def test_identify_input_output(self): g = GraphAnalyzer() g.graph = self.input_graph g.parse_graph() inputs, outputs = g.get_graph_input_output() self.assertEqual(inputs, self.inputs) self.assertEqual(outputs, self.outputs)
def test_identify_input_output(self): g = GraphAnalyzer() g.graph = self.input_graph g.parse_graph() inputs, outputs = g.get_graph_input_output() self.assertEqual(inputs, self.inputs) self.assertEqual(outputs, self.outputs) input_graph = tf.compat.v1.GraphDef() with open('model_1.pb', "rb") as f: input_graph.ParseFromString(f.read()) g = GraphAnalyzer() g.graph = input_graph g.parse_graph() inputs, outputs = g.get_graph_input_output() self.assertEqual(inputs, ['sub']) self.assertEqual(outputs, ['op_to_store']) input_graph = tf.compat.v1.GraphDef() with open('model_2.pb', "rb") as f: input_graph.ParseFromString(f.read()) g = GraphAnalyzer() g.graph = input_graph g.parse_graph() inputs, outputs = g.get_graph_input_output() self.assertEqual(inputs, []) self.assertEqual(outputs, []) input_graph = tf.compat.v1.GraphDef() with open('model_3.pb', "rb") as f: input_graph.ParseFromString(f.read()) g = GraphAnalyzer() g.graph = input_graph g.parse_graph() inputs, outputs = g.get_graph_input_output() self.assertEqual(inputs, []) self.assertEqual(outputs, [])
class PreOptimization(object): def __init__(self, model, inputs, outputs): self.output_node_names = list( set([output.split(":")[0] for output in outputs])) self.input_graph = get_graph_def(model, self.output_node_names) if 'MakeIterator' in [node.op for node in self.input_graph.node]: self.output_node_names.append('MakeIterator') self.analyzer = GraphAnalyzer() self.analyzer.graph = self.input_graph self.analyzer.parse_graph() self.input_node_names = inputs self.logger = logging.getLogger() self._tmp_graph_def = None self._excluded_node_names = [] if not self.input_node_names or not self.output_node_names: self.input_node_names, self.output_node_names = self.analyzer.get_graph_input_output( ) def get_excluded_node_names(self): """Get the excluded node name Returns: string list: the excluded ops' name """ return self._excluded_node_names @dump_elapsed_time("Pass Pre Optimization") def get_optimized_graphdef(self): """Executed the non-precision dependant graph optimization. The input graph will be optimized with following passes: 1. Remove the training nodes like Identity Op. 2. Split the shared nodes like weights node for multi-Conv2d. 3. Fold Constant Nodes as less as possible. 4. Fuse the Mul node into the previous Conv2D/MatMul if possible. 5. Strip the useless nodes. 6. Do the Common sequence elimation optimization on the graph. 7. Fold the BN node into the previous Conv2D if possible. Returns: [graphdef]: the optimized graphdef object. """ self.logger.debug("Start to pre optimize input model...") self._tmp_graph_def = ConvertLayoutOptimizer( self.input_graph, self.output_node_names).do_transformation() self._tmp_graph_def = RemoveTrainingNodesOptimizer( self._tmp_graph_def, protected_nodes=self.output_node_names).do_transformation() self._tmp_graph_def = SplitSharedInputOptimizer( self._tmp_graph_def).do_transformation() self._tmp_graph_def = GraphFoldConstantOptimizer( self._tmp_graph_def).do_transformation() self._tmp_graph_def = FuseColumnWiseMulOptimizer( self._tmp_graph_def).do_transformation() self._tmp_graph_def = StripUnusedNodesOptimizer( self._tmp_graph_def, self.input_node_names, self.output_node_names).do_transformation() self._tmp_graph_def = GraphCseOptimizer( self._tmp_graph_def).do_transformation() self._tmp_graph_def = FoldBatchNormNodesOptimizer( self._tmp_graph_def).do_transformation() #TODO we should handle all control ops elegantly not bypass it. self._tmp_graph_def, excluded_node_names = UpdateEnterOptimizer( self._tmp_graph_def).do_transformation() self._excluded_node_names.extend(excluded_node_names) self._tmp_graph_def.library.CopyFrom(self.input_graph.library) return self._tmp_graph_def def get_matched_nodes(self, patterns): """Searche the matched nodes with the specified patterns Args: patterns ([string list]): The patterns should be illustrated as below. [['MatMul'], ("BiasAdd"), ("Relu",)] Returns: [string list]: It will return the list that contains the matched nodes name and pattern. ['matched_node_a_name', 'matched_node_a_name',['MatMul','BiasAdd']] """ self.analyzer.graph = self._tmp_graph_def self.analyzer.parse_graph() res = [] for sub_pattern in patterns: res.extend(self.analyzer.query_fusion_pattern_nodes(sub_pattern)) return res def has_positive_input(self, node_name): """Check the specified node has the positive input or not. Args: node_name ([string]): node's name Returns: [bool]: True if the node has the positive input data, False if the node has the negative input data. """ return self.analyzer.has_positive_input(node_name)