File size: 9,644 Bytes
c59c099
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
import re
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
# Warning: spectral norm could be buggy
# under eval mode and multi-GPU inference
# A workaround is sticking to single-GPU inference and train mode
from torch.nn.utils import spectral_norm


class SPADE(nn.Module):

    def __init__(self, config_text, norm_nc, label_nc):
        super().__init__()

        assert config_text.startswith('spade')
        parsed = re.search('spade(\\D+)(\\d)x\\d', config_text)
        param_free_norm_type = str(parsed.group(1))
        ks = int(parsed.group(2))

        if param_free_norm_type == 'instance':
            self.param_free_norm = nn.InstanceNorm2d(norm_nc)
        elif param_free_norm_type == 'syncbatch':
            print('SyncBatchNorm is currently not supported under single-GPU mode, switch to "instance" instead')
            self.param_free_norm = nn.InstanceNorm2d(norm_nc)
        elif param_free_norm_type == 'batch':
            self.param_free_norm = nn.BatchNorm2d(norm_nc, affine=False)
        else:
            raise ValueError(f'{param_free_norm_type} is not a recognized param-free norm type in SPADE')

        # The dimension of the intermediate embedding space. Yes, hardcoded.
        nhidden = 128 if norm_nc > 128 else norm_nc

        pw = ks // 2
        self.mlp_shared = nn.Sequential(nn.Conv2d(label_nc, nhidden, kernel_size=ks, padding=pw), nn.ReLU())
        self.mlp_gamma = nn.Conv2d(nhidden, norm_nc, kernel_size=ks, padding=pw, bias=False)
        self.mlp_beta = nn.Conv2d(nhidden, norm_nc, kernel_size=ks, padding=pw, bias=False)

    def forward(self, x, segmap):

        # Part 1. generate parameter-free normalized activations
        normalized = self.param_free_norm(x)

        # Part 2. produce scaling and bias conditioned on semantic map
        segmap = F.interpolate(segmap, size=x.size()[2:], mode='nearest')
        actv = self.mlp_shared(segmap)
        gamma = self.mlp_gamma(actv)
        beta = self.mlp_beta(actv)

        # apply scale and bias
        out = normalized * gamma + beta

        return out


class SPADEResnetBlock(nn.Module):
    """
    ResNet block that uses SPADE. It differs from the ResNet block of pix2pixHD in that
    it takes in the segmentation map as input, learns the skip connection if necessary,
    and applies normalization first and then convolution.
    This architecture seemed like a standard architecture for unconditional or
    class-conditional GAN architecture using residual block.
    The code was inspired from https://github.com/LMescheder/GAN_stability.
    """

    def __init__(self, fin, fout, norm_g='spectralspadesyncbatch3x3', semantic_nc=3):
        super().__init__()
        # Attributes
        self.learned_shortcut = (fin != fout)
        fmiddle = min(fin, fout)

        # create conv layers
        self.conv_0 = nn.Conv2d(fin, fmiddle, kernel_size=3, padding=1)
        self.conv_1 = nn.Conv2d(fmiddle, fout, kernel_size=3, padding=1)
        if self.learned_shortcut:
            self.conv_s = nn.Conv2d(fin, fout, kernel_size=1, bias=False)

        # apply spectral norm if specified
        if 'spectral' in norm_g:
            self.conv_0 = spectral_norm(self.conv_0)
            self.conv_1 = spectral_norm(self.conv_1)
            if self.learned_shortcut:
                self.conv_s = spectral_norm(self.conv_s)

        # define normalization layers
        spade_config_str = norm_g.replace('spectral', '')
        self.norm_0 = SPADE(spade_config_str, fin, semantic_nc)
        self.norm_1 = SPADE(spade_config_str, fmiddle, semantic_nc)
        if self.learned_shortcut:
            self.norm_s = SPADE(spade_config_str, fin, semantic_nc)

    # note the resnet block with SPADE also takes in |seg|,
    # the semantic segmentation map as input
    def forward(self, x, seg):
        x_s = self.shortcut(x, seg)
        dx = self.conv_0(self.act(self.norm_0(x, seg)))
        dx = self.conv_1(self.act(self.norm_1(dx, seg)))
        out = x_s + dx
        return out

    def shortcut(self, x, seg):
        if self.learned_shortcut:
            x_s = self.conv_s(self.norm_s(x, seg))
        else:
            x_s = x
        return x_s

    def act(self, x):
        return F.leaky_relu(x, 2e-1)


class BaseNetwork(nn.Module):
    """ A basis for hifacegan archs with custom initialization """

    def init_weights(self, init_type='normal', gain=0.02):

        def init_func(m):
            classname = m.__class__.__name__
            if classname.find('BatchNorm2d') != -1:
                if hasattr(m, 'weight') and m.weight is not None:
                    init.normal_(m.weight.data, 1.0, gain)
                if hasattr(m, 'bias') and m.bias is not None:
                    init.constant_(m.bias.data, 0.0)
            elif hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):
                if init_type == 'normal':
                    init.normal_(m.weight.data, 0.0, gain)
                elif init_type == 'xavier':
                    init.xavier_normal_(m.weight.data, gain=gain)
                elif init_type == 'xavier_uniform':
                    init.xavier_uniform_(m.weight.data, gain=1.0)
                elif init_type == 'kaiming':
                    init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
                elif init_type == 'orthogonal':
                    init.orthogonal_(m.weight.data, gain=gain)
                elif init_type == 'none':  # uses pytorch's default init method
                    m.reset_parameters()
                else:
                    raise NotImplementedError(f'initialization method [{init_type}] is not implemented')
                if hasattr(m, 'bias') and m.bias is not None:
                    init.constant_(m.bias.data, 0.0)

        self.apply(init_func)

        # propagate to children
        for m in self.children():
            if hasattr(m, 'init_weights'):
                m.init_weights(init_type, gain)

    def forward(self, x):
        pass


def lip2d(x, logit, kernel=3, stride=2, padding=1):
    weight = logit.exp()
    return F.avg_pool2d(x * weight, kernel, stride, padding) / F.avg_pool2d(weight, kernel, stride, padding)


class SoftGate(nn.Module):
    COEFF = 12.0

    def forward(self, x):
        return torch.sigmoid(x).mul(self.COEFF)


class SimplifiedLIP(nn.Module):

    def __init__(self, channels):
        super(SimplifiedLIP, self).__init__()
        self.logit = nn.Sequential(
            nn.Conv2d(channels, channels, 3, padding=1, bias=False), nn.InstanceNorm2d(channels, affine=True),
            SoftGate())

    def init_layer(self):
        self.logit[0].weight.data.fill_(0.0)

    def forward(self, x):
        frac = lip2d(x, self.logit(x))
        return frac


class LIPEncoder(BaseNetwork):
    """Local Importance-based Pooling (Ziteng Gao et.al.,ICCV 2019)"""

    def __init__(self, input_nc, ngf, sw, sh, n_2xdown, norm_layer=nn.InstanceNorm2d):
        super().__init__()
        self.sw = sw
        self.sh = sh
        self.max_ratio = 16
        # 20200310: Several Convolution (stride 1) + LIP blocks, 4 fold
        kw = 3
        pw = (kw - 1) // 2

        model = [
            nn.Conv2d(input_nc, ngf, kw, stride=1, padding=pw, bias=False),
            norm_layer(ngf),
            nn.ReLU(),
        ]
        cur_ratio = 1
        for i in range(n_2xdown):
            next_ratio = min(cur_ratio * 2, self.max_ratio)
            model += [
                SimplifiedLIP(ngf * cur_ratio),
                nn.Conv2d(ngf * cur_ratio, ngf * next_ratio, kw, stride=1, padding=pw),
                norm_layer(ngf * next_ratio),
            ]
            cur_ratio = next_ratio
            if i < n_2xdown - 1:
                model += [nn.ReLU(inplace=True)]

        self.model = nn.Sequential(*model)

    def forward(self, x):
        return self.model(x)


def get_nonspade_norm_layer(norm_type='instance'):
    # helper function to get # output channels of the previous layer
    def get_out_channel(layer):
        if hasattr(layer, 'out_channels'):
            return getattr(layer, 'out_channels')
        return layer.weight.size(0)

    # this function will be returned
    def add_norm_layer(layer):
        nonlocal norm_type
        if norm_type.startswith('spectral'):
            layer = spectral_norm(layer)
            subnorm_type = norm_type[len('spectral'):]

        if subnorm_type == 'none' or len(subnorm_type) == 0:
            return layer

        # remove bias in the previous layer, which is meaningless
        # since it has no effect after normalization
        if getattr(layer, 'bias', None) is not None:
            delattr(layer, 'bias')
            layer.register_parameter('bias', None)

        if subnorm_type == 'batch':
            norm_layer = nn.BatchNorm2d(get_out_channel(layer), affine=True)
        elif subnorm_type == 'sync_batch':
            print('SyncBatchNorm is currently not supported under single-GPU mode, switch to "instance" instead')
            # norm_layer = SynchronizedBatchNorm2d(
            #    get_out_channel(layer), affine=True)
            norm_layer = nn.InstanceNorm2d(get_out_channel(layer), affine=False)
        elif subnorm_type == 'instance':
            norm_layer = nn.InstanceNorm2d(get_out_channel(layer), affine=False)
        else:
            raise ValueError(f'normalization layer {subnorm_type} is not recognized')

        return nn.Sequential(layer, norm_layer)

    print('This is a legacy from nvlabs/SPADE, and will be removed in future versions.')
    return add_norm_layer