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)
pass_propagate_datatypes(graph)
pass_propagate_format(graph)
pass_constant_folding(graph)
pass_simplify_batchnorm(graph)
pass_insert_cast(graph)
def test_pass_quantize_convolutions(self) -> None:
"""Test pass."""
data1 = np.float32(np.random.rand(1, 2, 2, 3))
data2 = np.float32(np.random.rand(1, 2, 2, 3))
graph1 = self.create_sample_graph(data1, data2)
pass_quantize_convolutions(graph1)
self.assertEqual(graph1.get_op('aqtz1').dtype, QUANTIZED_PACKED(),
'[Failed] Found output dtype of activation quantizer not proper')
self.assertEqual(graph1.get_op('kqtz1').dtype, PackedUint32(),
'[Failed] Found output dtype of kernel quantizer not proper')
self.assertEqual(graph1.get_op('conv2').dtype, Float32(),
'[Failed] Found output dtype of conv not proper')
print("Test pass #5 quantize_convolutions passed!")