efficient_net.EfficientNet.block() - Code Metrics - Inspection of "Issue 678: Add EfficientNet" - DeepRegNet/DeepReg - Measure and Improve Code Quality continuously with Scrutinizer

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Pull Request — main (#785)

unknown

created 2021-06-04 14:48 UTC

efficient_net.EfficientNet.block() B

↳ Parent: efficient_net

Complexity

Conditions

Size

Total Lines	70
Code Lines	44

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	44
dl	0
loc	70
rs	7.424
c	0
b	0
f	0
cc	7
nop	12

How to fix Long Method Many Parameters

"""This script provides an example of using efficient for training."""
import os
import math
import tensorflow as tf
import numpy as np
from copy import deepcopy
from tensorflow import keras
from tensorflow.keras import backend
from tensorflow.keras import layers
from tensorflow.keras.models import Model
from tensorflow.keras.applications import imagenet_utils
from tensorflow.keras.applications.imagenet_utils import decode_predictions
from tensorflow.keras.preprocessing import image

from deepreg.model.backbone import Backbone
from deepreg.registry import REGISTRY
from deepreg.train import train

efficientnet_params = {
    # model_name: (width_mult, depth_mult, image_size, dropout_rate, dropconnect_rate)
    "efficientnet-b0": (1.0, 1.0, 224, 0.2, 0.2),
    "efficientnet-b1": (1.0, 1.1, 240, 0.2, 0.2),
    "efficientnet-b2": (1.1, 1.2, 260, 0.3, 0.2),
    "efficientnet-b3": (1.2, 1.4, 300, 0.3, 0.2),
    "efficientnet-b4": (1.4, 1.8, 380, 0.4, 0.2),
    "efficientnet-b5": (1.6, 2.2, 456, 0.4, 0.2),
    "efficientnet-b6": (1.8, 2.6, 528, 0.5, 0.2),
    "efficientnet-b7": (2.0, 3.1, 600, 0.5, 0.2),
}


# Each Blocks Parameters
DEFAULT_BLOCKS_ARGS = [
    {'kernel_size': 3, 'repeats': 1, 'filters_in': 32, 'filters_out': 16,
     'expand_ratio': 1, 'id_skip': True, 'strides': 1, 'se_ratio': 0.25},
    {'kernel_size': 3, 'repeats': 2, 'filters_in': 16, 'filters_out': 24,
     'expand_ratio': 6, 'id_skip': True, 'strides': 2, 'se_ratio': 0.25},
    {'kernel_size': 5, 'repeats': 2, 'filters_in': 24, 'filters_out': 40,
     'expand_ratio': 6, 'id_skip': True, 'strides': 2, 'se_ratio': 0.25},
    {'kernel_size': 3, 'repeats': 3, 'filters_in': 40, 'filters_out': 80,
     'expand_ratio': 6, 'id_skip': True, 'strides': 2, 'se_ratio': 0.25},
    {'kernel_size': 5, 'repeats': 3, 'filters_in': 80, 'filters_out': 112,
     'expand_ratio': 6, 'id_skip': True, 'strides': 1, 'se_ratio': 0.25},
    {'kernel_size': 5, 'repeats': 4, 'filters_in': 112, 'filters_out': 192,
     'expand_ratio': 6, 'id_skip': True, 'strides': 2, 'se_ratio': 0.25},
    {'kernel_size': 3, 'repeats': 1, 'filters_in': 192, 'filters_out': 320,
     'expand_ratio': 6, 'id_skip': True, 'strides': 1, 'se_ratio': 0.25}
]

# Two Kernel Initializer
CONV_KERNEL_INITIALIZER = {
    'class_name': 'VarianceScaling',
    'config': {
        'scale': 2.0,
        'mode': 'fan_out',
        'distribution': 'normal'
    }
}

DENSE_KERNEL_INITIALIZER = {
    'class_name': 'VarianceScaling',
    'config': {
        'scale': 1. / 3.,
        'mode': 'fan_out',
        'distribution': 'uniform'
    }
}


@REGISTRY.register_backbone(name="efficient_net")
class EfficientNet(Backbone):
    """
    A dummy custom model for demonstration purpose only
    """

    def __init__(
        self,
        image_size: tuple,
        out_channels: int,
        num_channel_initial: int,
        out_kernel_initializer: str,
        out_activation: str,
        name: str = "EfficientNet",
        width_coefficient: float = 1.0,
        depth_coefficient: float = 1.0,
        default_size: int = 224,
        dropout_rate: float = 0.2,
        drop_connect_rate: float = 0.2,
        depth_divisor: int = 8,
        **kwargs,
    ):
        """
        Init.

        :param image_size: (dim1, dim2, dim3), dims of input image.
        :param out_channels: number of channels for the output
        :param num_channel_initial: number of initial channels
        :param depth: input is at level 0, bottom is at level depth
        :param out_kernel_initializer: kernel initializer for the last layer
        :param out_activation: activation at the last layer
        :param name: name of the backbone
        :param kwargs: additional arguments.
        """
        super().__init__(
            image_size=image_size,
            out_channels=out_channels,
            num_channel_initial=num_channel_initial,
            out_kernel_initializer=out_kernel_initializer,
            out_activation=out_activation,
            name=name,
            **kwargs,
        )

        self.width_coefficient =  width_coefficient
        self.depth_coefficient = depth_coefficient
        self.default_size = default_size
        self.dropout_rate = dropout_rate
        self.drop_connect_rate = drop_connect_rate
        self.depth_divisor = depth_divisor
        self.activation_fn = tf.nn.swish


    def correct_pad(self, inputs, kernel_size):
        img_dim = 1
        input_size = backend.int_shape(inputs)[img_dim:(img_dim + 3)]

        if isinstance(kernel_size, int):
            kernel_size = (kernel_size, kernel_size, kernel_size)

        if input_size[0] is None:
            adjust = (1, 1, 1)
        else:
            adjust = (1 - input_size[0] % 2, 1 - input_size[1] % 2, 1 - input_size[2] % 2)

        correct = (kernel_size[0] // 2, kernel_size[1] // 2, kernel_size[2] // 2)

        return ((correct[0] - adjust[0], correct[0]),
                (correct[1] - adjust[1], correct[1]),
                (correct[2] - adjust[2], correct[2]))

    def block(self, inputs, activation_fn=tf.nn.swish, drop_rate=0., name='',
            filters_in=32, filters_out=16, kernel_size=3, strides=1,
            expand_ratio=1, se_ratio=0., id_skip=True):

        bn_axis = 4

        filters = filters_in * expand_ratio

        # Inverted residuals
        if expand_ratio != 1:
            x = layers.Conv3D(filters, 1,
                            padding='same',
                            use_bias=False,
                            kernel_initializer=CONV_KERNEL_INITIALIZER,
                            name=name + 'expand_conv')(inputs)
            x = layers.BatchNormalization(axis=bn_axis, name=name + 'expand_bn')(x)
            x = layers.Activation(activation_fn, name=name + 'expand_activation')(x)
        else:
            x = inputs

        # padding
        # if strides == 2:
        #     x = layers.ZeroPadding3D(padding=self.correct_pad(x, kernel_size),
        #                             name=name + 'dwconv_pad')(x)
        #     conv_pad = 'valid'
        # else:
        #     conv_pad = 'same'

        # TODO(Sicong): Find DepthwiseConv3D
        # x = layers.DepthwiseConv2D(kernel_size,
        #                         strides=strides,
        #                         padding=conv_pad,
        #                         use_bias=False,
        #                         depthwise_initializer=CONV_KERNEL_INITIALIZER,
        #                         name=name + 'dwconv')(x)
        x = layers.BatchNormalization(axis=bn_axis, name=name + 'bn')(x)
        x = layers.Activation(activation_fn, name=name + 'activation')(x)

        if 0 < se_ratio <= 1:
            filters_se = max(1, int(filters_in * se_ratio))
            se = layers.GlobalAveragePooling3D(name=name + 'se_squeeze')(x)
            se = layers.Reshape((1, 1, 1, filters), name=name + 'se_reshape')(se)
            se = layers.Conv3D(filters_se, 1,
                            padding='same',
                            activation=activation_fn,
                            kernel_initializer=CONV_KERNEL_INITIALIZER,
                            name=name + 'se_reduce')(se)
            se = layers.Conv3D(filters, 1,
                            padding='same',
                            activation='sigmoid',
                            kernel_initializer=CONV_KERNEL_INITIALIZER,
                            name=name + 'se_expand')(se)
            x = layers.multiply([x, se], name=name + 'se_excite')

        # part3
        x = layers.Conv3D(filters_out, 1,
                        padding='same',
                        use_bias=False,
                        kernel_initializer=CONV_KERNEL_INITIALIZER,
                        name=name + 'project_conv')(x)
        x = layers.BatchNormalization(axis=bn_axis, name=name + 'project_bn')(x)

        if (id_skip is True and strides == 1 and filters_in == filters_out):
            if drop_rate > 0:
                x = layers.Dropout(drop_rate,
                                noise_shape=None,
                                name=name + 'drop')(x)
            x = layers.add([x, inputs], name=name + 'add')

        return x


    def call(self, inputs: tf.Tensor, training=None, mask=None) -> tf.Tensor:
        """
        Builds graph based on built layers.

        :param inputs: shape = (batch, f_dim1, f_dim2, f_dim3, in_channels)
        :param training:
        :param mask:
        :return: shape = (batch, f_dim1, f_dim2, f_dim3, out_channels)
        """
        img_input = layers.Input(tensor=inputs, shape=self.image_size)
        bn_axis = 4  
        # Build stem
        x = img_input
        # x = layers.ZeroPadding3D(padding=self.correct_pad(x, 3),
        #                         name='stem_conv_pad')(x)

        x = layers.Conv3D(self.round_filters(32), 3,
                        strides=2,
                        padding='same',
                        use_bias=False,
                        kernel_initializer=CONV_KERNEL_INITIALIZER,
                        name='stem_conv')(x)
        x = layers.BatchNormalization(axis=bn_axis, name='stem_bn')(x)
        x = layers.Activation(self.activation_fn, name='stem_activation')(x)
        blocks_args = deepcopy(DEFAULT_BLOCKS_ARGS)

        b = 0
        # 计算总的block的数量
        blocks = float(sum(args['repeats'] for args in blocks_args))
        for (i, args) in enumerate(blocks_args):
            assert args['repeats'] > 0
            args['filters_in'] = self.round_filters(args['filters_in'])
            args['filters_out'] = self.round_filters(args['filters_out'])

            for j in range(self.round_repeats(args.pop('repeats'))):
                if j > 0:
                    args['strides'] = 1
                    args['filters_in'] = args['filters_out']
                x = self.block(x, self.activation_fn, self.drop_connect_rate * b / blocks,
                        name='block{}{}_'.format(i + 1, chr(j + 97)), **args)
                b += 1
        
        x = layers.Conv3D(self.round_filters(1280), 1,
                        padding='same',
                        use_bias=False,
                        kernel_initializer=CONV_KERNEL_INITIALIZER,
                        name='top_conv')(x)
        x = layers.BatchNormalization(axis=bn_axis, name='top_bn')(x)
        x = layers.Activation(self.activation_fn, name='top_activation')(x)

        # Use GlobalAveragePooling2D replace Fully-connected layer
        # x = layers.GlobalAveragePooling3D(name='avg_pool')(x)
        print("input.shape", inputs.shape, x.shape)
        # if dropout_rate > 0:
        #     x = layers.Dropout(dropout_rate, name='top_dropout')(x)

        # x = layers.Dense(classes,
        #                     activation='softmax',
        #                     kernel_initializer=DENSE_KERNEL_INITIALIZER,
        #                     name='probs')(x)

        return x

    # 保证filter的大小可以被8整除
    def round_filters(self, filters):
        """Round number of filters based on depth multiplier."""
        filters *= self.width_coefficient
        divisor = self.depth_divisor
        new_filters = max(divisor, int(filters + divisor / 2) // divisor * divisor)
        # Make sure that round down does not go down by more than 10%.
        if new_filters < 0.9 * filters:
            new_filters += divisor
        return int(new_filters)

    # 重复次数，取顶
    def round_repeats(self, repeats):
        return int(math.ceil(self.depth_coefficient * repeats))


config_path = "examples/config_efficient_net.yaml"
train(
    gpu="",
    config_path=config_path,
    gpu_allow_growth=True,
    ckpt_path="",
)


1			"""This script provides an example of using efficient for training."""
2			import os
3			import math
4			import tensorflow as tf
5			import numpy as np
6			from copy import deepcopy
7			from tensorflow import keras
8			from tensorflow.keras import backend
9			from tensorflow.keras import layers
10			from tensorflow.keras.models import Model
11			from tensorflow.keras.applications import imagenet_utils
12			from tensorflow.keras.applications.imagenet_utils import decode_predictions
13			from tensorflow.keras.preprocessing import image
14
15			from deepreg.model.backbone import Backbone
16			from deepreg.registry import REGISTRY
17			from deepreg.train import train
18
19			efficientnet_params = {
20			# model_name: (width_mult, depth_mult, image_size, dropout_rate, dropconnect_rate)
21			"efficientnet-b0": (1.0, 1.0, 224, 0.2, 0.2),
22			"efficientnet-b1": (1.0, 1.1, 240, 0.2, 0.2),
23			"efficientnet-b2": (1.1, 1.2, 260, 0.3, 0.2),
24			"efficientnet-b3": (1.2, 1.4, 300, 0.3, 0.2),
25			"efficientnet-b4": (1.4, 1.8, 380, 0.4, 0.2),
26			"efficientnet-b5": (1.6, 2.2, 456, 0.4, 0.2),
27			"efficientnet-b6": (1.8, 2.6, 528, 0.5, 0.2),
28			"efficientnet-b7": (2.0, 3.1, 600, 0.5, 0.2),
29			}
30
31
32			# Each Blocks Parameters
33			DEFAULT_BLOCKS_ARGS = [
34			{'kernel_size': 3, 'repeats': 1, 'filters_in': 32, 'filters_out': 16,
35			'expand_ratio': 1, 'id_skip': True, 'strides': 1, 'se_ratio': 0.25},
36			{'kernel_size': 3, 'repeats': 2, 'filters_in': 16, 'filters_out': 24,
37			'expand_ratio': 6, 'id_skip': True, 'strides': 2, 'se_ratio': 0.25},
38			{'kernel_size': 5, 'repeats': 2, 'filters_in': 24, 'filters_out': 40,
39			'expand_ratio': 6, 'id_skip': True, 'strides': 2, 'se_ratio': 0.25},
40			{'kernel_size': 3, 'repeats': 3, 'filters_in': 40, 'filters_out': 80,
41			'expand_ratio': 6, 'id_skip': True, 'strides': 2, 'se_ratio': 0.25},
42			{'kernel_size': 5, 'repeats': 3, 'filters_in': 80, 'filters_out': 112,
43			'expand_ratio': 6, 'id_skip': True, 'strides': 1, 'se_ratio': 0.25},
44			{'kernel_size': 5, 'repeats': 4, 'filters_in': 112, 'filters_out': 192,
45			'expand_ratio': 6, 'id_skip': True, 'strides': 2, 'se_ratio': 0.25},
46			{'kernel_size': 3, 'repeats': 1, 'filters_in': 192, 'filters_out': 320,
47			'expand_ratio': 6, 'id_skip': True, 'strides': 1, 'se_ratio': 0.25}
48			]
49
50			# Two Kernel Initializer
51			CONV_KERNEL_INITIALIZER = {
52			'class_name': 'VarianceScaling',
53			'config': {
54			'scale': 2.0,
55			'mode': 'fan_out',
56			'distribution': 'normal'
57			}
58			}
59
60			DENSE_KERNEL_INITIALIZER = {
61			'class_name': 'VarianceScaling',
62			'config': {
63			'scale': 1. / 3.,
64			'mode': 'fan_out',
65			'distribution': 'uniform'
66			}
67			}
68
69
70			@REGISTRY.register_backbone(name="efficient_net")
71			class EfficientNet(Backbone):
72			"""
73			A dummy custom model for demonstration purpose only
74			"""
75
76			def __init__(
77			self,
78			image_size: tuple,
79			out_channels: int,
80			num_channel_initial: int,
81			out_kernel_initializer: str,
82			out_activation: str,
83			name: str = "EfficientNet",
84			width_coefficient: float = 1.0,
85			depth_coefficient: float = 1.0,
86			default_size: int = 224,
87			dropout_rate: float = 0.2,
88			drop_connect_rate: float = 0.2,
89			depth_divisor: int = 8,
90			**kwargs,
91			):
92			"""
93			Init.
94
95			:param image_size: (dim1, dim2, dim3), dims of input image.
96			:param out_channels: number of channels for the output
97			:param num_channel_initial: number of initial channels
98			:param depth: input is at level 0, bottom is at level depth
99			:param out_kernel_initializer: kernel initializer for the last layer
100			:param out_activation: activation at the last layer
101			:param name: name of the backbone
102			:param kwargs: additional arguments.
103			"""
104			super().__init__(
105			image_size=image_size,
106			out_channels=out_channels,
107			num_channel_initial=num_channel_initial,
108			out_kernel_initializer=out_kernel_initializer,
109			out_activation=out_activation,
110			name=name,
111			**kwargs,
112			)
113
114			self.width_coefficient = width_coefficient
115			self.depth_coefficient = depth_coefficient
116			self.default_size = default_size
117			self.dropout_rate = dropout_rate
118			self.drop_connect_rate = drop_connect_rate
119			self.depth_divisor = depth_divisor
120			self.activation_fn = tf.nn.swish
121
122
123			def correct_pad(self, inputs, kernel_size):
124			img_dim = 1
125			input_size = backend.int_shape(inputs)[img_dim:(img_dim + 3)]
126
127			if isinstance(kernel_size, int):
128			kernel_size = (kernel_size, kernel_size, kernel_size)
129
130			if input_size[0] is None:
131			adjust = (1, 1, 1)
132			else:
133			adjust = (1 - input_size[0] % 2, 1 - input_size[1] % 2, 1 - input_size[2] % 2)
134
135			correct = (kernel_size[0] // 2, kernel_size[1] // 2, kernel_size[2] // 2)
136
137			return ((correct[0] - adjust[0], correct[0]),
138			(correct[1] - adjust[1], correct[1]),
139			(correct[2] - adjust[2], correct[2]))
140
141			def block(self, inputs, activation_fn=tf.nn.swish, drop_rate=0., name='',
142			filters_in=32, filters_out=16, kernel_size=3, strides=1,
143			expand_ratio=1, se_ratio=0., id_skip=True):
144
145			bn_axis = 4
146
147			filters = filters_in * expand_ratio
148
149			# Inverted residuals
150			if expand_ratio != 1:
151			x = layers.Conv3D(filters, 1,
152			padding='same',
153			use_bias=False,
154			kernel_initializer=CONV_KERNEL_INITIALIZER,
155			name=name + 'expand_conv')(inputs)
156			x = layers.BatchNormalization(axis=bn_axis, name=name + 'expand_bn')(x)
157			x = layers.Activation(activation_fn, name=name + 'expand_activation')(x)
158			else:
159			x = inputs
160
161			# padding
162			# if strides == 2:
163			# x = layers.ZeroPadding3D(padding=self.correct_pad(x, kernel_size),
164			# name=name + 'dwconv_pad')(x)
165			# conv_pad = 'valid'
166			# else:
167			# conv_pad = 'same'
168
169			# TODO(Sicong): Find DepthwiseConv3D
170			# x = layers.DepthwiseConv2D(kernel_size,
171			# strides=strides,
172			# padding=conv_pad,
173			# use_bias=False,
174			# depthwise_initializer=CONV_KERNEL_INITIALIZER,
175			# name=name + 'dwconv')(x)
176			x = layers.BatchNormalization(axis=bn_axis, name=name + 'bn')(x)
177			x = layers.Activation(activation_fn, name=name + 'activation')(x)
178
179			if 0 < se_ratio <= 1:
180			filters_se = max(1, int(filters_in * se_ratio))
181			se = layers.GlobalAveragePooling3D(name=name + 'se_squeeze')(x)
182			se = layers.Reshape((1, 1, 1, filters), name=name + 'se_reshape')(se)
183			se = layers.Conv3D(filters_se, 1,
184			padding='same',
185			activation=activation_fn,
186			kernel_initializer=CONV_KERNEL_INITIALIZER,
187			name=name + 'se_reduce')(se)
188			se = layers.Conv3D(filters, 1,
189			padding='same',
190			activation='sigmoid',
191			kernel_initializer=CONV_KERNEL_INITIALIZER,
192			name=name + 'se_expand')(se)
193			x = layers.multiply([x, se], name=name + 'se_excite')
194
195			# part3
196			x = layers.Conv3D(filters_out, 1,
197			padding='same',
198			use_bias=False,
199			kernel_initializer=CONV_KERNEL_INITIALIZER,
200			name=name + 'project_conv')(x)
201			x = layers.BatchNormalization(axis=bn_axis, name=name + 'project_bn')(x)
202
203			if (id_skip is True and strides == 1 and filters_in == filters_out):
204			if drop_rate > 0:
205			x = layers.Dropout(drop_rate,
206			noise_shape=None,
207			name=name + 'drop')(x)
208			x = layers.add([x, inputs], name=name + 'add')
209
210			return x
211
212
213			def call(self, inputs: tf.Tensor, training=None, mask=None) -> tf.Tensor:
214			"""
215			Builds graph based on built layers.
216
217			:param inputs: shape = (batch, f_dim1, f_dim2, f_dim3, in_channels)
218			:param training:
219			:param mask:
220			:return: shape = (batch, f_dim1, f_dim2, f_dim3, out_channels)
221			"""
222			img_input = layers.Input(tensor=inputs, shape=self.image_size)
223			bn_axis = 4
224			# Build stem
225			x = img_input
226			# x = layers.ZeroPadding3D(padding=self.correct_pad(x, 3),
227			# name='stem_conv_pad')(x)
228
229			x = layers.Conv3D(self.round_filters(32), 3,
230			strides=2,
231			padding='same',
232			use_bias=False,
233			kernel_initializer=CONV_KERNEL_INITIALIZER,
234			name='stem_conv')(x)
235			x = layers.BatchNormalization(axis=bn_axis, name='stem_bn')(x)
236			x = layers.Activation(self.activation_fn, name='stem_activation')(x)
237			blocks_args = deepcopy(DEFAULT_BLOCKS_ARGS)
238
239			b = 0
240			# 计算总的block的数量
241			blocks = float(sum(args['repeats'] for args in blocks_args))
242			for (i, args) in enumerate(blocks_args):
243			assert args['repeats'] > 0
244			args['filters_in'] = self.round_filters(args['filters_in'])
245			args['filters_out'] = self.round_filters(args['filters_out'])
246
247			for j in range(self.round_repeats(args.pop('repeats'))):
248			if j > 0:
249			args['strides'] = 1
250			args['filters_in'] = args['filters_out']
251			x = self.block(x, self.activation_fn, self.drop_connect_rate * b / blocks,
252			name='block{}{}_'.format(i + 1, chr(j + 97)), **args)
253			b += 1
254
255			x = layers.Conv3D(self.round_filters(1280), 1,
256			padding='same',
257			use_bias=False,
258			kernel_initializer=CONV_KERNEL_INITIALIZER,
259			name='top_conv')(x)
260			x = layers.BatchNormalization(axis=bn_axis, name='top_bn')(x)
261			x = layers.Activation(self.activation_fn, name='top_activation')(x)
262
263			# Use GlobalAveragePooling2D replace Fully-connected layer
264			# x = layers.GlobalAveragePooling3D(name='avg_pool')(x)
265			print("input.shape", inputs.shape, x.shape)
266			# if dropout_rate > 0:
267			# x = layers.Dropout(dropout_rate, name='top_dropout')(x)
268
269			# x = layers.Dense(classes,
270			# activation='softmax',
271			# kernel_initializer=DENSE_KERNEL_INITIALIZER,
272			# name='probs')(x)
273
274			return x
275
276			# 保证filter的大小可以被8整除
277			def round_filters(self, filters):
278			"""Round number of filters based on depth multiplier."""
279			filters *= self.width_coefficient
280			divisor = self.depth_divisor
281			new_filters = max(divisor, int(filters + divisor / 2) // divisor * divisor)
282			# Make sure that round down does not go down by more than 10%.
283			if new_filters < 0.9 * filters:
284			new_filters += divisor
285			return int(new_filters)
286
287			# 重复次数，取顶
288			def round_repeats(self, repeats):
289			return int(math.ceil(self.depth_coefficient * repeats))
290
291
292			config_path = "examples/config_efficient_net.yaml"
293			train(
294			gpu="",
295			config_path=config_path,
296			gpu_allow_growth=True,
297			ckpt_path="",
298			)
299

DeepRegNet / DeepReg

Pull Request — main (#785)

efficient_net.EfficientNet.block() B

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How to fix Long Method Many Parameters

Long Method

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