def test_useful_crops(self):
graph = build_graph({'placeholder_in': {'kind': 'op', 'op': 'Parameter'},
'in_node': {'kind': 'data', 'shape': [1, 130]},
'crop1': {'kind': 'op', 'op': 'Crop', 'offset': 0, 'dim': 26, 'axis': -1},
'crop_data_1': {'kind': 'data', 'shape': [1, 26]},
'crop2': {'kind': 'op', 'op': 'Crop', 'offset': 26, 'dim': 26, 'axis': -1},
'crop_data_2': {'kind': 'data', 'shape': [1, 26]},
'crop4': {'kind': 'op', 'op': 'Crop', 'offset': 78, 'dim': 26, 'axis': -1},
'crop_data_4': {'kind': 'data', 'shape': [1, 26]},
'crop5': {'kind': 'op', 'op': 'Crop', 'offset': 104, 'dim': 26, 'axis': -1},
'crop_data_5': {'kind': 'data', 'shape': [1, 26]},
'concat': {'kind': 'op', 'op': 'Concat'},
'concat_data': {'kind': 'data', 'shape': [1, 104]},
'placeholder': {'kind': 'op', 'op': 'Parameter'},
},
[('placeholder_in', 'in_node'),
('in_node', 'crop1'), ('crop1', 'crop_data_1'),
('in_node', 'crop2'), ('crop2', 'crop_data_2'),
('in_node', 'crop4'), ('crop4', 'crop_data_4'),
('in_node', 'crop5'), ('crop5', 'crop_data_5'),
('crop_data_1', 'concat'),
('crop_data_2', 'concat'),
('crop_data_4', 'concat'),
('crop_data_5', 'concat'),
('concat', 'concat_data'),
('concat_data', 'placeholder')])
RemoveUselessCropsPattern().find_and_replace_pattern(graph)
ref_graph = build_graph({'placeholder_in': {'kind': 'op', 'op': 'Placeholder'},
'in_node': {'kind': 'data', 'shape': [1, 130]},
'crop1': {'kind': 'op', 'op': 'Crop', 'offset': 0, 'dim': 26, 'axis': -1},
'crop_data_1': {'kind': 'data', 'shape': [1, 26]},
'crop2': {'kind': 'op', 'op': 'Crop', 'offset': 26, 'dim': 26, 'axis': -1},
'crop_data_2': {'kind': 'data', 'shape': [1, 26]},
'crop4': {'kind': 'op', 'op': 'Crop', 'offset': 78, 'dim': 26, 'axis': -1},
'crop_data_4': {'kind': 'data', 'shape': [1, 26]},
'crop5': {'kind': 'op', 'op': 'Crop', 'offset': 104, 'dim': 26, 'axis': -1},
'crop_data_5': {'kind': 'data', 'shape': [1, 26]},
'concat': {'kind': 'op', 'op': 'Concat'},
'concat_data': {'kind': 'data', 'shape': [1, 104]},
'placeholder': {'kind': 'op', 'op': 'Placeholder'},
},
[('placeholder_in', 'in_node'),
('in_node', 'crop1'), ('crop1', 'crop_data_1'),
('in_node', 'crop2'), ('crop2', 'crop_data_2'),
('in_node', 'crop4'), ('crop4', 'crop_data_4'),
('in_node', 'crop5'), ('crop5', 'crop_data_5'),
('crop_data_1', 'concat'),
('crop_data_2', 'concat'),
('crop_data_4', 'concat'),
('crop_data_5', 'concat'),
('concat', 'concat_data'),
('concat_data', 'placeholder')]
)
(flag, resp) = compare_graphs(graph, ref_graph, 'placeholder')
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
