Skip to content

GitLab

Switch branch/tag
Find file Normal viewHistoryPermalink
test_gen_auxiliary_loss.py 7.34 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
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200 
# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch

from mmgen.models.architectures.pix2pix import UnetGenerator
from mmgen.models.architectures.stylegan import StyleGANv2Generator
from mmgen.models.losses import GeneratorPathRegularizer, PerceptualLoss
from mmgen.models.losses.pixelwise_loss import l1_loss, mse_loss


class TestPathRegularizer:

    @classmethod
    def setup_class(cls):
        cls.data_info = dict(generator='generator', num_batches='num_batches')
        cls.gen = StyleGANv2Generator(32, 10, num_mlps=2)

    def test_path_regularizer_cpu(self):
        gen = self.gen

        output_dict = dict(generator=gen, num_batches=2)
        pl = GeneratorPathRegularizer(data_info=self.data_info)
        pl_loss = pl(output_dict)
        assert pl_loss > 0

        output_dict = dict(generator=gen, num_batches=2, iteration=3)
        pl = GeneratorPathRegularizer(data_info=self.data_info, interval=2)
        pl_loss = pl(outputs_dict=output_dict)
        assert pl_loss is None

        with pytest.raises(NotImplementedError):
            _ = pl(asdf=1.)

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

        with pytest.raises(AssertionError):
            _ = pl(1., 2, outputs_dict=output_dict)

    @pytest.mark.skipif(
        not torch.cuda.is_available()
        or not hasattr(torch.backends.cudnn, 'allow_tf32'),
        reason='requires cuda')
    def test_path_regularizer_cuda(self):
        gen = self.gen.cuda()

        output_dict = dict(generator=gen, num_batches=2)
        pl = GeneratorPathRegularizer(data_info=self.data_info).cuda()
        pl_loss = pl(output_dict)
        assert pl_loss > 0

        output_dict = dict(generator=gen, num_batches=2, iteration=3)
        pl = GeneratorPathRegularizer(
            data_info=self.data_info, interval=2).cuda()
        pl_loss = pl(outputs_dict=output_dict)
        assert pl_loss is None

        with pytest.raises(NotImplementedError):
            _ = pl(asdf=1.)

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

        with pytest.raises(AssertionError):
            _ = pl(1., 2, outputs_dict=output_dict)


class TestPerceptualLoss:

    @classmethod
    def setup_class(cls):
        cls.data_info = dict(pred='fake_imgs', target='real_imgs')
        cls.gen = UnetGenerator(3, 3)

    def test_perceptual_loss_cpu(self):
        unknown_h, unknown_w = (32, 32)
        weight = torch.zeros(1, 1, 64, 64)
        weight[0, 0, :unknown_h, :unknown_w] = 1
        pred = weight.clone()
        target = weight.clone() * 2

        perceptual_loss = PerceptualLoss(data_info=self.data_info)
        loss_perceptual = perceptual_loss(
            outputs_dict=dict(fake_imgs=pred, real_imgs=target))
        assert loss_perceptual.shape == ()
        assert id(perceptual_loss.criterion) == id(l1_loss)

    def test_only_perceptual_loss(self):
        unknown_h, unknown_w = (32, 32)
        weight = torch.zeros(1, 1, 64, 64)
        weight[0, 0, :unknown_h, :unknown_w] = 1
        pred = weight.clone()
        target = weight.clone() * 2

        perceptual_loss = PerceptualLoss(
            data_info=self.data_info, style_weight=0)
        loss_percep = perceptual_loss(dict(fake_imgs=pred, real_imgs=target))
        assert loss_percep.shape == ()
        assert perceptual_loss.style_weight == 0

    def test_only_style_loss(self):
        unknown_h, unknown_w = (32, 32)
        weight = torch.zeros(1, 1, 64, 64)
        weight[0, 0, :unknown_h, :unknown_w] = 1
        pred = weight.clone()
        target = weight.clone() * 2

        perceptual_loss = PerceptualLoss(
            data_info=self.data_info, perceptual_weight=0)
        loss_style = perceptual_loss(dict(fake_imgs=pred, real_imgs=target))
        assert loss_style.shape == ()
        assert perceptual_loss.perceptual_weight == 0

    def test_with_different_layer_weights(self):
        unknown_h, unknown_w = (32, 32)
        weight = torch.zeros(1, 1, 64, 64)
        weight[0, 0, :unknown_h, :unknown_w] = 1
        pred = weight.clone()
        target = weight.clone() * 2

        layer_weights = {'1': 1., '2': 2., '3': 3.}
        perceptual_loss = PerceptualLoss(
            data_info=self.data_info, layer_weights=layer_weights)
        loss_perceptual = perceptual_loss(
            dict(fake_imgs=pred, real_imgs=target))
        assert loss_perceptual.shape == ()
        assert perceptual_loss.layer_weights == layer_weights and \
            perceptual_loss.layer_weights_style == layer_weights

    def test_with_different_perceptual_and_style_layers(self):
        unknown_h, unknown_w = (32, 32)
        weight = torch.zeros(1, 1, 64, 64)
        weight[0, 0, :unknown_h, :unknown_w] = 1
        pred = weight.clone()
        target = weight.clone() * 2

        layer_weights = {'1': 1., '2': 2., '3': 3.}
        layer_weights_style = {'4': 4., '5': 5., '6': 6.}
        perceptual_loss = PerceptualLoss(
            data_info=self.data_info,
            layer_weights=layer_weights,
            layer_weights_style=layer_weights_style)
        loss_perceptual = perceptual_loss(
            dict(fake_imgs=pred, real_imgs=target))
        assert loss_perceptual.shape == ()
        assert perceptual_loss.layer_weights == layer_weights and \
            perceptual_loss.layer_weights_style == layer_weights_style

    def test_MSE_critierion(self):
        unknown_h, unknown_w = (32, 32)
        weight = torch.zeros(1, 1, 64, 64)
        weight[0, 0, :unknown_h, :unknown_w] = 1
        pred = weight.clone()
        target = weight.clone() * 2

        perceptual_loss = PerceptualLoss(
            data_info=self.data_info, criterion='mse')
        loss_perceptual = perceptual_loss(
            outputs_dict=dict(fake_imgs=pred, real_imgs=target))
        assert loss_perceptual.shape == ()
        assert id(perceptual_loss.criterion) == id(mse_loss)

    def test_VGG_16(self):
        unknown_h, unknown_w = (32, 32)
        weight = torch.zeros(1, 1, 64, 64)
        weight[0, 0, :unknown_h, :unknown_w] = 1
        pred = weight.clone()
        target = weight.clone() * 2

        perceptual_loss = PerceptualLoss(
            data_info=self.data_info,
            vgg_type='vgg16',
            pretrained='torchvision://vgg16')
        loss_perceptual = perceptual_loss(
            outputs_dict=dict(fake_imgs=pred, real_imgs=target))
        assert loss_perceptual.shape == ()
        # TODO need to check whether vgg16 is loaded
        # assert perceptual_loss.vgg

    def test_split_style_loss(self):
        unknown_h, unknown_w = (32, 32)
        weight = torch.zeros(1, 1, 64, 64)
        weight[0, 0, :unknown_h, :unknown_w] = 1
        pred = weight.clone()
        target = weight.clone() * 2

        perceptual_loss = PerceptualLoss(
            data_info=self.data_info, split_style_loss=True)
        loss_percep, loss_style = perceptual_loss(
            outputs_dict=dict(fake_imgs=pred, real_imgs=target))
        assert loss_percep.shape == () and loss_style.shape == ()

    @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')
    def test_perceptual_loss_cuda(self):
        pred = torch.rand([2, 3, 256, 256]).cuda()
        target = torch.rand_like(pred).cuda()
        perceptual_loss = PerceptualLoss(data_info=self.data_info).cuda()
        loss_perceptual = perceptual_loss(
            outputs_dict=dict(fake_imgs=pred, real_imgs=target))
        assert loss_perceptual.shape == ()