Skip to content

GitLab

Switch branch/tag
Find file Normal viewHistoryPermalink
test_singan_archs.py 4.49 KB
Newer Older
limm's avatar
add tests code  
limm committed
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146 
# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch

from mmgen.models.architectures.singan import (SinGANMSGeneratorPE,
                                               SinGANMultiScaleDiscriminator,
                                               SinGANMultiScaleGenerator)


class TestSinGANGen:

    @classmethod
    def setup_class(cls):
        cls.default_args = dict(
            in_channels=3,
            out_channels=3,
            kernel_size=3,
            padding=0,
            num_layers=3,
            base_channels=32,
            num_scales=3,
            min_feat_channels=16)

        cls.fixed_noises = [
            torch.randn(1, 3, 8, 8),
            torch.randn(1, 3, 10, 10),
            torch.randn(1, 3, 12, 12),
            torch.randn(1, 3, 16, 16)
        ]
        cls.input_sample = torch.zeros_like(cls.fixed_noises[0])
        cls.noise_weights = [1., 0.5, 0.5, 0.5]

    def test_singan_gen(self):
        gen = SinGANMultiScaleGenerator(**self.default_args)
        res = gen(self.input_sample, self.fixed_noises, self.noise_weights,
                  'rand', 2)
        assert res.shape == (1, 3, 12, 12)

        output = gen(
            self.input_sample,
            self.fixed_noises,
            self.noise_weights,
            'rand',
            2,
            get_prev_res=True)

        assert output['prev_res_list'][0].shape == (1, 3, 8, 8)


class TestSinGANPEGen:

    @classmethod
    def setup_class(cls):
        cls.default_args = dict(
            in_channels=3,
            out_channels=3,
            kernel_size=3,
            num_layers=3,
            base_channels=32,
            num_scales=3,
            min_feat_channels=16)

        cls.fixed_noises = [
            torch.randn(1, 1, 8, 8),
            torch.randn(1, 3, 10, 10),
            torch.randn(1, 3, 12, 12),
            torch.randn(1, 3, 16, 16)
        ]
        cls.input_sample = torch.zeros((1, 3, 8, 8))
        cls.noise_weights = [1., 0.5, 0.5, 0.5]

    def test_singan_gen_pe(self):
        gen = SinGANMSGeneratorPE(**self.default_args)
        res = gen(self.input_sample, self.fixed_noises, self.noise_weights,
                  'rand', 2)
        assert res.shape == (1, 3, 12, 12)

        output = gen(
            self.input_sample,
            self.fixed_noises,
            self.noise_weights,
            'rand',
            2,
            get_prev_res=True)

        assert output['prev_res_list'][0].shape == (1, 3, 8, 8)

        gen = SinGANMSGeneratorPE(padding_mode='reflect', **self.default_args)
        res = gen(self.input_sample, self.fixed_noises, self.noise_weights,
                  'rand', 2)
        assert res.shape == (1, 3, 12, 12)

        with pytest.raises(NotImplementedError):
            _ = SinGANMSGeneratorPE(
                padding_mode='circular', **self.default_args)

        gen = SinGANMSGeneratorPE(
            padding=1, pad_at_head=False, **self.default_args)
        res = gen(self.input_sample, self.fixed_noises, self.noise_weights,
                  'rand', 2)
        assert res.shape == (1, 3, 12, 12)

        gen = SinGANMSGeneratorPE(
            pad_at_head=True, interp_pad=True, **self.default_args)
        res = gen(self.input_sample, self.fixed_noises, self.noise_weights,
                  'rand', 2)
        assert res.shape == (1, 3, 12, 12)

        gen = SinGANMSGeneratorPE(
            positional_encoding=dict(
                type='SPE2d', embedding_dim=4, padding_idx=0),
            allow_no_residual=True,
            first_stage_in_channels=8,
            **self.default_args)
        res = gen(self.input_sample, self.fixed_noises, self.noise_weights,
                  'rand', 2)
        assert res.shape == (1, 3, 12, 12)

        gen = SinGANMSGeneratorPE(
            positional_encoding=dict(type='CSG2d'),
            allow_no_residual=True,
            first_stage_in_channels=2,
            **self.default_args)
        res = gen(self.input_sample, self.fixed_noises, self.noise_weights,
                  'rand', 2)
        assert res.shape == (1, 3, 12, 12)


class TestSinGANDisc:

    @classmethod
    def setup_class(cls):
        cls.default_args = dict(
            in_channels=3,
            kernel_size=3,
            padding=0,
            num_layers=3,
            base_channels=32,
            num_scales=3,
            min_feat_channels=16)

    def test_singan_disc(self):
        disc = SinGANMultiScaleDiscriminator(**self.default_args)
        img = torch.randn(1, 3, 24, 24)
        res = disc(img, 2)
        assert res.shape[0] == 1