def optimize_graph_step(graph: Graph, config: Config) -> None: """Optimizing graph that imported from tensorflow pb. Args: graph (Graph): Graph that optimization passes are applying to config (Config): Collection of configurations Returns: """ pass_remove_identities(graph) pass_transpose(graph) if config.activate_hard_quantization: pass_lookup(graph) pass_propagate_quantization_details_into_conv(graph) if config.threshold_skipping: pass_compute_thresholds(graph) pass_pack_weights(graph) pass_quantize_convolutions(graph) if config.threshold_skipping: pass_propagate_output_type_backward(graph) pass_propagate_datatypes(graph) pass_propagate_format(graph) pass_constant_folding(graph) pass_simplify_batchnorm(graph)
def optimize_graph_step(model: Model, config: Config) -> None: """Optimize graph in the model. Parameters ---------- model : Model Model that contains the graph config : Config Collection of configurations """ graph: Graph = model.graph pass_remove_identities(graph) pass_transpose(graph) if config.activate_hard_quantization: pass_lookup(graph) pass_propagate_quantization_details_into_conv(graph) if config.threshold_skipping: pass_compute_thresholds(graph) pass_pack_weights(graph) pass_quantize_convolutions(graph) pass_fix_qtz_types_and_format(graph) pass_propagate_output_type_backward(graph) pass_propagate_datatypes(graph) pass_propagate_format(graph) pass_constant_folding(graph)
def test_pass_constant_folding(self) -> None: """Test pass.""" graph1 = self.create_sample_graph() pass_constant_folding(graph1) self.assertEqual(set(graph1.get_op('potatoes_new').data), set(np.array([2, 5])), '[Failed] Found folded constant not correct') print("Test pass #9 constant folding passed!")