def _get_hash_table_info_from_trackable(trackable, table_names, key_dtypes,
value_dtypes,
removed_resource_to_placeholder,
placeholder_to_table_info):
# pylint: disable=protected-access
for r in trackable.__dict__.values():
if isinstance(r, TfRestoredResourceType) and hasattr(
r, '_create_resource'):
try:
table_handle = id(r.resource_handle)
except Exception: # pylint: disable=broad-except
continue
initializer = r._create_resource.concrete_functions[0].function_def
new_names, new_k_dtypes, new_v_dtypes = get_hash_table_info(
initializer.node_def)
table_names.extend(new_names)
key_dtypes.extend(new_k_dtypes)
value_dtypes.extend(new_v_dtypes)
if table_handle in removed_resource_to_placeholder and len(
new_names) == 1:
table_info = (new_names[0], new_k_dtypes[0], new_v_dtypes[0])
placeholder_to_table_info[
removed_resource_to_placeholder[table_handle]] = table_info
if isinstance(r, TfAutoTrackableType):
_get_hash_table_info_from_trackable(
r, table_names, key_dtypes, value_dtypes,
removed_resource_to_placeholder, placeholder_to_table_info)
def _get_hash_table_info_from_trackable(trackable, table_info,
removed_resource_to_placeholder, placeholder_to_table_info):
# pylint: disable=protected-access
stack = [trackable]
visited = set()
while stack:
r = stack.pop()
visited.add(id(r))
try:
for trackable_ref in r._checkpoint_dependencies:
if id(trackable_ref.ref) not in visited:
if isinstance(trackable_ref.ref, TfTrackableType):
stack.append(trackable_ref.ref)
except Exception: # pylint: disable=broad-except
continue
for t in r.__dict__.values() if hasattr(r, '__dict__') else []:
if isinstance(t, TfStaticHashTableType) and hasattr(t, '_shared_name'):
info = HashTableInfo(t._shared_name.encode(), t.key_dtype.as_datatype_enum,
t.value_dtype.as_datatype_enum)
table_info.append(info)
table_handle = id(t.resource_handle)
if table_handle in removed_resource_to_placeholder:
placeholder_to_table_info[removed_resource_to_placeholder[table_handle]] = info
if isinstance(r, TfRestoredResourceType) and hasattr(r, '_create_resource'):
try:
table_handle = id(r.resource_handle)
except Exception: # pylint: disable=broad-except
continue
initializer = r._create_resource.concrete_functions[0].function_def
new_table_info = get_hash_table_info(initializer.node_def)
table_info.extend(new_table_info)
if table_handle in removed_resource_to_placeholder and len(new_table_info) == 1:
placeholder_to_table_info[removed_resource_to_placeholder[table_handle]] = new_table_info[0]
def freeze_session(sess, input_names=None, output_names=None, get_tables=False):
"""Freezes the state of a session into a pruned computation graph."""
output_node_names = [i.split(':')[:-1][0] for i in output_names]
keep_var_names = [i.split(':')[:-1][0] for i in input_names]
with sess.graph.as_default():
output_node_names = output_node_names or []
output_node_names += [v.op.name for v in tf_global_variables()]
output_node_names += keep_var_names
graph_def = sess.graph.as_graph_def(add_shapes=True)
for node in graph_def.node:
node.device = ""
graph_def = convert_variables_to_constants(sess, graph_def, output_node_names)
table_info = get_hash_table_info(graph_def)
if get_tables:
initialized_tables = {}
tf.tables_initializer().run(session=sess)
for info in table_info:
h = lookup_ops.hash_table_v2(info.key_dtype, info.val_dtype, shared_name=info.shared_name)
try:
k, v = lookup_ops.lookup_table_export_v2(h, info.key_dtype, info.val_dtype)
k, v = sess.run([k, v])
initialized_tables[n] = (k, v)
except Exception: # pylint: disable=broad-except
logger.warning("Could not initialize table with shared_name = %r", n)
return graph_def, initialized_tables
return graph_def
def from_trackable(trackable, concrete_func, inputs, outputs, large_model):
err_large_model = "model exceeds maximum protobuf size of 2GB. Try setting large_model."
# Avoid errors due to bug in TF freezing
removed_resource_to_placeholder, placeholder_to_resource, graph_captures_copy, func_captures_copy = \
_remove_non_variable_resources_from_captures(concrete_func)
try:
frozen_graph = from_function(concrete_func, inputs, outputs, large_model)
except ValueError as e:
if any(msg in str(e) for msg in ["exceeds maximum protobuf size of 2GB", "string too long"]):
raise ValueError(err_large_model)
raise e
# We might be returning the concrete_func so let's put it back in working order
_restore_captured_resources(concrete_func, graph_captures_copy, func_captures_copy)
table_info = get_hash_table_info(frozen_graph)
placeholder_to_table_info = {}
_get_hash_table_info_from_trackable(trackable, table_info,
removed_resource_to_placeholder, placeholder_to_table_info)
initialized_tables = {}
for info in table_info:
if info.shared_name is not None:
h = lookup_ops.hash_table_v2(info.key_dtype, info.val_dtype, shared_name=info.shared_name)
n = info.shared_name
elif info.resource_input in placeholder_to_resource and info.resource_input not in placeholder_to_table_info:
# We found a lookup op with no corresponding HashTable op, but we can associate the placeholder input
# from the op with the resource handle from graph captures and make up a shared_name
h = placeholder_to_resource[info.resource_input]
n = str(uuid.uuid4()).encode()
info.shared_name = n
placeholder_to_table_info[info.resource_input] = info
else:
# Found a lookup op but the corresponding HashTable op has already been found and processed.
continue
try:
k, v = lookup_ops.lookup_table_export_v2(h, info.key_dtype, info.val_dtype)
initialized_tables[n] = (k.numpy(), v.numpy())
except Exception: # pylint: disable=broad-except
logger.warning("Could not initialize table with shared_name = %r", n)
for placeholder in removed_resource_to_placeholder.values():
if placeholder not in placeholder_to_table_info:
logger.error("Could not find table resource to replace placeholder %s", placeholder)
replace_placeholders_with_tables(frozen_graph, placeholder_to_table_info)
return frozen_graph, initialized_tables
def from_trackable(trackable, concrete_func, inputs, outputs, large_model):
err_large_model = "model exceeds maximum protobuf size of 2GB. Try setting large_model."
# Avoid errors due to bug in TF freezing
removed_resource_to_placeholder, graph_captures_copy, func_captures_copy = \
_remove_non_variable_resources_from_captures(concrete_func)
try:
frozen_graph = from_function(concrete_func, inputs, outputs, large_model)
except ValueError as e:
if any(msg in str(e) for msg in ["exceeds maximum protobuf size of 2GB", "string too long"]):
raise ValueError(err_large_model)
raise e
# We might be returning the concrete_func so let's put it back in working order
_restore_captured_resources(concrete_func, graph_captures_copy, func_captures_copy)
table_names, key_dtypes, value_dtypes = get_hash_table_info(frozen_graph)
placeholder_to_table_info = {}
_get_hash_table_info_from_trackable(trackable, table_names, key_dtypes, value_dtypes,
removed_resource_to_placeholder, placeholder_to_table_info)
initialized_tables = {}
for n, k_dtype, val_dtype in zip(table_names, key_dtypes, value_dtypes):
h = lookup_ops.hash_table_v2(k_dtype, val_dtype, shared_name=n)
try:
k, v = lookup_ops.lookup_table_export_v2(h, k_dtype, val_dtype)
initialized_tables[n] = (k.numpy(), v.numpy())
except Exception: # pylint: disable=broad-except
logger.warning("Could not initialize table with shared_name = %r", n)
for placeholder in removed_resource_to_placeholder.values():
if placeholder not in placeholder_to_table_info:
logger.error("Could not find table resource to replace placeholder %s", placeholder)
replace_placeholders_with_tables(frozen_graph, placeholder_to_table_info)
return frozen_graph, initialized_tables
def _from_saved_model_v2(model_path, input_names, output_names, tag,
signature_def, concrete_function_index, large_model):
"""Load tensorflow graph from saved_model."""
wrn_no_tag = "'--tag' not specified for saved_model. Using --tag serve"
wrn_empty_tag = "'--tag' value is empty string. Using tag =[[]]"
wrn_sig_1 = "'--signature_def' not specified, using first signature: %s"
err_many_sig = "Cannot load multiple signature defs in TF2.x: %s"
err_no_call = "Model doesn't contain usable concrete functions under __call__. Try --signature-def instead."
err_index = "Invalid concrete_function value: %i. Valid values are [0 to %i]"
err_no_sig = "No signatures found in model. Try --concrete_function instead."
err_sig_nomatch = "Specified signature not in model %s"
err_large_model = "model exceeds maximum protobuf size of 2GB. Try running with --large_model flag."
if tag is None:
tag = ['serve']
logger.warning(wrn_no_tag)
if tag == '':
tag = [[]]
logger.warning(wrn_empty_tag)
utils.make_sure(len(signature_def) < 2, err_many_sig, str(signature_def))
imported = tf.saved_model.load(model_path, tags=tag) # pylint: disable=no-value-for-parameter
all_sigs = imported.signatures.keys()
valid_sigs = [s for s in all_sigs if not s.startswith("_")]
logger.info("Signatures found in model: %s",
"[" + ",".join(valid_sigs) + "].")
concrete_func = None
if concrete_function_index is not None:
utils.make_sure(hasattr(imported, "__call__"), err_no_call)
utils.make_sure(
concrete_function_index < len(
imported.__call__.concrete_functions), err_index,
concrete_function_index,
len(imported.__call__.concrete_functions) - 1)
args, kwargs = imported.__call__.concrete_functions[
concrete_function_index].structured_input_signature
concrete_func = imported.__call__.get_concrete_function(
*args, **kwargs)
elif signature_def:
utils.make_sure(signature_def[0] in valid_sigs, err_sig_nomatch,
signature_def[0])
concrete_func = imported.signatures[signature_def[0]]
else:
utils.make_sure(len(valid_sigs) > 0, err_no_sig)
logger.warning(wrn_sig_1, valid_sigs[0])
concrete_func = imported.signatures[valid_sigs[0]]
tensors_to_rename = {}
if input_names is None:
inputs = [
tensor.name for tensor in concrete_func.inputs
if tensor.dtype != tf.dtypes.resource
]
if concrete_func.structured_input_signature is not None:
args, kwargs = concrete_func.structured_input_signature
structured_inputs = [
t.name for t in args if isinstance(t, tf.TensorSpec)
] + sorted(kwargs.keys())
structured_inputs = set(inp + ":0" for inp in structured_inputs)
if any(inp in structured_inputs for inp in inputs):
inputs = [inp for inp in inputs if inp in structured_inputs]
else:
inputs = input_names
if output_names is None:
outputs = [
tensor.name for tensor in concrete_func.outputs
if tensor.dtype != tf.dtypes.resource
]
if isinstance(concrete_func.structured_outputs, dict):
# outputs are sorted, sort structured_outputs the same way
structured_outputs = sorted(
concrete_func.structured_outputs.keys())
tensors_to_rename.update(zip(outputs, structured_outputs))
logger.info("Output names: %r", structured_outputs)
else:
logger.info("Output names: %r", outputs)
else:
outputs = output_names
logger.info(
"Outputs not left as None; will use provided names not structured output names."
)
# Avoid errors due to bug in TF freezing
removed_resource_to_placeholder, graph_captures_copy, func_captures_copy = \
_remove_non_variable_resources_from_captures(concrete_func)
try:
frozen_graph = from_function(concrete_func, inputs, outputs,
large_model)
except ValueError as e:
if any(msg in str(e) for msg in
["exceeds maximum protobuf size of 2GB", "string too long"]):
raise ValueError(err_large_model)
raise e
# We might be returning the concrete_func so let's put it back in working order
_restore_captured_resources(concrete_func, graph_captures_copy,
func_captures_copy)
table_names, key_dtypes, value_dtypes = get_hash_table_info(frozen_graph)
placeholder_to_table_info = {}
_get_hash_table_info_from_trackable(imported, table_names, key_dtypes,
value_dtypes,
removed_resource_to_placeholder,
placeholder_to_table_info)
initialized_tables = {}
for n, k_dtype, val_dtype in zip(table_names, key_dtypes, value_dtypes):
h = lookup_ops.hash_table_v2(k_dtype, val_dtype, shared_name=n)
try:
k, v = lookup_ops.lookup_table_export_v2(h, k_dtype, val_dtype)
initialized_tables[n] = (k.numpy(), v.numpy())
except Exception: # pylint: disable=broad-except
logger.warning("Could not initialize table with shared_name = %r",
n)
for placeholder in removed_resource_to_placeholder.values():
if placeholder not in placeholder_to_table_info:
logger.error(
"Could not find table resource to replace placeholder %s",
placeholder)
replace_placeholders_with_tables(frozen_graph, placeholder_to_table_info)
return frozen_graph, inputs, outputs, concrete_func, imported, initialized_tables, tensors_to_rename
def _from_saved_model_v1(sess, model_path, input_names, output_names, tag,
signature_names):
"""Load tensorflow graph from saved_model."""
wrn_no_tag = "'--tag' not specified for saved_model. Using --tag serve"
wrn_empty_tag = "'--tag' value is empty string. Using tags = []"
wrn_empty_sig = "'--signature_def' not provided. Using all signatures."
if tag is None:
tag = [tf.saved_model.tag_constants.SERVING]
logger.warning(wrn_no_tag)
if not signature_names:
logger.warning(wrn_empty_sig)
if tag == '':
tag = []
logger.warning(wrn_empty_tag)
if not isinstance(tag, list):
tag = [tag]
imported = tf.saved_model.loader.load(sess, tag, model_path)
signatures = []
for k in imported.signature_def.keys():
if k in signature_names or (not signature_names
and not k.startswith("_")):
signatures.append(k)
try:
from tensorflow.contrib.saved_model.python.saved_model import signature_def_utils
# pylint: disable=unnecessary-lambda
get_signature_def = lambda meta_graph_def, k: \
signature_def_utils.get_signature_def_by_key(meta_graph_def, k)
except ImportError:
# TF1.12 changed the api
get_signature_def = lambda meta_graph_def, k: meta_graph_def.signature_def[
k]
if input_names is None:
input_names = []
for k in signatures:
inputs_tensor_info = get_signature_def(imported, k).inputs
for _, input_tensor in inputs_tensor_info.items():
if input_tensor.name not in input_names:
input_names.append(input_tensor.name)
tensors_to_rename = {}
if output_names is None:
output_names = []
for k in signatures:
outputs_tensor_info = get_signature_def(imported, k).outputs
for structured_name, output_tensor in outputs_tensor_info.items():
if output_tensor.name not in output_names:
output_names.append(output_tensor.name)
tensors_to_rename[output_tensor.name] = structured_name
frozen_graph = freeze_session(sess,
input_names=input_names,
output_names=output_names)
table_names, key_dtypes, value_dtypes = get_hash_table_info(frozen_graph)
initialized_tables = {}
tf.tables_initializer().run()
for n, k_dtype, val_dtype in zip(table_names, key_dtypes, value_dtypes):
h = lookup_ops.hash_table_v2(k_dtype, val_dtype, shared_name=n)
try:
k, v = lookup_ops.lookup_table_export_v2(h, k_dtype, val_dtype)
k, v = sess.run([k, v])
initialized_tables[n] = (k, v)
except Exception: # pylint: disable=broad-except
logger.warning("Could not initialize table with shared_name = %r",
n)
return frozen_graph, input_names, output_names, initialized_tables, tensors_to_rename
def _from_saved_model_v2(model_path, input_names, output_names, tag,
signature_def, concrete_function_index, large_model):
"""Load tensorflow graph from saved_model."""
wrn_no_tag = "'--tag' not specified for saved_model. Using --tag serve"
wrn_empty_tag = "'--tag' value is empty string. Using tag =[[]]"
wrn_sig_1 = "'--signature_def' not specified, using first signature: %s"
err_many_sig = "Cannot load multiple signature defs in TF2.x: %s"
err_no_call = "Model doesn't contain usable concrete functions under __call__. Try --signature-def instead."
err_index = "Invalid concrete_function value: %i. Valid values are [0 to %i]"
err_no_sig = "No signatures found in model. Try --concrete_function instead."
err_sig_nomatch = "Specified signature not in model %s"
err_large_model = "model exceeds maximum protobuf size of 2GB. Try running with --large_model flag."
if tag is None:
tag = ['serve']
logger.warning(wrn_no_tag)
if tag == '':
tag = [[]]
logger.warning(wrn_empty_tag)
utils.make_sure(len(signature_def) < 2, err_many_sig, str(signature_def))
imported = tf.saved_model.load(model_path, tags=tag) # pylint: disable=no-value-for-parameter
all_sigs = imported.signatures.keys()
valid_sigs = [s for s in all_sigs if not s.startswith("_")]
logger.info("Signatures found in model: %s",
"[" + ",".join(valid_sigs) + "].")
concrete_func = None
if concrete_function_index is not None:
utils.make_sure(hasattr(imported, "__call__"), err_no_call)
utils.make_sure(
concrete_function_index < len(
imported.__call__.concrete_functions), err_index,
concrete_function_index,
len(imported.__call__.concrete_functions) - 1)
sig = imported.__call__.concrete_functions[
concrete_function_index].structured_input_signature[0]
concrete_func = imported.__call__.get_concrete_function(*sig)
elif signature_def:
utils.make_sure(signature_def[0] in valid_sigs, err_sig_nomatch,
signature_def[0])
concrete_func = imported.signatures[signature_def[0]]
else:
utils.make_sure(len(valid_sigs) > 0, err_no_sig)
logger.warning(wrn_sig_1, valid_sigs[0])
concrete_func = imported.signatures[valid_sigs[0]]
inputs = [
tensor.name for tensor in concrete_func.inputs
if tensor.dtype != tf.dtypes.resource
]
outputs = [
tensor.name for tensor in concrete_func.outputs
if tensor.dtype != tf.dtypes.resource
]
# filter by user specified inputs/outputs
if input_names:
inputs = list(set(input_names) & set(inputs))
if output_names:
outputs = list(set(output_names) & set(outputs))
try:
frozen_graph = from_function(concrete_func, inputs, outputs,
large_model)
except ValueError as e:
if any(msg in str(e) for msg in
["exceeds maximum protobuf size of 2GB", "string too long"]):
raise ValueError(err_large_model)
raise e
table_names, key_dtypes, value_dtypes = get_hash_table_info(frozen_graph)
initialized_tables = {}
for n, k_dtype, val_dtype in zip(table_names, key_dtypes, value_dtypes):
h = lookup_ops.hash_table_v2(k_dtype, val_dtype, shared_name=n)
try:
k, v = lookup_ops.lookup_table_export_v2(h, k_dtype, val_dtype)
initialized_tables[n] = (k.numpy(), v.numpy())
except Exception: # pylint: disable=broad-except
logger.warning("Could not initialize table with shared_name = %r",
n)
return frozen_graph, inputs, outputs, concrete_func, imported, initialized_tables
def _from_saved_model_v2(model_path, input_names, output_names, tag,
signature_def, concrete_function_index, large_model):
"""Load tensorflow graph from saved_model."""
wrn_no_tag = "'--tag' not specified for saved_model. Using --tag serve"
wrn_empty_tag = "'--tag' value is empty string. Using tag =[[]]"
wrn_sig_1 = "'--signature_def' not specified, using first signature: %s"
err_many_sig = "Cannot load multiple signature defs in TF2.x: %s"
err_no_call = "Model doesn't contain usable concrete functions under __call__. Try --signature-def instead."
err_index = "Invalid concrete_function value: %i. Valid values are [0 to %i]"
err_no_sig = "No signatures found in model. Try --concrete_function instead."
err_sig_nomatch = "Specified signature not in model %s"
err_large_model = "model exceeds maximum protobuf size of 2GB. Try running with --large_model flag."
if tag is None:
tag = ['serve']
logger.warning(wrn_no_tag)
if tag == '':
tag = [[]]
logger.warning(wrn_empty_tag)
utils.make_sure(len(signature_def) < 2, err_many_sig, str(signature_def))
imported = tf.saved_model.load(model_path, tags=tag) # pylint: disable=no-value-for-parameter
all_sigs = imported.signatures.keys()
valid_sigs = [s for s in all_sigs if not s.startswith("_")]
logger.info("Signatures found in model: %s",
"[" + ",".join(valid_sigs) + "].")
concrete_func = None
if concrete_function_index is not None:
utils.make_sure(hasattr(imported, "__call__"), err_no_call)
utils.make_sure(
concrete_function_index < len(
imported.__call__.concrete_functions), err_index,
concrete_function_index,
len(imported.__call__.concrete_functions) - 1)
sig = imported.__call__.concrete_functions[
concrete_function_index].structured_input_signature[0]
concrete_func = imported.__call__.get_concrete_function(*sig)
elif signature_def:
utils.make_sure(signature_def[0] in valid_sigs, err_sig_nomatch,
signature_def[0])
concrete_func = imported.signatures[signature_def[0]]
else:
utils.make_sure(len(valid_sigs) > 0, err_no_sig)
logger.warning(wrn_sig_1, valid_sigs[0])
concrete_func = imported.signatures[valid_sigs[0]]
inputs = [
tensor.name for tensor in concrete_func.inputs
if tensor.dtype != tf.dtypes.resource
]
outputs = [
tensor.name for tensor in concrete_func.outputs
if tensor.dtype != tf.dtypes.resource
]
# filter by user specified inputs/outputs
if input_names:
inputs = list(set(input_names) & set(inputs))
if output_names:
outputs = list(set(output_names) & set(outputs))
# Avoid errors due to bug in TF freezing
removed_resource_to_placeholder, graph_captures_copy, func_captures_copy = \
_remove_non_variable_resources_from_captures(concrete_func)
try:
frozen_graph = from_function(concrete_func, inputs, outputs,
large_model)
except ValueError as e:
if any(msg in str(e) for msg in
["exceeds maximum protobuf size of 2GB", "string too long"]):
raise ValueError(err_large_model)
raise e
# We might be returning the concrete_func so let's put it back in working order
_restore_captured_resources(concrete_func, graph_captures_copy,
func_captures_copy)
table_names, key_dtypes, value_dtypes = get_hash_table_info(frozen_graph)
placeholder_to_table_info = {}
if hasattr(imported, '_table') and hasattr(imported._table,
'_create_resource'): # pylint: disable=protected-access
# Add tables from saved_model table initializers
# pylint: disable=protected-access
initializer = imported._table._create_resource.concrete_functions[
0].function_def
new_names, new_k_dtypes, new_v_dtypes = get_hash_table_info(
initializer.node_def)
table_names.extend(new_names)
key_dtypes.extend(new_k_dtypes)
value_dtypes.extend(new_v_dtypes)
table_handle = id(imported._table.resource_handle)
if table_handle in removed_resource_to_placeholder and len(
new_names) == 1:
table_info = (new_names[0], new_k_dtypes[0], new_v_dtypes[0])
placeholder_to_table_info[
removed_resource_to_placeholder[table_handle]] = table_info
initialized_tables = {}
for n, k_dtype, val_dtype in zip(table_names, key_dtypes, value_dtypes):
h = lookup_ops.hash_table_v2(k_dtype, val_dtype, shared_name=n)
try:
k, v = lookup_ops.lookup_table_export_v2(h, k_dtype, val_dtype)
initialized_tables[n] = (k.numpy(), v.numpy())
except Exception: # pylint: disable=broad-except
logger.warning("Could not initialize table with shared_name = %r",
n)
replace_placeholders_with_tables(frozen_graph, placeholder_to_table_info)
return frozen_graph, inputs, outputs, concrete_func, imported, initialized_tables
