def optimize()

in tinynn/converter/operators/optimize.py [0:0]

• 75 lines of code
• 16 McCabe index (conditional complexity)

    def optimize(self):
        # Input/output passes
        self.output_list_unpack_pass()
        self.input_transpose_pass()
        self.output_transpose_pass()

        # Connect unused tensors with special nodes
        self.connect_unused_tensors_pass()

        # Transpose, Reshape and NO-OP cleanup
        self.branch_reshape_expand_pass()
        self.fuse_simple_reshape_pass()
        self.branch_transpose_expand_pass()
        self.fuse_simple_transpose_pass()
        self.fuse_simple_gather_pass()
        for branch in (False, True):
            self.remove_noop_pass(branch)
        self.fuse_wrapped_reshape_within_transpose_pass()

        # Buffer folding, which is needed by the fusion passes below
        for _ in range(2):
            self.fold_reshape_buffer()
            self.fold_transpose_buffer()

        # Move `transpose` ops for the rewrite quantizable pass
        self.elementwise_op_transpose_passthrough_pass(quantizable_ops_only=True)
        self.branch_transpose_expand_pass()
        self.fuse_simple_transpose_pass()

        # Fuse reciprocal and sqrt
        self.fuse_reciprocal_sqrt()

        # Map quantizable ops to quantized kernels
        self.elementwise_op_quantize_passthrough_pass()

        # Remove consecutive dequantize and quantize nodes
        self.fuse_dequant_quant_pass(q_first=False)

        # OP fusion passes before transformation
        self.fuse_conv_fc_bn()
        self.fuse_activation()
        self.fuse_requantize()
        self.fuse_bn_conv()

        # Convert TinyNeuralNetwork ops to TFLite ops
        self.transform_graph()

        # OP fusion passes after transformation
        self.fuse_bmm_add_pass()
        self.fuse_activation()

        # Transpose and reshape cleanup
        self.branch_reshape_expand_pass()
        self.branch_transpose_expand_pass()
        self.fuse_simple_transpose_pass()
        self.fuse_simple_reshape_pass()

        # Branch transpose & reshape cleanup
        for i in range(11):
            t_count = self.elementwise_op_transpose_passthrough_pass()
            self.branch_transpose_expand_pass()
            self.fuse_simple_transpose_pass()

            r_count = self.elementwise_op_reshape_passthrough_pass()
            self.branch_reshape_expand_pass()
            self.fuse_simple_reshape_pass()

            c_count = self.elementwise_reshape_transpose_passthrough_pass()
            self.branch_transpose_expand_pass()
            self.fuse_simple_transpose_pass()

            if t_count + r_count + c_count == 0:
                log.debug(f'elem p/t pass finished in {i + 1} steps')
                break

        # Other cleanups
        self.fuse_simple_slice_pass()
        for branch in (False, True):
            self.remove_noop_pass(branch)
        self.fuse_wrapped_reshape_within_transpose_pass()

        # Buffer folding
        for _ in range(2):
            self.fold_reshape_buffer()
            self.fold_transpose_buffer()

        # Transpose and reshape cleanup
        for _ in range(2):
            self.transpose_to_reshape_pass()
            self.branch_reshape_expand_pass()
            self.fuse_simple_reshape_pass()
            self.fuse_simple_transpose_pass()

        self.lower_transpose_dim_pass()

        # Some advanced fusion logic
        self.fuse_conv2d_gather()

        # Remove consecutive dequantize and quantize nodes
        self.fuse_dequant_quant_pass(q_first=True)

        # Fuse reciprocal and sqrt
        self.fuse_reciprocal_sqrt()

        # Remove additional tile nodes before elementwise ops
        self.remove_tile_before_binary_elementwise_ops()

        # Fuse activation
        self.fuse_activation()

        # Fuse quant/dequant nodes
        self.fuse_quant_dequant_nodes()

        # Input output quantize type
        self.quantize_input_output_type_pass()

        # Fuse same padding
        self.fuse_same_padding()
        self.fuse_same_padding_slicing()

        self.fuse_gather_conv2d()

        # Group conv & deconv
        self.group_conv_rewrite_pass()
        self.group_deconv_rewrite_pass()

        # TFLite micro specific
        self.cat_split_pass()
        self.split_requantize()

        # Group the same tensors into one
        self.group_tensors_pass()

        # Final cleanup
        self.cleanup_dead_nodes()