def find_and_replace_pattern(self, graph: Graph):
for_graph_and_each_sub_graph_recursively(graph, lambda G: G.clean_up())
EltwiseChecker().find_and_replace_pattern(graph)
for_graph_and_each_sub_graph_recursively(graph, convert_muladd_to_scaleshift)
for_graph_and_each_sub_graph_recursively(graph, lambda G: G.clean_up())
for_graph_and_each_sub_graph_recursively(graph, convert_add_or_mul_to_scaleshift)
for_graph_and_each_sub_graph_recursively(graph, lambda G: G.clean_up())
def find_and_replace_pattern(self, graph: Graph):
for_graph_and_each_sub_graph_recursively(graph, lambda G: G.clean_up())
EltwiseChecker().find_and_replace_pattern(graph)
# turn off run of transformation for body of TI due to error on TF_Multicell topology
convert_muladd_to_scaleshift(graph)
graph.clean_up()
# turn off run of transformation for body of TI due to error on TF_Multicell topology
convert_add_or_mul_to_scaleshift(graph)
graph.clean_up()
def find_and_replace_pattern(self, graph: Graph):
# to prevent fusing of non per channel lin ops, we run EltwiseChecker to mark nodes with can_be_fused attribute
EltwiseChecker().find_and_replace_pattern(graph)
eltwise_nodes = graph.get_op_nodes(op='Mul', can_be_fused=True) + \
graph.get_op_nodes(op='Sub', can_be_fused=True) + \
graph.get_op_nodes(op='Add', can_be_fused=True)
for elt in eltwise_nodes:
if elt.in_port(0).data.get_value() is not None or elt.in_port(
1).data.get_value() is not None:
elt['fuse_up_to_quantize_ports'] = [3, 4]
slice = graph.get_op_nodes(op='Slice')
for sl in slice:
sl['fuse_up_to_quantize_ports'] = [0]
def mark_fusable_muls_on_weights(graph):
for node in graph.get_op_nodes(op='Mul'):
children = node.out_port(0).get_destinations()
if len(children) > 1 or children[0].node.soft_get('type') not in [
'Convolution', 'Deconvolution', 'MatMul'
]:
continue
value_in_port = get_value_in_port(node)
if value_in_port is None:
continue
value_shape = value_in_port.data.get_shape()
non_one_axis = np.argwhere(value_shape != 1)
if non_one_axis.size != 1:
continue
non_one_axis = non_one_axis.item(0)
node['can_be_fused'] = True
EltwiseChecker().mark_eltwise_node(node, non_one_axis)
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.
"""
log_step(argv.steps, 'LOAD')
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 ------------------------------------------------------
log_step(argv.steps, 'FRONT')
class_registration.apply_replacements(graph, class_registration.ClassType.FRONT_REPLACER)
log_step(argv.steps, 'MIDDLE')
class_registration.apply_replacements(graph, class_registration.ClassType.MIDDLE_REPLACER)
fuse_pad(graph)
graph_clean_up_tf(graph)
for_graph_and_each_sub_graph_recursively(graph, convert_matmul_to_fully_connected)
# 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:
for_graph_and_each_sub_graph_recursively(graph, grouped_convolutions_fusing)
graph_clean_up_tf(graph)
if not argv.disable_fusing:
fuse_linear_ops(graph)
graph_clean_up_tf(graph)
for_graph_and_each_sub_graph_recursively(graph, EltwiseChecker().find_and_replace_pattern)
# Converting Mul->Add to ScaleShift node
for_graph_and_each_sub_graph_recursively(graph, convert_muladd_to_scaleshift)
# 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, 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:
for_graph_and_each_sub_graph_recursively(graph, move_scaleshift_to_preprocess)
graph_clean_up_tf(graph)
FuseReshapesSequence().find_and_replace_pattern(graph)
RemoveRedundantReshapes().find_and_replace_pattern(graph)
EltwiseInputNormalize().find_and_replace_pattern(graph)
if argv.enable_concat_optimization:
ConcatOptimization().find_and_replace_pattern(graph)
for_graph_and_each_sub_graph_recursively(graph, InsertLayoutPropagationTranspose().find_and_replace_pattern)
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, permute_input_data)
for_graph_and_each_sub_graph_recursively(graph, graph_clean_up_tf)
graph.graph['layout'] = 'NCHW'
log_step(argv.steps, 'BACK')
class_registration.apply_replacements(graph, class_registration.ClassType.BACK_REPLACER)
for_graph_and_each_sub_graph_recursively(graph, graph_clean_up_tf)
for_graph_and_each_sub_graph_recursively(graph, remove_const_ops)
for_graph_and_each_sub_graph_recursively(graph, CreateConstNodesReplacement().find_and_replace_pattern)
for_graph_and_each_sub_graph_recursively(graph, remove_output_ops)
log_step(argv.steps, 'EMIT')
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