def load(self, graph: Graph):
argv = graph.graph['cmd_params']
if argv.tensorflow_custom_layer_libraries:
libraries = argv.tensorflow_custom_layer_libraries.split(',')
for library in libraries:
log.info('Loading library "{}" with custom operations'.format(library))
tf_v1.load_op_library(library)
graph_def, variables_values = load_tf_graph_def(graph_file_name=argv.input_model,
is_binary=not argv.input_model_is_text,
checkpoint=argv.input_checkpoint,
user_output_node_names_list=argv.output,
model_dir=argv.saved_model_dir,
meta_graph_file=argv.input_meta_graph,
saved_model_tags=argv.saved_model_tags)
try:
tf_v1.import_graph_def(graph_def, name='')
except:
log.warning("TensorFlow post-processing of loaded model was unsuccessful. "
"This is an optional step that Model Optimizer performs for any input model but it is not usually "
"required for all models."
"It likely means that the original model is ill-formed. "
"Model Optimizer will continue converting this model.")
log.debug("Number of nodes in graph_def: {}".format(len(graph_def.node))) # pylint: disable=no-member
if argv.tensorboard_logdir:
tensorboard_util.dump_for_tensorboard(graph_def, argv.tensorboard_logdir)
update_extractors_with_extensions(tf_op_extractors)
try:
protobuf2nx(graph, graph_def)
except Exception as e:
raise Error(
'Cannot pre-process TensorFlow graph after reading from model file "{}". ' \
'File is corrupt or has unsupported format. Details: {}. ' +
refer_to_faq_msg(44),
argv.model_name,
str(e)
) from e
graph.__setattr__('name', argv.model_name)
# 'layout' parameter change may cause an issue in EltwiseInputReshape replacer
# and convert_nhwc_to_nchw(graph)
graph.graph['layout'] = 'NCHW' if argv.disable_nhwc_to_nchw else 'NHWC'
graph.graph['fw'] = 'tf'
graph.graph['variables_values'] = variables_values
del variables_values
restore_edges(graph, get_tf_edges)
remove_control_dependency_inputs(graph)
graph.check_empty_graph('protobuf2nx. It may happen due to problems with loaded model')
extract_node_attrs(graph, lambda node: tf_op_extractor(node, check_for_duplicates(tf_op_extractors)))
def extract_if(cls, if_node: Node):
If.update_node_stat(if_node, {})
# check that required body and condition functions exist in the graph library
main_graph = if_node.graph
then_graph_proto = get_graph_proto(main_graph, 'then_branch', if_node)
else_graph_proto = get_graph_proto(main_graph, 'else_branch', if_node)
then_graph = create_internal_graph(main_graph)
if_node['then_graph'] = then_graph
else_graph = create_internal_graph(main_graph)
if_node['else_graph'] = else_graph
# create Parameter nodes for the then/else graphs
for input_index, (body_graph, body_graph_proto) in enumerate(
zip((then_graph, else_graph),
(then_graph_proto, else_graph_proto))):
body_parameters, body_parameter_names = convert_graph_inputs_to_parameters(
body_graph, body_graph_proto)
# update the If body graph with the body function graph
body_results = []
update_body_graph(body_graph, body_graph_proto, body_parameter_names,
body_results)
body_graph.stage = 'front'
# connect external input ports with body parameter nodes except input with condition
for idx in range(0, len(body_parameters)):
If.connect_body_input(if_node, not input_index, idx + 1,
body_parameters[idx])
# connect body outputs with If operation output ports
for idx in range(len(body_results)):
If.connect_body_output(if_node, not input_index, idx,
body_results[idx])
# run function to parse body nodes attributes similar to the main graph
extract_node_attrs(
body_graph, lambda node: tf_op_extractor(
node, check_for_duplicates(tf_op_extractors)))
return cls.enabled
def tf2nx(argv: argparse.Namespace, model_file_name: str, output_model_name: str, outputs: list, output_dir: str,
scale: float, is_binary: bool,
user_shapes: [None, list, np.array] = None,
mean_scale_values: [dict, list] = ()):
"""
Convert TF GraphDef object to NetworkX representation.
The resulting graph is still TF-specific and needs normalization passes to be applied.
The specific TF structure assumes each GraphDef node is converted to a single
NetworkX node, node id is an original TF node name, and edges go directly from one op to another op.
"""
meta_info = get_meta_info(argv)
if argv.tensorflow_custom_layer_libraries:
libraries = argv.tensorflow_custom_layer_libraries.split(',')
for library in libraries:
log.info('Loading library "{}" with custom operations'.format(library))
tf.load_op_library(library)
graph_def, variables_values = load_tf_graph_def(graph_file_name=model_file_name, is_binary=is_binary,
checkpoint=argv.input_checkpoint,
user_output_node_names_list=outputs,
model_dir=argv.saved_model_dir,
meta_graph_file=argv.input_meta_graph,
saved_model_tags=argv.saved_model_tags)
try:
tf.import_graph_def(graph_def, name='')
except:
log.warning("TensorFlow post-processing of loaded model was unsuccessful. "
"This is an optional step that Model Optimizer performs for any input model but it is not usually "
"required for all models."
"It likely means that the original model is ill-formed. "
"Model Optimizer will continue converting this model.")
log.debug("Number of nodes in graph_def: {}".format(len(graph_def.node))) # pylint: disable=no-member
if argv.tensorboard_logdir:
tensorboard.dump_for_tensorboard(graph_def, argv.tensorboard_logdir)
update_extractors_with_extensions(tf_op_extractors)
try:
graph = protobuf2nx(graph_def)
graph.__setattr__('name', output_model_name)
# 'layout' parameter change may cause an issue in EltwiseInputReshape replacer
# and convert_nhwc_to_nchw(graph)
graph.graph['layout'] = 'NCHW' if argv.disable_nhwc_to_nchw else 'NHWC'
graph.graph['cmd_params'] = argv
graph.graph['fw'] = 'tf'
graph.graph['ir_version'] = 2 if argv.generate_deprecated_IR_V2 else 4
if graph.graph['ir_version'] == 2:
# When the deprecated IR version was requested,
# we configure only those phases that can lead to
# functional regressions in the version 2.
# BasicLSTMCell is one such transformation; when it is turned off,
# the body of TF basic_lstm_cell is converted as-is in a decomposed form,
# and should work in version 2.
BasicLSTMCell.enabled = False
# placeholder for request from a transformation pass to repeat the entire conversion
graph.graph['repeat_conversion'] = False
graph = restore_edges(graph, get_tf_edges)
graph = remove_control_dependency_inputs(graph)
# extract basic attributes earlier to enable some passes that relies on them before full attribute
# extractor is called
extract_node_attrs(graph, lambda node: (True, common_tf_fields(node)))
except Exception as e:
raise Error(
'Cannot pre-process TensorFlow graph after reading from model file "{}". ' \
'File is corrupt or has unsupported format. Details: {}. ' +
refer_to_faq_msg(44),
model_file_name,
str(e)
) from e
check_empty_graph(graph, 'protobuf2nx. It may happen due to problems with loaded model')
packed_user_shapes, packed_outputs, freeze_placeholder = user_data_repack(graph, user_shapes, outputs,
argv.freeze_placeholder_with_value)
if freeze_placeholder is not None:
FreezePlaceholderValue.enabled = True
FreezePlaceholderValue.replacement_dict = freeze_placeholder
update_registration()
GemmResolver.enabled = False
inputs = list(packed_user_shapes.keys()) if packed_user_shapes is not None and isinstance(packed_user_shapes,
dict) else None
graph.graph['inputs'] = inputs # save user defined inputs for other extensions
output_op_nodes = add_output_ops(graph, packed_outputs, inputs=packed_user_shapes)
input_op_nodes = add_input_ops(graph, packed_user_shapes, True)
# this call of 'graph_clean_up' removes child nodes of outputs which is useful when custom output is specified
graph_clean_up_tf(graph)
check_empty_graph(graph, 'add_output_ops and add_input_ops. It may happen due to absence of \'Placeholder\' layer '
'in the model')
variables_to_constants(graph, variables_values)
del variables_values
graph_clean_up_tf(graph)
if argv.tensorflow_custom_operations_config_update:
if update_custom_replacement_config_file(graph, argv.tensorflow_custom_operations_config_update):
return 0
else:
return 1
unsupported_ops_to_offload_to_tf = list()
MAX_ITERATIONS = 5
cur_iteration = 0
while cur_iteration < MAX_ITERATIONS:
graph_copy = copy.deepcopy(graph) # create a copy of graph for the case when some ops are unsupported
if argv.tensorflow_subgraph_patterns is not None:
csc.replace_subgraph_calls(graph, argv.tensorflow_subgraph_patterns)
if argv.tensorflow_operation_patterns is not None:
csc.offload_operations_to_tf(graph, argv.tensorflow_operation_patterns)
if argv.offload_unsupported_operations_to_tf and len(unsupported_ops_to_offload_to_tf):
csc.offload_unsupported_operations_to_tf(graph, unsupported_ops_to_offload_to_tf)
extract_node_attrs(graph, lambda node: tf_op_extractor(node, check_for_duplicates(tf_op_extractors)))
if argv.tensorflow_use_custom_operations_config is not None:
registry = CustomReplacementRegistry()
registry.add_custom_replacement_description_from_config(argv.tensorflow_use_custom_operations_config)
# automatically generate sub-classes for custom replacements that replace sub-graph with a single node
for replacement_desc in registry.get_all_replacements_descriptions():
if replacement_desc.has('op'):
type('FrontReplacementFromConfigFileOp' + replacement_desc.op, (FrontReplacementFromConfigFileOp,),
{'replacement_id': replacement_desc.id})
update_registration()
override_placeholder_shapes(graph, packed_user_shapes)
# the user shapes are used to convert TensorFlow Object Detection API models
graph.graph['user_shapes'] = packed_user_shapes
class_registration.apply_replacements(graph, class_registration.ClassType.FRONT_REPLACER)
override_batch(graph, argv.batch)
create_tensor_nodes(graph)
graph_clean_up_tf(graph)
remove_output_ops(graph)
partial_infer(graph)
delete_control_flow_edges(graph)
replacer = AddIsCyclicAttribute()
replacer.find_and_replace_pattern(graph)
# TENSOR ITERATOR CREATING BEGINS
if graph.graph['is_cyclic']:
replacer = DeleteSelect()
replacer.find_and_replace_pattern(graph)
replacer = SmartInputMatcher()
replacer.find_and_replace_pattern(graph)
replacer = SmartOutputMatcher()
replacer.find_and_replace_pattern(graph)
replacer = LoopConditionMatcher()
replacer.find_and_replace_pattern(graph)
replacer = SimpleConditionMather()
replacer.find_and_replace_pattern(graph)
replacer = BackEdgesMatching()
replacer.find_and_replace_pattern(graph)
replacer = ConditionChecks()
replacer.find_and_replace_pattern(graph)
delete_not_executable(graph)
graph_clean_up_tf(graph)
if graph.graph['is_cyclic']:
replacer = SimpleInputMatcher()
replacer.find_and_replace_pattern(graph)
replacer = BackEdgeSimpleInputMatcher()
replacer.find_and_replace_pattern(graph)
# Here will be optimizing path (ops after Enter and before body take out of body)
replacer = TensorIteratorMerge()
replacer.find_and_replace_pattern(graph)
# TENSOR ITERATOR CREATING ENDS
check_for_cycle(graph)
for_graph_and_each_sub_graph_recursively(graph, graph_clean_up_tf)
check_empty_graph(graph, 'partial_infer')
csc.prepare_tf_call_nodes(graph)
graph_clean_up_tf(graph)
duplicate_shared_weights(graph)
input_op_nodes = add_input_ops(graph, packed_user_shapes, False)
graph_clean_up_tf(graph)
check_empty_graph(graph, 'add_input_ops')
change_placeholders_types_to_FP32(graph)
scale_input(graph, scale)
add_mean_scale_values(graph, mean_scale_values)
convert_dilated_convolution(graph)
for_graph_and_each_sub_graph_recursively(graph, graph_clean_up_tf)
l2_norm_to_norm(graph)
graph_clean_up_tf(graph)
remove_op_nodes(graph, {'identity': True})
remove_useless_split(graph)
class_registration.apply_replacements(graph, class_registration.ClassType.MIDDLE_REPLACER)
mean_to_avgpool(graph)
convert_nasnet(graph)
fuse_pad(graph)
graph_clean_up_tf(graph)
convert_matmul_to_fully_connected(graph)
# Mark nodes with attr 'can_be_fused': False to disable fusing for specified nodes
for_graph_and_each_sub_graph_recursively(graph, lambda graph: mark_unfused_nodes(graph, argv.finegrain_fusing))
# Converting FusedBatchNorm layer to Mul->Add->Mul->Add sequence
# IE doesn't support BN with 4 inputs, so we have to split it to two ScaleShift
convert_batch_norm(graph)
graph_clean_up_tf(graph)
if not argv.disable_fusing:
# Converting ScaleShift layer to Mul->Add
for_graph_and_each_sub_graph_recursively(graph, convert_scale_shift_to_mul_add)
for_graph_and_each_sub_graph_recursively(graph, graph_clean_up_tf)
# Fusing the sequences of Mul/Add operations
for_graph_and_each_sub_graph_recursively(graph, fuse_mul_add_sequence)
for_graph_and_each_sub_graph_recursively(graph, graph_clean_up_tf)
# Fusing linear operation to Convolution
for_graph_and_each_sub_graph_recursively(graph, fuse_linear_ops)
for_graph_and_each_sub_graph_recursively(graph, graph_clean_up_tf)
if not argv.disable_gfusing:
grouped_convolutions_fusing(graph)
graph_clean_up_tf(graph)
if not argv.disable_fusing:
fuse_linear_ops(graph)
graph_clean_up_tf(graph)
# Converting Mul->Add to ScaleShift node
for_graph_and_each_sub_graph_recursively(graph, convert_muladd_to_scaleshift_or_power)
for_graph_and_each_sub_graph_recursively(graph, graph_clean_up_tf)
for_graph_and_each_sub_graph_recursively(graph, convert_mul_add_to_power)
# Need to eliminate dead nodes before doing update_fully_connected_shapes
# because update_fully_connected_shapes does partial inference and dead
# nodes will lead to sporadic failures.
for_graph_and_each_sub_graph_recursively(graph, graph_clean_up_tf)
for_graph_and_each_sub_graph_recursively(graph, update_fully_connected_shapes)
for_graph_and_each_sub_graph_recursively(graph, convert_mul_eltwise_to_leaky_relu)
graph_clean_up_tf(graph)
for_graph_and_each_sub_graph_recursively(graph, graph_clean_up_tf)
for_graph_and_each_sub_graph_recursively(graph, fuse_pad)
for_graph_and_each_sub_graph_recursively(graph, graph_clean_up_tf)
for_graph_and_each_sub_graph_recursively(graph, convert_reshape)
for_graph_and_each_sub_graph_recursively(graph, convert_squeeze)
for_graph_and_each_sub_graph_recursively(graph, convert_add_to_scaleshift) # scale = 1
for_graph_and_each_sub_graph_recursively(graph, convert_mul_to_scaleshift) # biases = 0
if argv.reverse_input_channels:
reverse_input_channels(graph)
if argv.move_to_preprocess:
move_scaleshift_to_preprocess(graph)
graph_clean_up_tf(graph)
for_graph_and_each_sub_graph_recursively(graph, fuse_sequence_of_reshapes)
pattern = EltwiseInputNormalize()
pattern.find_and_replace_pattern(graph)
conv_flatten_concat(graph)
for_graph_and_each_sub_graph_recursively(graph, apply_nhwc_to_nchw_permutation)
for_graph_and_each_sub_graph_recursively(graph, merge_nodes_permutations)
for_graph_and_each_sub_graph_recursively(graph, permute_data_nodes_attrs)
for_graph_and_each_sub_graph_recursively(graph, permute_op_nodes_attrs)
for_graph_and_each_sub_graph_recursively(graph, repack_fully_connected_weights_nhwc_to_nchw)
for_graph_and_each_sub_graph_recursively(graph, transpose_fully_connected_weights)
for_graph_and_each_sub_graph_recursively(graph, graph_clean_up_tf)
if argv.offload_unsupported_operations_to_tf:
unsupported_ops_to_offload_to_tf = find_unsupported_ops(graph)
if len(unsupported_ops_to_offload_to_tf) == 0:
log.info('All operations are supported! Exit from the loop.')
if not need_to_repeat_conversion(graph):
break
else:
print('After {} iteration there are {} unsupported ops'.format(cur_iteration + 1,
len(unsupported_ops_to_offload_to_tf)))
else:
if not need_to_repeat_conversion(graph):
break
graph = graph_copy
cur_iteration += 1
class_registration.apply_replacements(graph, class_registration.ClassType.BACK_REPLACER)
prepare_emit_ir(graph=graph, data_type=argv.data_type, output_dir=output_dir, output_model_name=output_model_name,
meta_info=meta_info)
return 0
def extract(cls, loop_node):
Loop.update_node_stat(loop_node, {})
loop_name = loop_node.soft_get('name', loop_node.id)
# check that required body and condition functions exist in the graph library
main_graph = loop_node.graph
body_graph_name = loop_node.pb.attr['body'].func.name
cond_graph_name = loop_node.pb.attr['cond'].func.name
assert 'library' in main_graph.graph, 'The graph does not contain a library that is required ' \
'by node with name "{}".'.format(loop_name)
library_graph = main_graph.graph['library']
assert body_graph_name in library_graph, 'The library does not contain a function with name "{}" ' \
'that is required by node ' \
'with name "{}".'.format(body_graph_name, loop_name)
body_graph_proto = library_graph[body_graph_name]
assert cond_graph_name in library_graph, 'The library does not contain a function with name "{}" ' \
'that is required by node ' \
'with name "{}".'.format(cond_graph_name, loop_name)
cond_graph_proto = library_graph[cond_graph_name]
body_graph = Graph()
# fill the body graph
for attr_key in main_graph.graph.keys():
if attr_key != 'library':
body_graph.graph[attr_key] = copy.deepcopy(main_graph.graph[attr_key])
else:
# it is sufficient to have a link to the library
body_graph.graph['library'] = main_graph.graph['library']
loop_node['body'] = body_graph
# create Parameter nodes for the body graph
body_parameters = []
body_parameter_names = []
for idx, pb_node in enumerate(body_graph_proto['input_arg']):
param_id = body_graph.unique_id(pb_node.name)
body_graph.add_node(param_id, name=param_id, kind='op', op='Parameter', pb=None, shape=None)
parameter_node = Node(body_graph, pb_node.name)
Parameter.update_node_stat(parameter_node,
{'data_type': tf_dtype_extractor(pb_node.type),
'permute_attrs': PermuteAttrs().update_attrs(attrs=[('shape', 'output:0')])}
)
body_parameters.append(parameter_node)
body_parameter_names.append(param_id)
# update the loop body graph with the body function graph
body_results = []
update_body_graph(body_graph, body_graph_proto, body_parameter_names, body_results)
# update the loop body graph with the condition function graph
update_body_graph(body_graph, cond_graph_proto, body_parameter_names, body_results)
# add 'internal_layer_id' attribute which is a must have attribute for the loop body node
for idx, body_node in enumerate(body_graph.get_op_nodes()):
body_node['internal_layer_id'] = idx
body_graph.stage = 'front'
# Currently,
# Loop Inputs Order:
# 0 - current iteration
# 1 - trip count
# 2.. - "loop carried" dependencies variables
#
# Body Inputs Order:
# 0 - current iteration
# 1 - trip count
# 2.. - "loop carried" dependencies variables
#
# Body Outputs Order:
# 0 - current iteration
# 1 - trip count
# 2.. - "loop carried" dependencies variables
#
# Loop Outputs Order:
# 0 - current iteration
# 1 - trip count
# 2.. - "loop carried" dependencies variables
#
# so inputs must be reordered and execution condition must be created in the front transformation
# to be aligned with the specification
# connect external input ports with body parameter nodes except current iteration
# since it must be disconnected from external port
for idx in range(1, len(body_parameters)):
Loop.connect_body_input(loop_node, idx, body_parameters[idx])
# mark current iteration input Parameter node and execution condition Result node
Loop.mark_current_iteration_parameter_node(loop_node, body_parameters[0])
Loop.mark_execution_condition_result_node(loop_node, body_results[-1])
# connect back edges in the body except current iteration
for idx in range(1, len(body_parameters)):
Loop.add_back_edge(loop_node, body_parameters[idx], body_results[idx])
# connect body outputs with Loop operation output ports except the execution condition result
for idx in range(len(body_results)-1):
Loop.connect_body_output(loop_node, idx, body_results[idx])
# run function to parse body nodes attributes similar to the main graph
extract_node_attrs(body_graph, lambda node: tf_op_extractor(node, check_for_duplicates(tf_op_extractors)))
return cls.enabled
def tf2nx(argv: argparse.Namespace, model_file_name: str,
output_model_name: str, output_dir: str, is_binary: bool):
"""
Convert TF GraphDef object to NetworkX representation.
The resulting graph is still TF-specific and needs normalization passes to be applied.
The specific TF structure assumes each GraphDef node is converted to a single
NetworkX node, node id is an original TF node name, and edges go directly from one op to another op.
"""
meta_info = get_meta_info(argv)
if argv.tensorflow_custom_layer_libraries:
libraries = argv.tensorflow_custom_layer_libraries.split(',')
for library in libraries:
log.info(
'Loading library "{}" with custom operations'.format(library))
tf.load_op_library(library)
graph_def, variables_values = load_tf_graph_def(
graph_file_name=model_file_name,
is_binary=is_binary,
checkpoint=argv.input_checkpoint,
user_output_node_names_list=argv.output,
model_dir=argv.saved_model_dir,
meta_graph_file=argv.input_meta_graph,
saved_model_tags=argv.saved_model_tags)
try:
tf.import_graph_def(graph_def, name='')
except:
log.warning(
"TensorFlow post-processing of loaded model was unsuccessful. "
"This is an optional step that Model Optimizer performs for any input model but it is not usually "
"required for all models."
"It likely means that the original model is ill-formed. "
"Model Optimizer will continue converting this model.")
log.debug("Number of nodes in graph_def: {}".format(len(graph_def.node))) # pylint: disable=no-member
if argv.tensorboard_logdir:
tensorboard.dump_for_tensorboard(graph_def, argv.tensorboard_logdir)
update_extractors_with_extensions(tf_op_extractors)
try:
graph = protobuf2nx(graph_def)
graph.__setattr__('name', output_model_name)
# 'layout' parameter change may cause an issue in EltwiseInputReshape replacer
# and convert_nhwc_to_nchw(graph)
graph.graph['layout'] = 'NCHW' if argv.disable_nhwc_to_nchw else 'NHWC'
graph.graph['cmd_params'] = argv
graph.graph['fw'] = 'tf'
if graph.graph['cmd_params'].generate_experimental_IR_V10:
version = 10
else:
version = 6
graph.graph[
'ir_version'] = 2 if argv.generate_deprecated_IR_V2 else version
graph.graph['variables_values'] = variables_values
del variables_values
graph = restore_edges(graph, get_tf_edges)
graph = remove_control_dependency_inputs(graph)
except Exception as e:
raise Error(
'Cannot pre-process TensorFlow graph after reading from model file "{}". ' \
'File is corrupt or has unsupported format. Details: {}. ' +
refer_to_faq_msg(44),
model_file_name,
str(e)
) from e
graph.check_empty_graph(
'protobuf2nx. It may happen due to problems with loaded model')
extract_node_attrs(
graph, lambda node: tf_op_extractor(
node, check_for_duplicates(tf_op_extractors)))
# --------------------------------- LOAD END ------------------------------------------------------
class_registration.apply_replacements(graph, [
class_registration.ClassType.FRONT_REPLACER,
class_registration.ClassType.MIDDLE_REPLACER,
class_registration.ClassType.BACK_REPLACER
])
prepare_emit_ir(graph=graph,
data_type=argv.data_type,
output_dir=output_dir,
output_model_name=output_model_name,
meta_info=meta_info)
return 0
def extract(cls, loop_node):
Loop.update_node_stat(loop_node, {})
# check that required body and condition functions exist in the graph library
main_graph = loop_node.graph
body_graph_proto = get_graph_proto(main_graph, 'body', loop_node)
cond_graph_proto = get_graph_proto(main_graph, 'cond', loop_node)
body_graph = create_internal_graph(main_graph)
loop_node['body'] = body_graph
# create Parameter nodes for the body graph
body_parameters, body_parameter_names = convert_graph_inputs_to_parameters(
body_graph, body_graph_proto)
# update the loop body graph with the body function graph
body_results = []
update_body_graph(body_graph, body_graph_proto, body_parameter_names,
body_results)
# update the loop body graph with the condition function graph
update_body_graph(body_graph, cond_graph_proto, body_parameter_names,
body_results)
# add 'internal_layer_id' attribute which is a must have attribute for the loop body node
for idx, body_node in enumerate(body_graph.get_op_nodes()):
body_node['internal_layer_id'] = idx
body_graph.stage = 'front'
# Currently,
# Loop Inputs Order:
# 0 - current iteration
# 1 - trip count
# 2.. - "loop carried" dependencies variables
#
# Body Inputs Order:
# 0 - current iteration
# 1 - trip count
# 2.. - "loop carried" dependencies variables
#
# Body Outputs Order:
# 0 - current iteration
# 1 - trip count
# 2.. - "loop carried" dependencies variables
#
# Loop Outputs Order:
# 0 - current iteration
# 1 - trip count
# 2.. - "loop carried" dependencies variables
#
# so inputs must be reordered and execution condition must be created in the front transformation
# to be aligned with the specification
# connect external input ports with body parameter nodes except current iteration
# since it must be disconnected from external port
for idx in range(1, len(body_parameters)):
Loop.connect_body_input(loop_node, idx, body_parameters[idx])
# mark current iteration input Parameter node and execution condition Result node
Loop.mark_current_iteration_parameter_node(loop_node,
body_parameters[0])
Loop.mark_execution_condition_result_node(loop_node, body_results[-1])
# connect back edges in the body except current iteration
for idx in range(1, len(body_parameters)):
Loop.add_back_edge(loop_node, body_parameters[idx],
body_results[idx])
# connect body outputs with Loop operation output ports except the execution condition result
for idx in range(len(body_results) - 1):
Loop.connect_body_output(loop_node, idx, body_results[idx])
# run function to parse body nodes attributes similar to the main graph
extract_node_attrs(
body_graph, lambda node: tf_op_extractor(
node, check_for_duplicates(tf_op_extractors)))
return cls.enabled
def load(self, graph: Graph):
argv = graph.graph['cmd_params']
if argv.tensorflow_custom_layer_libraries:
libraries = argv.tensorflow_custom_layer_libraries.split(',')
for library in libraries:
log.info('Loading library "{}" with custom operations'.format(
library))
tf_v1.load_op_library(library)
graph_def, variables_values, framework = load_tf_graph_def(
graph_file_name=argv.input_model,
is_binary=not argv.input_model_is_text,
checkpoint=argv.input_checkpoint,
user_output_node_names_list=argv.output,
model_dir=argv.saved_model_dir,
meta_graph_file=argv.input_meta_graph,
saved_model_tags=argv.saved_model_tags)
send_framework_info(framework)
try:
tf_v1.import_graph_def(graph_def, name='')
except:
log.warning(
"TensorFlow post-processing of loaded model was unsuccessful. "
"This is an optional step that Model Optimizer performs for any input model but it is not usually "
"required for all models. "
"It likely means that the original model is ill-formed. "
"Model Optimizer will continue converting this model.")
log.debug("Number of nodes in graph_def: {}".format(len(
graph_def.node))) # pylint: disable=no-member
if argv.tensorboard_logdir:
tensorboard_util.dump_for_tensorboard(graph_def,
argv.tensorboard_logdir)
update_extractors_with_extensions(tf_op_extractors)
try:
protobuf2nx(graph, graph_def)
except Exception as e:
raise Error(
'Cannot pre-process TensorFlow graph after reading from model file "{}". ' \
'File is corrupt or has unsupported format. Details: {}. ' +
refer_to_faq_msg(44),
argv.model_name,
str(e)
) from e
graph.__setattr__('name', argv.model_name)
# 'layout' parameter change may cause an issue in EltwiseInputReshape replacer
# and convert_nhwc_to_nchw(graph)
graph.graph['layout'] = 'NCHW' if argv.disable_nhwc_to_nchw else 'NHWC'
graph.graph['fw'] = 'tf'
graph.graph['variables_values'] = variables_values
del variables_values
used_tensors = restore_edges(graph, get_tf_edges)
# Tensor names information corresponding to a node is stored on outgoing edges.
# As output nodes do not have outgoing edges, fake outputs are required. In the following code
# for each output Identity node is added, and tensor name for the output is kept
# on (output, fake output) edge. After Result nodes adding transformation fake outputs
# are deleted from graph.
add_outputs_identity(
graph, graph.nodes - used_tensors,
lambda g, output, fake_node_name: g.add_edges_from(
[create_tf_edge(output, fake_node_name, 0)]))
remove_control_dependency_inputs(graph)
graph.check_empty_graph(
'protobuf2nx. It may happen due to problems with loaded model')
extract_node_attrs(
graph, lambda node: tf_op_extractor(
node, check_for_duplicates(tf_op_extractors)))
# try to detect layout from the nodes of the graph. If there are no convolution nodes in N(D)HWC layout then we
# consider that the graph is in NCHW layout and no layout conversion should be performed
if not argv.disable_nhwc_to_nchw and not argv.silent and not graph_or_sub_graph_has_nhwc_ops(
graph):
log.error(
'The TensorFlow model does not contain Convolution operations with N(D)HWC layout. Most likely '
'the model should be converted using additional "--disable_nhwc_to_nchw" command line parameter '
'which disables model layout conversion inside the Model Optimizer.',
extra={'is_warning': True})
send_op_names_info(framework, graph)
send_shapes_info(framework, graph)
def tf2nx(argv: argparse.Namespace, model_file_name: str, output_model_name: str, output_dir: str,
is_binary: bool):
"""
Convert TF GraphDef object to NetworkX representation.
The resulting graph is still TF-specific and needs normalization passes to be applied.
The specific TF structure assumes each GraphDef node is converted to a single
NetworkX node, node id is an original TF node name, and edges go directly from one op to another op.
"""
meta_info = get_meta_info(argv)
if argv.tensorflow_custom_layer_libraries:
libraries = argv.tensorflow_custom_layer_libraries.split(',')
for library in libraries:
log.info('Loading library "{}" with custom operations'.format(library))
tf.load_op_library(library)
graph_def, variables_values = load_tf_graph_def(graph_file_name=model_file_name, is_binary=is_binary,
checkpoint=argv.input_checkpoint,
user_output_node_names_list=argv.output,
model_dir=argv.saved_model_dir,
meta_graph_file=argv.input_meta_graph,
saved_model_tags=argv.saved_model_tags)
try:
tf.import_graph_def(graph_def, name='')
except:
log.warning("TensorFlow post-processing of loaded model was unsuccessful. "
"This is an optional step that Model Optimizer performs for any input model but it is not usually "
"required for all models."
"It likely means that the original model is ill-formed. "
"Model Optimizer will continue converting this model.")
log.debug("Number of nodes in graph_def: {}".format(len(graph_def.node))) # pylint: disable=no-member
if argv.tensorboard_logdir:
tensorboard.dump_for_tensorboard(graph_def, argv.tensorboard_logdir)
update_extractors_with_extensions(tf_op_extractors)
try:
graph = protobuf2nx(graph_def)
graph.__setattr__('name', output_model_name)
# 'layout' parameter change may cause an issue in EltwiseInputReshape replacer
# and convert_nhwc_to_nchw(graph)
graph.graph['layout'] = 'NCHW' if argv.disable_nhwc_to_nchw else 'NHWC'
graph.graph['cmd_params'] = argv
graph.graph['fw'] = 'tf'
graph.graph['ir_version'] = 2 if argv.generate_deprecated_IR_V2 else 5
graph.graph['variables_values'] = variables_values
del variables_values
graph = restore_edges(graph, get_tf_edges)
graph = remove_control_dependency_inputs(graph)
except Exception as e:
raise Error(
'Cannot pre-process TensorFlow graph after reading from model file "{}". ' \
'File is corrupt or has unsupported format. Details: {}. ' +
refer_to_faq_msg(44),
model_file_name,
str(e)
) from e
graph.check_empty_graph('protobuf2nx. It may happen due to problems with loaded model')
extract_node_attrs(graph, lambda node: tf_op_extractor(node, check_for_duplicates(tf_op_extractors)))
# --------------------------------- LOAD END ------------------------------------------------------
class_registration.apply_replacements(graph, class_registration.ClassType.FRONT_REPLACER)
class_registration.apply_replacements(graph, class_registration.ClassType.MIDDLE_REPLACER)
fuse_pad(graph)
graph_clean_up_tf(graph)
convert_matmul_to_fully_connected(graph)
# Mark nodes with attr 'can_be_fused': False to disable fusing for specified nodes
for_graph_and_each_sub_graph_recursively(graph, lambda graph: mark_unfused_nodes(graph, argv.finegrain_fusing))
# Converting FusedBatchNorm layer to Mul->Add->Mul->Add sequence
# IE doesn't support BN with 4 inputs, so we have to split it to two ScaleShift
convert_batch_norm(graph)
graph_clean_up_tf(graph)
if not argv.disable_fusing:
# Converting ScaleShift layer to Mul->Add
for_graph_and_each_sub_graph_recursively(graph, convert_scale_shift_to_mul_add)
for_graph_and_each_sub_graph_recursively(graph, graph_clean_up_tf)
# Fusing the sequences of Mul/Add operations
for_graph_and_each_sub_graph_recursively(graph, fuse_mul_add_sequence)
for_graph_and_each_sub_graph_recursively(graph, graph_clean_up_tf)
# Fusing linear operation to Convolution
for_graph_and_each_sub_graph_recursively(graph, fuse_linear_ops)
for_graph_and_each_sub_graph_recursively(graph, graph_clean_up_tf)
if not argv.disable_gfusing:
grouped_convolutions_fusing(graph)
graph_clean_up_tf(graph)
if not argv.disable_fusing:
fuse_linear_ops(graph)
graph_clean_up_tf(graph)
# Converting Mul->Add to ScaleShift node
for_graph_and_each_sub_graph_recursively(graph, convert_muladd_to_scaleshift_or_power)
for_graph_and_each_sub_graph_recursively(graph, graph_clean_up_tf)
for_graph_and_each_sub_graph_recursively(graph, convert_mul_add_to_power)
# Need to eliminate dead nodes before doing update_fully_connected_shapes
# because update_fully_connected_shapes does partial inference and dead
# nodes will lead to sporadic failures.
for_graph_and_each_sub_graph_recursively(graph, graph_clean_up_tf)
for_graph_and_each_sub_graph_recursively(graph, update_fully_connected_shapes)
for_graph_and_each_sub_graph_recursively(graph, convert_mul_eltwise_to_leaky_relu)
graph_clean_up_tf(graph)
for_graph_and_each_sub_graph_recursively(graph, graph_clean_up_tf)
for_graph_and_each_sub_graph_recursively(graph, fuse_pad)
for_graph_and_each_sub_graph_recursively(graph, graph_clean_up_tf)
for_graph_and_each_sub_graph_recursively(graph, convert_reshape)
for_graph_and_each_sub_graph_recursively(graph, convert_squeeze)
for_graph_and_each_sub_graph_recursively(graph, graph_clean_up_tf)
for_graph_and_each_sub_graph_recursively(graph, convert_add_or_mul_to_scaleshift) # scale = 1
for_graph_and_each_sub_graph_recursively(graph, graph_clean_up_tf)
if argv.reverse_input_channels:
reverse_input_channels(graph)
if argv.move_to_preprocess:
move_scaleshift_to_preprocess(graph)
graph_clean_up_tf(graph)
fuse_sequence_of_reshapes(graph)
pattern = EltwiseInputNormalize()
pattern.find_and_replace_pattern(graph)
conv_flatten_concat(graph)
if argv.enable_concat_optimization:
ConcatOptimization().find_and_replace_pattern(graph)
LayoutChangeForConstantShapePaths().find_and_replace_pattern(graph)
for_graph_and_each_sub_graph_recursively(graph, graph_clean_up_tf)
for_graph_and_each_sub_graph_recursively(graph, apply_nhwc_to_nchw_permutation)
for_graph_and_each_sub_graph_recursively(graph, merge_nodes_permutations)
for_graph_and_each_sub_graph_recursively(graph, permute_data_nodes_attrs)
for_graph_and_each_sub_graph_recursively(graph, permute_op_nodes_attrs)
for_graph_and_each_sub_graph_recursively(graph, repack_fully_connected_weights_nhwc_to_nchw)
for_graph_and_each_sub_graph_recursively(graph, transpose_fully_connected_weights)
for_graph_and_each_sub_graph_recursively(graph, graph_clean_up_tf)
class_registration.apply_replacements(graph, class_registration.ClassType.BACK_REPLACER)
for_graph_and_each_sub_graph_recursively(graph, remove_const_ops)
CreateConstNodesReplacement().find_and_replace_pattern(graph)
for_graph_and_each_sub_graph_recursively(graph, remove_output_ops)
prepare_emit_ir(graph=graph, data_type=argv.data_type, output_dir=output_dir, output_model_name=output_model_name,
meta_info=meta_info)
return 0