def save(self, model: Model, model_path: Union[str, Path]) -> None:
if isinstance(model_path, Path):
model_path = model_path.as_posix()
session_config = create_session_config(allow_growth=True)
with tf.compat.v1.Session(config=session_config) as sess:
tf.import_graph_def(model.handle, name="")
is_func = is_function(sess.graph)
if not is_func:
infer_shape(sess.graph, {})
inputs = {name: sess.graph.get_tensor_by_name(spec.name) for name, spec in model.inputs.items()}
outputs = {name: sess.graph.get_tensor_by_name(spec.name) for name, spec in model.outputs.items()}
def _ensure_shape(tensors_dict, tensors_specs):
for name, tensor in tensors_dict.items():
if tensor.shape.rank is None:
tensor.set_shape(tensors_specs[name].shape)
return tensors_dict
inputs = _ensure_shape(inputs, model.inputs)
outputs = _ensure_shape(outputs, model.outputs)
LOGGER.info(inputs)
LOGGER.info(outputs)
tf.compat.v1.saved_model.simple_save(sess, model_path, inputs, outputs, legacy_init_op=None)
def graphs_from_tf(tf_graph,
input_names,
output_names,
shape_override=None,
const_node_values=None,
ignore_default=None,
use_default=None):
"""make tf2onnx internal subgraphs from the tensorflow subgraphs"""
if shape_override is None:
shape_override = {}
ordered_func = resolve_functions(tf_graph)
subgraphs = []
for func in ordered_func:
f_inputs_names = [t.name for t in func.inputs]
f_output_names = [t.name for t in func.outputs]
outputs_to_values, _ = compute_const_folding_using_tf(
func, const_node_values, output_names)
onnx_nodes, _, _, output_shapes, dtypes, _ = \
tensorflow_to_onnx(func, shape_override, const_node_values, ignore_default, use_default)
fg = Graph(onnx_nodes,
output_shapes,
dtypes,
input_names=f_inputs_names,
output_names=f_output_names,
is_subgraph=True,
graph_name=func.name)
fold_constants_using_tf(fg, outputs_to_values)
subgraphs.append(fg)
is_func = is_function(tf_graph)
if not is_func:
tf_graph = infer_shape(tf_graph, shape_override)
outputs_to_values, _ = compute_const_folding_using_tf(
tf_graph, const_node_values, output_names)
onnx_nodes, _, _, output_shapes, dtypes, _ = \
tensorflow_to_onnx(tf_graph, shape_override, const_node_values, ignore_default, use_default)
utils.check_io(input_names, output_names, output_shapes.keys())
main_g = Graph(onnx_nodes,
output_shapes,
dtypes,
input_names=input_names,
output_names=output_names)
fold_constants_using_tf(main_g, outputs_to_values)
return main_g, subgraphs
def process_tf_graph(tf_graph, continue_on_error=False, verbose=False, target=None,
opset=None, custom_op_handlers=None, custom_rewriter=None,
extra_opset=None, shape_override=None, inputs_as_nchw=None,
input_names=None, output_names=None, is_subgraph=False):
"""Convert tensorflow graph to onnx graph.
Args:
tf_graph: tensorflow graph
continue_on_error: if an op can't be processed (aka there is no mapping), continue
verbose: print summary stats (deprecated)
target: list of workarounds applied to help certain platforms
opset: the opset to be used (int, default is latest)
custom_op_handlers: dictionary of custom ops handlers
custom_rewriter: list of custom graph rewriters
extra_opset: list of extra opset's, for example the opset's used by custom ops
shape_override: dict with inputs that override the shapes given by tensorflow
inputs_as_nchw: transpose inputs in list from nchw to nchw
input_names: list of input node names in graph, input name format as node_name:port_id
output_names: list of output node names in graph, output name format as node_name:port_id
Return:
onnx graph
"""
if verbose:
logger.warning("Argument verbose for process_tf_graph is deprecated. Please use --verbose option instead.")
del verbose
opset = utils.find_opset(opset)
if not is_subgraph:
logger.info("Using tensorflow=%s, onnx=%s, tf2onnx=%s/%s",
get_tf_version(), utils.get_onnx_version(), tf2onnx.__version__, tf2onnx.version.git_version[:6])
logger.info("Using opset <onnx, %s>", opset)
if opset > schemas.get_max_supported_opset_version():
logger.warning("Currently installed onnx package %s is too low to support opset %s, "
"please upgrade onnx package to avoid potential conversion issue.",
utils.get_onnx_version(), opset)
is_func = is_function(tf_graph)
if not is_func:
tf_graph = infer_shape(tf_graph, shape_override)
if shape_override is None:
shape_override = {}
if inputs_as_nchw is None:
inputs_as_nchw = []
if target is None:
target = constants.DEFAULT_TARGET
onnx_nodes, op_cnt, attr_cnt, output_shapes, dtypes, _ = tensorflow_to_onnx(tf_graph, shape_override)
if not is_subgraph:
# make tf2onnx internal subgraphs from the tensorflow subgraphs
ordered_func = resolve_functions(tf_graph)
for func in ordered_func:
f_inputs_names = [t.name for t in func.inputs]
f_output_names = [t.name for t in func.outputs]
fg = process_tf_graph(func, continue_on_error, False, target, opset,
custom_op_handlers, custom_rewriter,
extra_opset, shape_override, inputs_as_nchw,
f_inputs_names, f_output_names, is_subgraph=True)
fg.graph_name = func.name
fg.func_inputs = f_inputs_names
set_function(func.name, fg)
io_to_check = []
if input_names:
io_to_check.extend(input_names)
if output_names:
io_to_check.extend(output_names)
if io_to_check:
# check output existence in case user passed in wrong output ids
non_exists = set(io_to_check) - set(output_shapes.keys())
if non_exists:
logger.error("\nFailed to convert: inputs/outputs specified do not exist, make sure your passed"
"in format: input/output_node_name:port_id. Problematical inputs/outputs are: %s \n",
non_exists)
raise ValueError("Inputs/Outputs Not Found")
g = Graph(onnx_nodes, output_shapes, dtypes, target, opset, extra_opset, output_names, is_subgraph=is_subgraph)
# create ops mapping for the desired opsets
ops_mapping = handler.tf_op.create_mapping(g.opset, g.extra_opset)
# apply custom ops on top of the assembled opset. We can either complement the opset
# or override existing ops with a custom op.
if custom_op_handlers is not None:
# below is a bit tricky since there are a few api's:
# 1. the future way we want custom ops to be registered with the @tf_op decorator. THose handlers will be
# registered via the decorator on load of the module ... nothing is required here.
# 2. the old custom op api: a dictionary of {name: (func, args[])
# We deal with this by using a compat_handler that wraps to old handler with a new style handler.
# This is tempoary to give people give to move to the new api and after tf2onnx-1.5 we want to remove this
custom_opset = {}
for k, v in custom_op_handlers.items():
# FIXME: remove this after tf2onnx-1.5
def compat_handler(ctx, node, **kwargs):
# wrap old handler
name = node.name
args = kwargs["args"]
func = kwargs["func"]
return func(ctx, node, name, args)
args = v[1]
kwargs = {"func": v[0]}
if args:
onnx_op = args[0]
kwargs["onnx_op"] = onnx_op
args = args[1:]
kwargs["args"] = args
new_handler = handler.tf_op(k,
domain=constants.TENSORFLOW_OPSET.domain,
kwargs=kwargs)
new_handler.register_compat_handler(compat_handler, 1)
custom_opset[k] = (compat_handler, kwargs)
ops_mapping.update(custom_opset)
if inputs_as_nchw:
transpose_inputs(g, inputs_as_nchw)
# pre-processing graph rewrites
# bi-directional re-writer should be placed after single directional re-writer
rewriters = [rewrite_transpose, rewrite_flatten, rewrite_gemm,
rewrite_random_uniform, rewrite_random_uniform_fold_const,
rewrite_random_normal, rewrite_dropout, rewrite_eye,
rewrite_leakyrelu, rewrite_thresholded_relu, rewrite_conv2d_with_pad,
rewrite_single_direction_lstm, rewrite_bi_direction_lstm,
rewrite_single_direction_gru, rewrite_bi_direction_gru,
rewrite_custom_rnn_cell, rewrite_generic_loop, rewrite_cond,
rewrite_biasadd_with_conv2d,
]
if custom_rewriter is not None:
rewriters.extend(custom_rewriter)
run_rewriters(g, rewriters, continue_on_error)
# some nodes may already copied into inner Graph, so remove them from main Graph.
g.delete_unused_nodes(output_names)
topological_sort(g, continue_on_error)
mapped_op, unmapped_op, exceptions = tensorflow_onnx_mapping(g, ops_mapping)
if unmapped_op:
logger.error("Unsupported ops: %s", unmapped_op)
if exceptions and not continue_on_error:
raise exceptions[0]
# post-processing rewriters
late_rewriters = []
if constants.TARGET_RS5 in target:
late_rewriters.append(rewrite_incomplete_type_support_rs5)
if constants.TARGET_RS6 in target:
late_rewriters.append(rewrite_incomplete_type_support_rs6)
if late_rewriters:
run_rewriters(g, late_rewriters, continue_on_error)
# onnx requires topological sorting
topological_sort(g, continue_on_error)
g.update_proto()
logger.verbose(
"Summay Stats:\n"
"\ttensorflow ops: {}\n"
"\ttensorflow attr: {}\n"
"\tonnx mapped: {}\n"
"\tonnx unmapped: {}".format(op_cnt, attr_cnt, mapped_op, unmapped_op))
return g
def process_tf_graph(tf_graph,
continue_on_error=False,
verbose=False,
target=None,
opset=None,
custom_op_handlers=None,
custom_rewriter=None,
extra_opset=None,
shape_override=None,
inputs_as_nchw=None,
input_names=None,
output_names=None,
ignore_default=None,
use_default=None,
is_subgraph=False,
const_node_values=None,
tensors_to_rename=None,
initialized_tables=None,
tflite_path=None,
dequantize=False):
"""Convert tensorflow graph to onnx graph.
Args:
tf_graph: tensorflow graph
continue_on_error: if an op can't be processed (aka there is no mapping), continue
verbose: print summary stats (deprecated)
target: list of workarounds applied to help certain platforms
opset: the opset to be used (int, default is latest)
custom_op_handlers: dictionary of custom ops handlers
custom_rewriter: list of custom graph rewriters
extra_opset: list of extra opset's, for example the opset's used by custom ops
shape_override: dict with inputs that override the shapes given by tensorflow
inputs_as_nchw: transpose inputs in list from nchw to nhwc
input_names: list of input node names in graph, input name format as node_name:port_id. Optional.
output_names: list of output node names in graph, format is node_name:port_id. Optional for tflite.
ignore_default: list of node names of PlaceholderWithDefault ops to change into Placeholder ops
use_default: list of node names of PlaceholderWithDefault ops to change into Identity ops using the default
const_node_values: a dict returned by compress_graph_def mapping node names to tensor values
tensors_to_rename: an optional dict (string->string) mapping tensor names to new names
initialized_tables: mapping from table shared_names to tuple of keys and values of table
tflite_path: Path to a tflite file to convert. If used, pass None to tf_graph
Return:
onnx graph
"""
# NOTE: process_parsed_graph and Graph are always given tensors post-rename.
# process_tf_graph (this function) gets tensors pre-rename.
if verbose:
logger.warning(
"Argument verbose for process_tf_graph is deprecated. Please use --verbose option instead."
)
del verbose
opset = utils.find_opset(opset)
if not is_subgraph:
logger.info("Using tensorflow=%s, onnx=%s, tf2onnx=%s/%s",
get_tf_version(), utils.get_onnx_version(),
tf2onnx.__version__, tf2onnx.version.git_version[:6])
logger.info("Using opset <onnx, %s>", opset)
if opset > schemas.get_max_supported_opset_version():
logger.warning(
"Currently installed onnx package %s is too low to support opset %s, "
"please upgrade onnx package to avoid potential conversion issue.",
utils.get_onnx_version(), opset)
if shape_override is None:
shape_override = {}
if inputs_as_nchw is None:
inputs_as_nchw = []
if target is None:
target = constants.DEFAULT_TARGET
def check_io(input_names, output_names, output_shapes):
io_to_check = []
if input_names:
io_to_check.extend(input_names)
if output_names:
io_to_check.extend(output_names)
if io_to_check:
# check output existence in case user passed in wrong output ids
non_exists = set(io_to_check) - set(output_shapes.keys())
if non_exists:
logger.error(
"\nFailed to convert: inputs/outputs specified do not exist, make sure your passed"
"in format: input/output_node_name:port_id. Problematic inputs/outputs are: %s \n",
non_exists)
raise ValueError("Inputs/Outputs Not Found")
def rename_tensors_in_dict(d):
if tensors_to_rename is None:
return d
return {tensors_to_rename.get(k, k): v for k, v in d.items()}
def rename_tensors_in_list(tensors):
if tensors_to_rename is None or tensors is None:
return tensors
return [tensors_to_rename.get(t, t) for t in tensors]
def rename_tensors_in_nodes(onnx_nodes):
if tensors_to_rename is None:
return
for n in onnx_nodes:
n.input[:] = rename_tensors_in_list(n.input)
n.output[:] = rename_tensors_in_list(n.output)
if tflite_path is not None:
tflite_graphs, opcodes, model, tensor_shapes = read_tflite_model(
tflite_path)
main_g = None
inputs_as_nchw = rename_tensors_in_list(inputs_as_nchw)
for i, tfl_graph in enumerate(tflite_graphs):
is_main_g = i == len(tflite_graphs) - 1
prefix = '' if is_main_g else tfl_graph.Name().decode() + '_'
tensor_shapes_from_interpreter = None
if is_main_g:
tensor_shapes_from_interpreter = tensor_shapes
onnx_nodes, op_cnt, attr_cnt, output_shapes, dtypes, f_inputs, f_outputs, graph_name = \
parse_tflite_graph(tfl_graph, opcodes, model, prefix, tensor_shapes_from_interpreter)
g_inputs = f_inputs
g_outputs = f_outputs
if is_main_g:
# Override IO in main graph
check_io(input_names, output_names, output_shapes)
if input_names is not None:
g_inputs = input_names
if output_names is not None:
g_outputs = output_names
rename_tensors_in_nodes(onnx_nodes)
g_inputs = rename_tensors_in_list(g_inputs)
g_outputs = rename_tensors_in_list(g_outputs)
output_shapes = rename_tensors_in_dict(output_shapes)
dtypes = rename_tensors_in_dict(dtypes)
g = Graph(onnx_nodes, output_shapes, dtypes, target, opset,
extra_opset, g_inputs, g_outputs, is_subgraph)
fg = process_parsed_graph(g,
custom_op_handlers,
inputs_as_nchw,
continue_on_error,
custom_rewriter,
target,
g_outputs, {}, {}, {},
op_cnt,
attr_cnt,
is_tflite=True,
dequantize=dequantize)
fg.graph_name = graph_name
if is_main_g:
main_g = fg
else:
set_function(graph_name, fg)
return main_g
is_func = is_function(tf_graph)
if not is_func:
tf_graph = infer_shape(tf_graph, shape_override)
outputs_to_values, outputs_to_dtypes = compute_const_folding_using_tf(
tf_graph, const_node_values, output_names)
onnx_nodes, op_cnt, attr_cnt, output_shapes, dtypes, _ = \
tensorflow_to_onnx(tf_graph, shape_override, const_node_values, ignore_default, use_default)
if not is_subgraph:
# make tf2onnx internal subgraphs from the tensorflow subgraphs
ordered_func = resolve_functions(tf_graph)
for func in ordered_func:
f_inputs_names = [t.name for t in func.inputs]
f_output_names = [t.name for t in func.outputs]
fg = process_tf_graph(func,
continue_on_error,
False,
target,
opset,
custom_op_handlers,
custom_rewriter,
extra_opset,
shape_override,
inputs_as_nchw,
f_inputs_names,
f_output_names,
is_subgraph=True,
const_node_values=const_node_values,
tensors_to_rename=tensors_to_rename,
initialized_tables=initialized_tables)
fg.graph_name = func.name
set_function(func.name, fg)
check_io(input_names, output_names, output_shapes)
if not is_subgraph:
rename_tensors_in_nodes(onnx_nodes)
input_names = rename_tensors_in_list(input_names)
output_names = rename_tensors_in_list(output_names)
output_shapes = rename_tensors_in_dict(output_shapes)
dtypes = rename_tensors_in_dict(dtypes)
inputs_as_nchw = rename_tensors_in_list(inputs_as_nchw)
g = Graph(onnx_nodes, output_shapes, dtypes, target, opset, extra_opset,
input_names, output_names, is_subgraph)
g = process_parsed_graph(g, custom_op_handlers, inputs_as_nchw,
continue_on_error, custom_rewriter, target,
output_names, initialized_tables,
outputs_to_values, outputs_to_dtypes, op_cnt,
attr_cnt)
return g
