Skip to content

GitLab

Switch branch/tag
Find file Normal viewHistoryPermalink
test_disc_auxilary_loss.py 6.05 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
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185 
# Copyright (c) OpenMMLab. All rights reserved.
from functools import partial

import pytest
import torch

from mmgen.models.architectures import DCGANDiscriminator
from mmgen.models.architectures.pggan.generator_discriminator import \
    PGGANDiscriminator
from mmgen.models.losses import (DiscShiftLoss, GradientPenaltyLoss,
                                 disc_shift_loss, gradient_penalty_loss)
from mmgen.models.losses.disc_auxiliary_loss import (R1GradientPenalty,
                                                     r1_gradient_penalty_loss)


class TestDiscShiftLoss(object):

    @classmethod
    def setup_class(cls):
        cls.input_tensor = torch.randn((2, 10))
        cls.default_cfg = dict(
            loss_weight=0.1, data_info=dict(pred='disc_pred'))
        cls.default_input_dict = dict(disc_pred=cls.input_tensor)

    def test_disc_shift_loss(self):
        loss = disc_shift_loss(self.input_tensor)
        assert loss.ndim == 0
        assert loss >= 0

        loss = disc_shift_loss(self.input_tensor, weight=-0.1)
        assert loss.ndim == 0
        assert loss <= 0

        loss = disc_shift_loss(self.input_tensor, reduction='none')
        assert loss.ndim == 2
        assert (loss >= 0).all()

        loss_sum = disc_shift_loss(self.input_tensor, reduction='sum')
        loss_avg = disc_shift_loss(self.input_tensor, avg_factor=1000)
        assert loss_avg.ndim == 0 and loss_sum.ndim == 0
        assert loss_sum > loss_avg

        with pytest.raises(ValueError):
            _ = disc_shift_loss(
                self.input_tensor, reduction='sum', avg_factor=100)

    def test_module_wrapper(self):
        # test with default config
        loss_module = DiscShiftLoss(**self.default_cfg)
        loss = loss_module(self.default_input_dict)
        assert loss.ndim == 0

        with pytest.raises(NotImplementedError):
            _ = loss_module(self.default_input_dict, 1)

        with pytest.raises(AssertionError):
            _ = loss_module(1, outputs_dict=self.default_input_dict)
        input_ = dict(outputs_dict=self.default_input_dict)
        loss = loss_module(**input_)
        assert loss.ndim == 0

        with pytest.raises(AssertionError):
            _ = loss_module(self.input_tensor)

        # test without data_info
        loss_module = DiscShiftLoss(data_info=None)
        loss = loss_module(self.input_tensor)
        assert loss.ndim == 0


class TestGradientPenalty:

    @classmethod
    def setup_class(cls):
        cls.input_img = torch.randn((2, 3, 8, 8))
        cls.disc = DCGANDiscriminator(
            input_scale=8, output_scale=4, out_channels=5)
        cls.pggan_disc = PGGANDiscriminator(
            in_scale=8, base_channels=32, max_channels=32)
        cls.data_info = dict(
            discriminator='disc', real_data='real_imgs', fake_data='fake_imgs')

    def test_gp_loss(self):
        loss = gradient_penalty_loss(self.disc, self.input_img, self.input_img)
        assert loss > 0

        loss = gradient_penalty_loss(
            self.disc, self.input_img, self.input_img, norm_mode='HWC')
        assert loss > 0

        with pytest.raises(NotImplementedError):
            _ = gradient_penalty_loss(
                self.disc, self.input_img, self.input_img, norm_mode='xxx')

        loss = gradient_penalty_loss(
            self.disc,
            self.input_img,
            self.input_img,
            norm_mode='HWC',
            weight=10)
        assert loss > 0

        loss = gradient_penalty_loss(
            self.disc,
            self.input_img,
            self.input_img,
            norm_mode='HWC',
            mask=torch.ones_like(self.input_img),
            weight=10)
        assert loss > 0

        data_dict = dict(
            real_imgs=self.input_img,
            fake_imgs=self.input_img,
            disc=partial(self.pggan_disc, transition_weight=0.5, curr_scale=8))
        gp_loss = GradientPenaltyLoss(
            loss_weight=10, norm_mode='pixel', data_info=self.data_info)

        loss = gp_loss(data_dict)
        assert loss > 0
        loss = gp_loss(outputs_dict=data_dict)
        assert loss > 0
        with pytest.raises(NotImplementedError):
            _ = gp_loss(asdf=1.)

        with pytest.raises(AssertionError):
            _ = gp_loss(1.)

        with pytest.raises(AssertionError):
            _ = gp_loss(1., 2, outputs_dict=data_dict)


class TestR1GradientPenalty:

    @classmethod
    def setup_class(cls):
        cls.data_info = dict(discriminator='disc', real_data='real_imgs')
        cls.disc = DCGANDiscriminator(
            input_scale=8, output_scale=4, out_channels=5)
        cls.pggan_disc = PGGANDiscriminator(
            in_scale=8, base_channels=32, max_channels=32)
        cls.input_img = torch.randn((2, 3, 8, 8))

    def test_r1_regularizer(self):
        loss = r1_gradient_penalty_loss(self.disc, self.input_img)
        assert loss > 0

        loss = r1_gradient_penalty_loss(
            self.disc, self.input_img, norm_mode='HWC')
        assert loss > 0

        with pytest.raises(NotImplementedError):
            _ = r1_gradient_penalty_loss(
                self.disc, self.input_img, norm_mode='xxx')

        loss = r1_gradient_penalty_loss(
            self.disc, self.input_img, norm_mode='HWC', weight=10)
        assert loss > 0

        loss = r1_gradient_penalty_loss(
            self.disc,
            self.input_img,
            norm_mode='HWC',
            mask=torch.ones_like(self.input_img),
            weight=10)
        assert loss > 0

        data_dict = dict(
            real_imgs=self.input_img,
            disc=partial(self.pggan_disc, transition_weight=0.5, curr_scale=8))
        gp_loss = R1GradientPenalty(
            loss_weight=10, norm_mode='pixel', data_info=self.data_info)

        loss = gp_loss(data_dict)
        assert loss > 0
        loss = gp_loss(outputs_dict=data_dict)
        assert loss > 0
        with pytest.raises(NotImplementedError):
            _ = gp_loss(asdf=1.)

        with pytest.raises(AssertionError):
            _ = gp_loss(1.)

        with pytest.raises(AssertionError):
            _ = gp_loss(1., 2, outputs_dict=data_dict)