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 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 test_pass_transpose(self) -> None:
"""Test code for transposing optimizer pass."""
data = np.random.rand(3, 2, 2, 1)
graph1 = self.create_sample_graph(data)
graph2 = self.create_expected_graph(data)
pass_transpose(graph1)
self.assertEqual(graph1, graph2, 'transpose to NHWC failed.')
print("Test pass #1 transpose passed!")