def test_insert_select_0(self):
graph = build_graph(
{
'in_node': {
'kind': 'data',
'shape': [1, 13]
},
'placeholder_1': {
'kind': 'op',
'op': None
},
'placeholder_data_1': {
'kind': 'data',
'shape': [1, 13]
},
'memory': {
'kind': 'op',
'op': 'Assign'
},
}, [('in_node', 'placeholder_1'),
('placeholder_1', 'placeholder_data_1'),
('placeholder_data_1', 'memory')],
nodes_with_edges_only=True)
ref_graph = graph.copy()
AddSelectBeforeMemoryNodePattern().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, ref_graph, 'memory')
self.assertTrue(flag, resp)
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 test_insert_select_1(self):
graph = build_graph({'in_node': {'kind': 'data', 'shape': [1, 13]},
'placeholder_1': {'kind': 'op', 'op': None},
'placeholder_data_1': {'kind': 'data', 'shape': [1, 13]},
'splice_1': {'kind': 'op', 'op': 'Splice', 'context': np.array([-2, -1, 0, 1, 2])},
'splice_data_1': {'kind': 'data', 'shape': [1, 13]},
'placeholder_2': {'kind': 'op', 'op': None},
'placeholder_data_2': {'kind': 'data', 'shape': [1, 26]},
'memory': {'kind': 'op', 'op': 'Assign', 'index': 0},
},
[('in_node', 'placeholder_1'), ('placeholder_1', 'placeholder_data_1'),
('placeholder_data_1', 'splice_1'), ('splice_1', 'splice_data_1'),
('splice_data_1', 'placeholder_2'), ('placeholder_2', 'placeholder_data_2'),
('placeholder_data_2', 'memory')
],
nodes_with_edges_only=True)
AddSelectBeforeMemoryNodePattern().find_and_replace_pattern(graph)
ref_graph = build_graph({'in_node': {'kind': 'data', 'shape': [1, 13]},
'placeholder_1': {'kind': 'op', 'op': None},
'placeholder_data_1': {'kind': 'data', 'shape': [1, 13]},
'splice_1': {'kind': 'op', 'op': 'Splice', 'context': np.array([-2, -1, 0, 1, 2])},
'splice_data_1': {'kind': 'data', 'shape': [1, 13]},
'placeholder_2': {'kind': 'op', 'op': None},
'shape': {'kind': 'op', 'op': 'ShapeOf'},
'shape_data': {'kind': 'data'},
'crop_batch': {'kind': 'op', 'op': 'Crop', 'offset': int64_array([0])},
'crop_batch_data': {'kind': 'data'},
'crop_batch_dim':{'kind': 'op', 'op': 'Const', 'value': int64_array([1])},
'crop_batch_dim_data': {'kind': 'data'},
'second_dim': {'kind': 'op', 'op': 'Const', 'value': int64_array([5])},
'second_dim_data': {'kind': 'data'},
'gather_shape': {'kind': 'op', 'op': 'Concat'},
'gather_shape_data': {'kind': 'data'},
'fill_value': {'kind': 'op', 'op': 'Const', 'value': int64_array([0])},
'fill_value_data': {'kind': 'data'},
'broadcast': {'kind': 'op', 'op': 'Broadcast'},
'broadcast_data': {'kind': 'data'},
'memory_in': {'kind': 'op', 'op': 'ReadValue', 'shape': int64_array([5])},
'memory_in_data': {'kind': 'data'},
'memory_out': {'kind': 'op', 'op': 'Assign', 'shape': int64_array([5])},
'memory_out_data': {'kind': 'data'},
'result': {'kind': 'op', 'op': 'Result'},
'crop_in': {'kind': 'op', 'op': 'Crop', 'axis': 1, 'offset': 1, 'dim': 4},
'crop_in_data': {'kind': 'data'},
'crop_out': {'kind': 'op', 'op': 'Crop', 'axis': 1, 'offset': 0, 'dim': 1},
'crop_out_data': {'kind': 'data'},
'equal': {'kind': 'op', 'op': 'Equal'},
'equal_data': {'kind': 'data'},
'select': {'kind': 'op', 'op': 'Select'},
'select_out_data': {'kind': 'data', 'shape': [1, 26]},
'const_0': {'kind': 'op', 'op': 'Const'},
'const_0_data': {'kind': 'data'},
'const_1': {'kind': 'op', 'op': 'Const'},
'const_1_data': {'kind': 'data'},
'concat': {'kind': 'op', 'op': 'Concat'},
'concat_data': {'kind': 'data'},
'placeholder_data_2': {'kind': 'data', 'shape': [1, 26]},
'memory': {'kind': 'op', 'op': 'Assign'},
},
[('in_node', 'placeholder_1'), ('placeholder_1', 'placeholder_data_1'),
('placeholder_data_1', 'splice_1'), ('splice_1', 'splice_data_1'),
('splice_data_1', 'placeholder_2'), ('placeholder_2', 'placeholder_data_2'),
('placeholder_data_2', 'select', {'in': 1}),
('placeholder_data_2', 'shape'), ('shape', 'shape_data'),
('shape_data', 'crop_batch'), ('crop_batch', 'crop_batch_data'),
('crop_batch_dim', 'crop_batch_dim_data'),
('crop_batch_dim_data', 'crop_batch', {'in': 1}),
('second_dim', 'second_dim_data'), ('second_dim_data', 'gather_shape', {'in': 1}),
('crop_batch_data', 'gather_shape', {'in': 0}), ('gather_shape', 'gather_shape_data'),
('fill_value', 'fill_value_data'), ('fill_value_data', 'broadcast', {'in': 0}),
('gather_shape_data', 'broadcast', {'in': 1}), ('broadcast', 'broadcast_data'),
('broadcast_data', 'memory_in'),
('memory_in', 'memory_in_data'), ('memory_in_data', 'crop_in'),
('crop_in', 'crop_in_data'), ('crop_in_data', 'concat', {'in': 0}),
('const_1', 'const_1_data'), ('const_1_data', 'concat', {'in': 1}),
('concat', 'concat_data'), ('concat_data', 'memory_out'),
('memory_out', 'memory_out_data'), ('memory_out_data', 'result'),
('concat_data', 'crop_out'), ('crop_out', 'crop_out_data'),
('crop_out_data', 'equal', {'in': 1}), ('const_1_data', 'equal', {'in': 0}),
('equal', 'equal_data'),
('equal_data', 'select', {'in': 0}),
('const_0', 'const_0_data'), ('const_0_data', 'select', {'in': 2}),
('select', 'select_out_data'),
('select_out_data', 'memory')
],
nodes_with_edges_only=True
)
(flag, resp) = compare_graphs(graph, ref_graph, 'memory')
self.assertTrue(flag, resp)
def test_insert_select_3(self):
graph = build_graph({'in_node': {'kind': 'data', 'shape': [1, 13]},
'placeholder_1': {'kind': 'op', 'op': None},
'placeholder_data_1': {'kind': 'data', 'shape': [1, 13]},
'splice_1': {'kind': 'op', 'op': 'Splice', 'context': np.array([-2, -1, 0, 1, 2])},
'splice_data_1': {'kind': 'data', 'shape': [1, 65]},
'splice_2': {'kind': 'op', 'op': 'Splice', 'context': np.array([-1, 0, 1])},
'splice_data_2': {'kind': 'data', 'shape': [1, 39]},
'placeholder_2': {'kind': 'op', 'op': None},
'placeholder_data_2': {'kind': 'data', 'shape': [1, 26]},
'memory': {'kind': 'op', 'op': 'Memory', 'index': 0},
},
[('in_node', 'placeholder_1'), ('placeholder_1', 'placeholder_data_1'),
('placeholder_data_1', 'splice_1'), ('splice_1', 'splice_data_1'),
('splice_data_1', 'splice_2'), ('splice_2', 'splice_data_2'),
('splice_data_2', 'placeholder_2'), ('placeholder_2', 'placeholder_data_2'),
('placeholder_data_2', 'memory')
],
nodes_with_edges_only=True)
AddSelectBeforeMemoryNodePattern().find_and_replace_pattern(graph)
ref_graph = build_graph({'in_node': {'kind': 'data', 'shape': [1, 13]},
'placeholder_1': {'kind': 'op', 'op': None},
'placeholder_data_1': {'kind': 'data', 'shape': [1, 13]},
'splice_1': {'kind': 'op', 'op': 'Splice', 'context': np.array([-2, -1, 0, 1, 2])},
'splice_data_1': {'kind': 'data', 'shape': [1, 65]},
'splice_2': {'kind': 'op', 'op': 'Splice', 'context': np.array([-1, 0, 1])},
'splice_data_2': {'kind': 'data', 'shape': [1, 39]},
'placeholder_2': {'kind': 'op', 'op': None},
'memory_in': {'kind': 'op', 'op': 'Memory', 'shape': int64_array([7])},
'memory_in_data': {'kind': 'data'},
'memory_out': {'kind': 'op', 'op': 'Memory', 'shape': int64_array([7])},
'memory_out_data': {'kind': 'data'},
'result': {'kind': 'op', 'op': 'Result'},
'crop_in': {'kind': 'op', 'op': 'Crop', 'axis': 1, 'offset': 1, 'dim': 6},
'crop_in_data': {'kind': 'data'},
'crop_out': {'kind': 'op', 'op': 'Crop', 'axis': 1, 'offset': 0, 'dim': 1},
'crop_out_data': {'kind': 'data'},
'select': {'kind': 'op', 'op': 'Select'},
'select_out_data': {'kind': 'data', 'shape': [1, 26]},
'const_0': {'kind': 'op', 'op': 'Const'},
'const_0_data': {'kind': 'data'},
'const_1': {'kind': 'op', 'op': 'Const'},
'const_1_data': {'kind': 'data'},
'concat': {'kind': 'op', 'op': 'Concat'},
'concat_data': {'kind': 'data'},
'placeholder_data_2': {'kind': 'data', 'shape': [1, 26]},
'memory': {'kind': 'op', 'op': 'Memory', 'index': 0},
},
[('in_node', 'placeholder_1'), ('placeholder_1', 'placeholder_data_1'),
('placeholder_data_1', 'splice_1'), ('splice_1', 'splice_data_1'),
('splice_data_1', 'splice_2'), ('splice_2', 'splice_data_2'),
('splice_data_2', 'placeholder_2'), ('placeholder_2', 'placeholder_data_2'),
('placeholder_data_2', 'select', {'in': 1}),
('memory_in', 'memory_in_data'), ('memory_in_data', 'crop_in'),
('crop_in', 'crop_in_data'), ('crop_in_data', 'concat', {'in': 0}),
('const_1', 'const_1_data'), ('const_1_data', 'concat', {'in': 1}),
('concat', 'concat_data'), ('concat_data', 'memory_out'),
('memory_out', 'memory_out_data'), ('memory_out_data', 'result'),
('concat_data', 'crop_out'), ('crop_out', 'crop_out_data'),
('crop_out_data', 'select', {'in': 0}),
('const_0', 'const_0_data'), ('const_0_data', 'select', {'in': 2}),
('select', 'select_out_data'),
('select_out_data', 'memory')
],
nodes_with_edges_only=True
)
(flag, resp) = compare_graphs(graph, ref_graph, 'memory')
self.assertTrue(flag, resp)