can use a flexible input shape to enable the generated Core ML model to work using different input shapes. For more information, see Flexible Input Shapes.

Python

import coremltools as ct
mlmodel = ct.convert(graph,
                     inputs=[ct.TensorType(shape=x.shape)])

# Core ML model prediction
coreml_out_dict = mlmodel.predict({"input" : x}, useCPUOnly=True)
coreml_out = list(coreml_out_dict.values())[0]
np.testing.assert_allclose(tf_out, coreml_out, rtol=1e-3, atol=1e-2)

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Tip

The coremltools converter generates by default a model with a MLMultiArray as the input, which works for checking conversion and numerical accuracy. For the final export, the best practice is to use the image type. For details, see Image Inputs.

More Examples

The following examples demonstrate some of the capabilities of the Core ML Tools converter for converting TensorFlow 1 models to Core ML:

• Converting a TensorFlow 1 Image Classifier: Demonstrates the importance of setting the image preprocessing parameters correctly during conversion to get the right results.
• Converting a TensorFlow 1 DeepSpeech Model: Demonstrates automatic handling of flexible shapes using automatic speech recognition.