def test_remove_out_data_for_memory(self):
graph = build_graph(self.nodes, [('input_node', 'memory_node'),
('memory_node', 'output_node'),
('output_node', 'op_output')])
KaldiRemoveMemoryOutputBackReplacementPattern(
).find_and_replace_pattern(graph)
self.assertNotIn('output_node', graph.node)
def test_do_not_remove_out_data_for_memory(self):
graph = build_graph(self.nodes, [('input_node', 'memory_node')])
graph.add_node('output_node', **self.nodes['output_node'])
graph.add_edge('memory_node', 'output_node', out=0)
KaldiRemoveMemoryOutputBackReplacementPattern(
).find_and_replace_pattern(graph)
self.assertIn('output_node', graph.node)
def test_remove_out_data_for_memory(self):
graph = build_graph(self.nodes, [('input_node', 'memory_node')])
# Need for matching in pattern. The edge memory_node->out_node must contain only the attribute 'out' = 0
# build_graph creates edge memory_node->out_node with attributes 'in' and 'out'
graph.add_node('output_node',
is_output=True,
**self.nodes['output_node'])
graph.add_edge('memory_node', 'output_node', out=0)
KaldiRemoveMemoryOutputBackReplacementPattern(
).find_and_replace_pattern(graph)
self.assertNotIn('output_node', graph.node)
def driver(argv, input_model, output_model_name, output_dir):
meta_info = get_meta_info(argv)
EltwiseChecker.enabled = False
try:
graph, input_shapes = load_kaldi_model(input_model)
except Exception as e:
raise Error('Model Optimizer is not able to read Kaldi model {}. '.format(input_model) +
refer_to_faq_msg(91)) from e
graph.check_empty_graph('load_kaldi_nnet_model')
graph.graph['cmd_params'] = argv
graph.graph['fw'] = 'kaldi'
graph.graph['ir_version'] = 2 if argv.generate_deprecated_IR_V2 else 5
update_extractors_with_extensions(kaldi_type_extractors)
extract_node_attrs(graph, lambda node: kaldi_extractor(node))
# --------------------------------- LOAD END ------------------------------------------------------
class_registration.apply_replacements(graph, class_registration.ClassType.FRONT_REPLACER)
graph = partial_infer(graph)
# The order is intentional, firstly eliminate repeated, then remove redundant
FuseRepeatedReshapes().find_and_replace_pattern(graph)
EliminateRedundantReshape().find_and_replace_pattern(graph)
graph.check_empty_graph('partial_infer')
if argv.counts:
try:
counts = read_counts_file(argv.counts)
except Exception as e:
raise Error('Model Optimizer is not able to read counts file {}'.format(argv.counts) +
refer_to_faq_msg(92)) from e
apply_biases_to_last_layer(graph, counts)
if argv.remove_output_softmax:
RemoveLastSoftMaxPattern().find_and_replace_pattern(graph)
graph_clean_up(graph)
log.debug("After removing softmax")
graph.print_graph_stat()
# Intentionally after all transformations
KaldiRemoveMemoryOutputBackReplacementPattern().find_and_replace_pattern(graph)
remove_const_ops(graph)
CreateConstNodesReplacement().find_and_replace_pattern(graph)
remove_output_ops(graph)
prepare_emit_ir(graph, argv.data_type, output_dir, output_model_name, meta_info=meta_info)
return 0
def driver(argv, input_model, output_model_name, output_dir):
log_step(argv.steps, 'LOAD')
meta_info = get_meta_info(argv)
EltwiseChecker.enabled = False
try:
graph = load_kaldi_model(input_model)
except Exception as e:
raise Error('Model Optimizer is not able to parse Kaldi model {}. '.format(input_model) +
refer_to_faq_msg(91)) from e
graph.check_empty_graph('load_kaldi_nnet_model')
graph.graph['cmd_params'] = argv
graph.graph['fw'] = 'kaldi'
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
update_extractors_with_extensions(kaldi_type_extractors)
extract_node_attrs(graph, lambda node: kaldi_extractor(node))
# --------------------------------- LOAD END ------------------------------------------------------
log_step(argv.steps, 'FRONT')
ReplaceLSTMNodePattern().find_and_replace_pattern(graph)
class_registration.apply_replacements(graph, class_registration.ClassType.FRONT_REPLACER)
log_step(argv.steps, 'MIDDLE')
graph = partial_infer(graph)
ReplacePNormNodePattern().find_and_replace_pattern(graph)
ReplaceMemoryOffsetNodePattern().find_and_replace_pattern(graph)
ReplaceMemoryOffsetWithMemoryNodePattern().find_and_replace_pattern(graph)
RemoveMemoryDuplicationPattern().find_and_replace_pattern(graph)
MergeNeighborSplicePattern().find_and_replace_pattern(graph)
RemoveUselessCropsPattern().find_and_replace_pattern(graph)
RemoveIdentity().find_and_replace_pattern(graph)
graph_clean_up(graph)
AddSelectBeforeMemoryNodePattern().find_and_replace_pattern(graph)
ReplaceSpliceNodePattern().find_and_replace_pattern(graph)
graph_clean_up(graph)
# The order is intentional, firstly eliminate repeated, then remove redundant
FuseRepeatedReshapes().find_and_replace_pattern(graph)
EliminateRedundantReshape().find_and_replace_pattern(graph)
graph_clean_up(graph)
graph.check_empty_graph('partial_infer')
if argv.counts:
try:
counts = read_counts_file(argv.counts)
except Exception as e:
raise Error('Model Optimizer is not able to read counts file {}'.format(argv.counts) +
refer_to_faq_msg(92)) from e
apply_biases_to_last_layer(graph, counts)
if argv.remove_output_softmax:
RemoveLastSoftMaxPattern().find_and_replace_pattern(graph)
graph_clean_up(graph)
log.debug("After removing softmax")
graph.print_graph_stat()
log_step(argv.steps, 'BACK')
LeakyReluToReluWithNegativeSlope().find_and_replace_pattern(graph)
TransposeToPermute().find_and_replace_pattern(graph)
DivideToEltwises().find_and_replace_pattern(graph)
SubtractToEltwises().find_and_replace_pattern(graph)
SimpleEltwiseToEltwiseOp().find_and_replace_pattern(graph)
for_graph_and_each_sub_graph_recursively(graph, convert_matmul_to_fully_connected)
# Intentionally after all transformations
if argv.remove_memory:
CutMemory().find_and_replace_pattern(graph)
graph_clean_up(graph)
ParameterToInput().find_and_replace_pattern(graph)
KaldiRemoveMemoryOutputBackReplacementPattern().find_and_replace_pattern(graph)
ForceStrictPrecision().find_and_replace_pattern(graph)
remove_const_ops(graph)
CreateConstNodesReplacement().find_and_replace_pattern(graph)
remove_output_ops(graph)
log_step(argv.steps, 'EMIT')
prepare_emit_ir(graph, argv.data_type, output_dir, output_model_name, meta_info=meta_info)
return 0
def driver(argv,
input_model,
output_model_name,
outputs,
output_dir,
scale,
placeholder_shapes=None,
mean_scale_values=()):
meta_info = get_meta_info(argv)
EltwiseChecker.enabled = False
try:
graph, input_shapes = load_kaldi_model(input_model)
except Exception as e:
raise Error('Model Optimizer is not able to read Kaldi model {}. '.
format(input_model) + refer_to_faq_msg(91)) from e
check_empty_graph(graph, 'load_kaldi_nnet_model')
graph.graph['cmd_params'] = argv
graph.graph['fw'] = 'kaldi'
graph.graph['ir_version'] = 2 if argv.generate_deprecated_IR_V2 else 4
update_extractors_with_extensions(kaldi_type_extractors)
extract_node_attrs(graph, lambda node: kaldi_extractor(node))
class_registration.apply_replacements(
graph, class_registration.ClassType.FRONT_REPLACER)
output_op_nodes = add_output_ops(
graph, outputs) # TODO pass real outputs instead of None
log.debug("After adding specific nodes for outputs")
print_graph_stat(graph)
check_empty_graph(graph, 'add_output_ops')
create_tensor_nodes(graph)
graph_clean_up(graph)
log.debug("After removing specific nodes for output")
print_graph_stat(graph)
override_placeholder_shapes(graph, placeholder_shapes)
override_batch(graph, argv.batch)
graph_clean_up(graph)
log.debug("After setting input shapes")
print_graph_stat(graph)
graph_clean_up(graph)
remove_output_ops(graph)
log.debug("After removing specific nodes for output")
print_graph_stat(graph)
# You need to pass required network outputs here
# but we don't have a way yet, so just passing all discovered sinks
mark_outputs(graph)
graph_clean_up(graph)
log.debug("After graph_cleanup")
print_graph_stat(graph)
graph = partial_infer(graph)
# The order is intentional, firstly eliminate repeated, then remove redundant
FuseRepeatedReshapes().find_and_replace_pattern(graph)
EliminateRedundantReshape().find_and_replace_pattern(graph)
check_empty_graph(graph, 'partial_infer')
if argv.counts:
try:
counts = read_counts_file(argv.counts)
except Exception as e:
raise Error('Model Optimizer is not able to read counts file {}'.
format(argv.counts) + refer_to_faq_msg(92)) from e
apply_biases_to_last_layer(graph, counts)
if argv.remove_output_softmax:
RemoveLastSoftMaxPattern().find_and_replace_pattern(graph)
graph_clean_up(graph)
log.debug("After removing softmax")
print_graph_stat(graph)
# Intentionally after all transformations
KaldiRemoveMemoryOutputBackReplacementPattern().find_and_replace_pattern(
graph)
prepare_emit_ir(graph,
argv.data_type,
output_dir,
output_model_name,
meta_info=meta_info)
return 0
