def testSetTensorShapeEmpty(self):
tensor = array_ops.placeholder(shape=[None, 3, 5],
dtype=dtypes.float32)
self.assertEqual([None, 3, 5], tensor.shape.as_list())
util.set_tensor_shapes([tensor], {})
self.assertEqual([None, 3, 5], tensor.shape.as_list())
def testSetTensorShapeNoneValid(self):
with ops.Graph().as_default():
tensor = array_ops.placeholder(dtype=dtypes.float32)
self.assertEqual(None, tensor.shape)
util.set_tensor_shapes([tensor], {"Placeholder": [1, 3, 5]})
self.assertEqual([1, 3, 5], tensor.shape.as_list())
def testSetTensorShapeEmpty(self):
with ops.Graph().as_default():
tensor = array_ops.placeholder(dtype=dtypes.float32, shape=[None, 3, 5])
self.assertAllEqual([None, 3, 5], tensor.shape)
util.set_tensor_shapes([tensor], {})
self.assertAllEqual([None, 3, 5], tensor.shape)
def freeze_saved_model(saved_model_dir, input_arrays, input_shapes,
output_arrays, tag_set, signature_key):
"""Converts a SavedModel to a frozen graph.
Args:
saved_model_dir: SavedModel directory to convert.
input_arrays: List of input tensors to freeze graph with. Uses input arrays
from SignatureDef when none are provided.
input_shapes: Dict of strings representing input tensor names to list of
integers representing input shapes (e.g., {"foo": : [1, 16, 16, 3]}).
Automatically determined when input shapes is None (e.g., {"foo" : None}).
output_arrays: List of output tensors to freeze graph with. Uses output
arrays from SignatureDef when none are provided.
tag_set: Set of tags identifying the MetaGraphDef within the SavedModel to
analyze. All tags in the tag set must be present.
signature_key: Key identifying SignatureDef containing inputs and outputs.
Returns:
frozen_graph_def: Frozen GraphDef.
in_tensors: List of input tensors for the graph.
out_tensors: List of output tensors for the graph.
Raises:
ValueError:
SavedModel doesn't contain a MetaGraphDef identified by tag_set.
signature_key is not in the MetaGraphDef.
assets/ directory is in the MetaGraphDef.
input_shapes does not match the length of input_arrays.
input_arrays or output_arrays are not valid.
"""
# Read SignatureDef.
meta_graph = get_meta_graph_def(saved_model_dir, tag_set)
signature_def = get_signature_def(meta_graph, signature_key)
inputs, outputs = get_inputs_outputs(signature_def)
# Check SavedModel for assets directory.
collection_def = meta_graph.collection_def
if constants.ASSETS_KEY in collection_def:
raise ValueError(
"SavedModels with assets/ directory are not supported.")
graph = ops.Graph()
with session.Session(graph=graph) as sess:
loader.load(sess, meta_graph.meta_info_def.tags, saved_model_dir)
# Gets input and output tensors.
# TODO(zhixianyan): Use TFLite supported Op list to filter outputs.
in_tensors = _get_tensors(graph, inputs, input_arrays)
out_tensors = _get_tensors(graph, outputs, output_arrays)
util.set_tensor_shapes(in_tensors, input_shapes)
output_names = [node.split(":")[0] for node in outputs]
frozen_graph_def = tf_graph_util.convert_variables_to_constants(
sess, graph.as_graph_def(), output_names)
return frozen_graph_def, in_tensors, out_tensors
def testSetTensorShapeDimensionInvalid(self):
# Tests set_tensor_shape where the shape passed in is incompatiable.
tensor = array_ops.placeholder(shape=[None, 3, 5], dtype=dtypes.float32)
self.assertEqual([None, 3, 5], tensor.shape.as_list())
with self.assertRaises(ValueError) as error:
util.set_tensor_shapes([tensor], {"Placeholder": [1, 5, 5]})
self.assertIn("The shape of tensor 'Placeholder' cannot be changed",
str(error.exception))
self.assertEqual([None, 3, 5], tensor.shape.as_list())
def testSetTensorShapeArrayInvalid(self):
# Tests set_tensor_shape where the tensor name passed in doesn't exist.
tensor = array_ops.placeholder(shape=[None, 3, 5], dtype=dtypes.float32)
self.assertEqual([None, 3, 5], tensor.shape.as_list())
with self.assertRaises(ValueError) as error:
util.set_tensor_shapes([tensor], {"invalid-input": [5, 3, 5]})
self.assertEqual(
"Invalid tensor 'invalid-input' found in tensor shapes map.",
str(error.exception))
self.assertEqual([None, 3, 5], tensor.shape.as_list())
def freeze_saved_model(saved_model_dir, input_arrays, input_shapes,
output_arrays, tag_set, signature_key):
"""Converts a SavedModel to a frozen graph.
Args:
saved_model_dir: SavedModel directory to convert.
input_arrays: List of input tensors to freeze graph with. Uses input arrays
from SignatureDef when none are provided.
input_shapes: Dict of strings representing input tensor names to list of
integers representing input shapes (e.g., {"foo": : [1, 16, 16, 3]}).
Automatically determined when input shapes is None (e.g., {"foo" : None}).
output_arrays: List of output tensors to freeze graph with. Uses output
arrays from SignatureDef when none are provided.
tag_set: Set of tags identifying the MetaGraphDef within the SavedModel to
analyze. All tags in the tag set must be present.
signature_key: Key identifying SignatureDef containing inputs and outputs.
Returns:
frozen_graph_def: Frozen GraphDef.
in_tensors: List of input tensors for the graph.
out_tensors: List of output tensors for the graph.
Raises:
ValueError:
SavedModel doesn't contain a MetaGraphDef identified by tag_set.
signature_key is not in the MetaGraphDef.
assets/ directory is in the MetaGraphDef.
input_shapes does not match the length of input_arrays.
input_arrays or output_arrays are not valid.
"""
# Read SignatureDef.
meta_graph = get_meta_graph_def(saved_model_dir, tag_set)
signature_def = get_signature_def(meta_graph, signature_key)
inputs, outputs = get_inputs_outputs(signature_def)
# Check SavedModel for assets directory.
collection_def = meta_graph.collection_def
if constants.ASSETS_KEY in collection_def:
raise ValueError("SavedModels with assets/ directory are not supported.")
graph = ops.Graph()
with session.Session(graph=graph) as sess:
loader.load(sess, meta_graph.meta_info_def.tags, saved_model_dir)
# Gets input and output tensors.
# TODO(zhixianyan): Use TFLite supported Op list to filter outputs.
in_tensors = _get_tensors(graph, inputs, input_arrays)
out_tensors = _get_tensors(graph, outputs, output_arrays)
util.set_tensor_shapes(in_tensors, input_shapes)
frozen_graph_def = util.freeze_graph(sess, in_tensors, out_tensors)
return frozen_graph_def, in_tensors, out_tensors
def testSetTensorShapeNoneValid(self):
tensor = array_ops.placeholder(dtype=dtypes.float32)
self.assertEqual(None, tensor.shape)
util.set_tensor_shapes([tensor], {"Placeholder": [1, 3, 5]})
self.assertEqual([1, 3, 5], tensor.shape.as_list())
def testSetTensorShapeEmpty(self):
tensor = array_ops.placeholder(shape=[None, 3, 5], dtype=dtypes.float32)
self.assertEqual([None, 3, 5], tensor.shape.as_list())
util.set_tensor_shapes([tensor], {})
self.assertEqual([None, 3, 5], tensor.shape.as_list())
