def emit_Pool()

in mmdnn/conversion/tensorflow/tensorflow_emitter.py [0:0]
56 lines of code
17 McCabe index (conditional complexity)

    def emit_Pool(self, IR_node):
        pooling_type = IR_node.get_attr('pooling_type')
        if pooling_type == 'MAX':
            op = 'max_pool'
            padding_const = ", constant_values=float('-Inf')"
        elif pooling_type == 'AVG':
            op = 'avg_pool'
            padding_const = ""
        else:
            raise ValueError("unknown pooling type [{}].".format(pooling_type))

        arrlen = len(IR_node.get_attr('strides'))
        dim_str = '3d' if arrlen == 5 else ""

        if IR_node.layer.attr['global_pooling'].b:
            code = "{:<15} = tf.nn.{}{}({}, [1] + {}.get_shape().as_list()[1:-1] + [1], strides = [1] * {}, padding = 'VALID', name = '{}')".format(
                IR_node.variable_name,
                op,
                dim_str,
                self.parent_variable_name(IR_node),
                self.parent_variable_name(IR_node),
                arrlen,
                IR_node.name)
        else:
            dim = len(IR_node.get_attr("strides")) - 2
            dilations = IR_node.get_attr('dilations')
            if dilations:
                for e in IR_node.get_attr('dilations'):
                    assert e == 1

            pool_size = IR_node.get_attr('kernel_shape')[1:-1]
            strides = IR_node.get_attr('strides')[1:-1]
            padding = IR_node.get_attr('pads')[1:dim]

            if pooling_type == "AVG" and pool_size.count(pool_size[0]) == len(pool_size) and strides[0] == 1 and strides.count(strides[0]) == len(strides) and padding.count(padding[0]) == len(padding) and pool_size[0] == padding[0]*2 + 1:
                kernel_shape_str = ', '.join('%s' % i for i in IR_node.get_attr('kernel_shape'))
                strides_str = ', '.join('%s' % i for i in IR_node.get_attr('strides'))

                code = "{:<15} = tf.nn.{}{}({}, [{}], [{}], padding='{}', name='{}')".format(
                    IR_node.variable_name,
                    op,
                    dim_str,
                    self.parent_variable_name(IR_node),
                    kernel_shape_str,
                    strides_str,
                    'SAME',
                    IR_node.name)
            else:
                kernel_shape_str = ', '.join('%s' % i for i in IR_node.get_attr('kernel_shape'))
                strides_str = ', '.join('%s' % i for i in IR_node.get_attr('strides'))
                input_node, padding = self._defuse_padding(IR_node, padding_const)
                code = "{:<15} = tf.nn.{}{}({}, [{}], [{}], padding='{}', name='{}')".format(
                    IR_node.variable_name,
                    op,
                    dim_str,
                    input_node,
                    kernel_shape_str,
                    strides_str,
                    padding,
                    IR_node.name)

        return code