test.unit.test_backbone_unet.TestUNet.test_channels() - Code Metrics - Inspection of "Merge pull request #673 from DeepRegNet/671-custom..." - DeepRegNet/DeepReg - Measure and Improve Code Quality continuously with Scrutinizer

Completed

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by Yunguan

created 2021-03-05 13:19 UTC

TestUNet.test_channels() B

↳ Parent: test.unit.test_backbone_unet

Complexity

Conditions

Size

Total Lines	56
Code Lines	37

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	37
dl	0
loc	56
rs	8.9919
c	0
b	0
f	0
cc	3
nop	6

How to fix Long Method

# coding=utf-8

"""
Tests for deepreg/model/backbone/u_net.py
"""
from typing import Tuple

import pytest
import tensorflow as tf

from deepreg.model.backbone.u_net import UNet


class TestUNet:
    """
    Test the backbone.u_net.UNet class
    """

    @pytest.mark.parametrize(
        "depth,encode_num_channels,decode_num_channels",
        [
            (2, (4, 8, 16), (4, 8, 16)),
            (2, (4, 8, 8), (4, 8, 8)),
            (2, (4, 8, 8), (8, 8, 8)),
        ],
    )
    @pytest.mark.parametrize("pooling", [True, False])

    @pytest.mark.parametrize("concat_skip", [True, False])
    def test_channels(
        self,
        depth: int,
        encode_num_channels: Tuple,
        decode_num_channels: Tuple,
        pooling: bool,
        concat_skip: bool,
    ):
        """
        Test unet with custom encode/decode channels.

        :param depth: input is at level 0, bottom is at level depth
        :param encode_num_channels: filters/channels for down-sampling,
            by default it is doubled at each layer during down-sampling
        :param decode_num_channels: filters/channels for up-sampling,
            by default it is the same as encode_num_channels
        :param pooling: for down-sampling, use non-parameterized
                        pooling if true, otherwise use conv3d
        :param concat_skip: if concatenate skip or add it
        """
        # in case of adding skip tensors, the channels should match
        expect_err = (not concat_skip) and encode_num_channels != decode_num_channels

        image_size = (5, 6, 7)
        out_ch = 3
        try:
            network = UNet(
                image_size=image_size,
                out_channels=out_ch,
                num_channel_initial=None,
                encode_num_channels=encode_num_channels,
                decode_num_channels=decode_num_channels,
                depth=depth,
                out_kernel_initializer="he_normal",
                out_activation="softmax",
                pooling=pooling,
                concat_skip=concat_skip,
            )
        except ValueError as err:
            if expect_err:
                return
            raise err
        inputs = tf.ones(shape=(5, *image_size, out_ch))

        output = network.call(inputs)
        assert inputs.shape == output.shape

    @pytest.mark.parametrize(
        "image_size,depth",
        [((11, 12, 13), 5), ((8, 8, 8), 3)],
    )
    @pytest.mark.parametrize("pooling", [True, False])
    @pytest.mark.parametrize("concat_skip", [True, False])
    def test_call(
        self,
        image_size: Tuple,
        depth: int,
        pooling: bool,
        concat_skip: bool,
    ):
        """
        Test unet call function.

        :param image_size: (dim1, dim2, dim3), dims of input image.
        :param depth: input is at level 0, bottom is at level depth
        :param pooling: for down-sampling, use non-parameterized
                        pooling if true, otherwise use conv3d
        :param concat_skip: if concatenate skip or add it
        """
        out_ch = 3
        network = UNet(
            image_size=image_size,
            out_channels=out_ch,
            num_channel_initial=2,
            depth=depth,
            out_kernel_initializer="he_normal",
            out_activation="softmax",
            pooling=pooling,
            concat_skip=concat_skip,
        )
        inputs = tf.ones(shape=(5, *image_size, out_ch))
        output = network.call(inputs)
        assert inputs.shape == output.shape

    def test_get_config(self):

        config = dict(
            image_size=(4, 5, 6),
            out_channels=3,
            num_channel_initial=2,
            depth=2,
            extract_levels=(0, 1),
            out_kernel_initializer="he_normal",
            out_activation="softmax",
            pooling=False,
            concat_skip=False,
            encode_kernel_sizes=3,
            decode_kernel_sizes=3,
            encode_num_channels=(2, 4, 8),
            decode_num_channels=(2, 4, 8),
            strides=2,
            padding="same",
            name="Test",
        )
        network = UNet(**config)
        got = network.get_config()
        assert got == config


1		# coding=utf-8
2
3		"""
4		Tests for deepreg/model/backbone/u_net.py
5		"""
6		from typing import Tuple
7
8		import pytest
9		import tensorflow as tf
10
11		from deepreg.model.backbone.u_net import UNet
12
13
14		class TestUNet:
15		"""
16		Test the backbone.u_net.UNet class
17		"""
18
19		@pytest.mark.parametrize(
20		"depth,encode_num_channels,decode_num_channels",
21		[
22		(2, (4, 8, 16), (4, 8, 16)),
23		(2, (4, 8, 8), (4, 8, 8)),
24		(2, (4, 8, 8), (8, 8, 8)),
25		],
26		)
27		@pytest.mark.parametrize("pooling", [True, False])
		0 ignored issues – show introduced 2021-02-20 23:13 UTC by Report Bug Copy Issue Report "ValueError" not documented as being raised Loading history...
28		@pytest.mark.parametrize("concat_skip", [True, False])
29		def test_channels(
30		self,
31		depth: int,
32		encode_num_channels: Tuple,
33		decode_num_channels: Tuple,
34		pooling: bool,
35		concat_skip: bool,
36		):
37		"""
38		Test unet with custom encode/decode channels.
39
40		:param depth: input is at level 0, bottom is at level depth
41		:param encode_num_channels: filters/channels for down-sampling,
42		by default it is doubled at each layer during down-sampling
43		:param decode_num_channels: filters/channels for up-sampling,
44		by default it is the same as encode_num_channels
45		:param pooling: for down-sampling, use non-parameterized
46		pooling if true, otherwise use conv3d
47		:param concat_skip: if concatenate skip or add it
48		"""
49		# in case of adding skip tensors, the channels should match
50		expect_err = (not concat_skip) and encode_num_channels != decode_num_channels
51
52		image_size = (5, 6, 7)
53		out_ch = 3
54		try:
55		network = UNet(
56		image_size=image_size,
57		out_channels=out_ch,
58		num_channel_initial=None,
59		encode_num_channels=encode_num_channels,
60		decode_num_channels=decode_num_channels,
61		depth=depth,
62		out_kernel_initializer="he_normal",
63		out_activation="softmax",
64		pooling=pooling,
65		concat_skip=concat_skip,
66		)
67		except ValueError as err:
68		if expect_err:
69		return
70		raise err
71		inputs = tf.ones(shape=(5, *image_size, out_ch))
72
73		output = network.call(inputs)
74		assert inputs.shape == output.shape
75
76		@pytest.mark.parametrize(
77		"image_size,depth",
78		[((11, 12, 13), 5), ((8, 8, 8), 3)],
79		)
80		@pytest.mark.parametrize("pooling", [True, False])
81		@pytest.mark.parametrize("concat_skip", [True, False])
82		def test_call(
83		self,
84		image_size: Tuple,
85		depth: int,
86		pooling: bool,
87		concat_skip: bool,
88		):
89		"""
90		Test unet call function.
91
92		:param image_size: (dim1, dim2, dim3), dims of input image.
93		:param depth: input is at level 0, bottom is at level depth
94		:param pooling: for down-sampling, use non-parameterized
95		pooling if true, otherwise use conv3d
96		:param concat_skip: if concatenate skip or add it
97		"""
98		out_ch = 3
99		network = UNet(
100		image_size=image_size,
101		out_channels=out_ch,
102		num_channel_initial=2,
103		depth=depth,
104		out_kernel_initializer="he_normal",
105		out_activation="softmax",
106		pooling=pooling,
107		concat_skip=concat_skip,
108		)
109		inputs = tf.ones(shape=(5, *image_size, out_ch))
110		output = network.call(inputs)
111		assert inputs.shape == output.shape
112
113	View Code Duplication	def test_get_config(self):
		0 ignored issues – show introduced 2021-02-20 14:46 UTC by Report Bug Copy Issue Report Missing function or method docstring Loading history... Duplication introduced 2021-02-20 14:46 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
114		config = dict(
115		image_size=(4, 5, 6),
116		out_channels=3,
117		num_channel_initial=2,
118		depth=2,
119		extract_levels=(0, 1),
120		out_kernel_initializer="he_normal",
121		out_activation="softmax",
122		pooling=False,
123		concat_skip=False,
124		encode_kernel_sizes=3,
125		decode_kernel_sizes=3,
126		encode_num_channels=(2, 4, 8),
127		decode_num_channels=(2, 4, 8),
128		strides=2,
129		padding="same",
130		name="Test",
131		)
132		network = UNet(**config)
133		got = network.get_config()
134		assert got == config
135

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TestUNet.test_channels() B

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