File size: 14,659 Bytes
e64d6ac
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
import sys
import math

import torch
from torch import nn
from torch.nn import functional as F
from torch.autograd import Function
from torch.nn.utils.spectral_norm import spectral_norm as SpectralNorm


class LayerNorm2d(nn.Module):
    def __init__(self, n_out, affine=True):
        super(LayerNorm2d, self).__init__()
        self.n_out = n_out
        self.affine = affine

        if self.affine:
          self.weight = nn.Parameter(torch.ones(n_out, 1, 1))
          self.bias = nn.Parameter(torch.zeros(n_out, 1, 1))

    def forward(self, x):
        normalized_shape = x.size()[1:]
        if self.affine:
          return F.layer_norm(x, normalized_shape, \
              self.weight.expand(normalized_shape), 
              self.bias.expand(normalized_shape))
              
        else:
          return F.layer_norm(x, normalized_shape)  

class ADAINHourglass(nn.Module):
    def __init__(self, image_nc, pose_nc, ngf, img_f, encoder_layers, decoder_layers, nonlinearity, use_spect):
        super(ADAINHourglass, self).__init__()
        self.encoder = ADAINEncoder(image_nc, pose_nc, ngf, img_f, encoder_layers, nonlinearity, use_spect)
        self.decoder = ADAINDecoder(pose_nc, ngf, img_f, encoder_layers, decoder_layers, True, nonlinearity, use_spect)
        self.output_nc = self.decoder.output_nc

    def forward(self, x, z):
        return self.decoder(self.encoder(x, z), z)                 



class ADAINEncoder(nn.Module):
    def __init__(self, image_nc, pose_nc, ngf, img_f, layers, nonlinearity=nn.LeakyReLU(), use_spect=False):
        super(ADAINEncoder, self).__init__()
        self.layers = layers
        self.input_layer = nn.Conv2d(image_nc, ngf, kernel_size=7, stride=1, padding=3)
        for i in range(layers):
            in_channels = min(ngf * (2**i), img_f)
            out_channels = min(ngf *(2**(i+1)), img_f)
            model = ADAINEncoderBlock(in_channels, out_channels, pose_nc, nonlinearity, use_spect)
            setattr(self, 'encoder' + str(i), model)
        self.output_nc = out_channels
        
    def forward(self, x, z):
        out = self.input_layer(x)
        out_list = [out]
        for i in range(self.layers):
            model = getattr(self, 'encoder' + str(i))
            out = model(out, z)
            out_list.append(out)
        return out_list
        
class ADAINDecoder(nn.Module):
    """docstring for ADAINDecoder"""
    def __init__(self, pose_nc, ngf, img_f, encoder_layers, decoder_layers, skip_connect=True, 
                 nonlinearity=nn.LeakyReLU(), use_spect=False):

        super(ADAINDecoder, self).__init__()
        self.encoder_layers = encoder_layers
        self.decoder_layers = decoder_layers
        self.skip_connect = skip_connect
        use_transpose = True

        for i in range(encoder_layers-decoder_layers, encoder_layers)[::-1]:
            in_channels = min(ngf * (2**(i+1)), img_f)
            in_channels = in_channels*2 if i != (encoder_layers-1) and self.skip_connect else in_channels
            out_channels = min(ngf * (2**i), img_f)
            model = ADAINDecoderBlock(in_channels, out_channels, out_channels, pose_nc, use_transpose, nonlinearity, use_spect)
            setattr(self, 'decoder' + str(i), model)

        self.output_nc = out_channels*2 if self.skip_connect else out_channels

    def forward(self, x, z):
        out = x.pop() if self.skip_connect else x
        for i in range(self.encoder_layers-self.decoder_layers, self.encoder_layers)[::-1]:
            model = getattr(self, 'decoder' + str(i))
            out = model(out, z)
            out = torch.cat([out, x.pop()], 1) if self.skip_connect else out
        return out

class ADAINEncoderBlock(nn.Module):       
    def __init__(self, input_nc, output_nc, feature_nc, nonlinearity=nn.LeakyReLU(), use_spect=False):
        super(ADAINEncoderBlock, self).__init__()
        kwargs_down = {'kernel_size': 4, 'stride': 2, 'padding': 1}
        kwargs_fine = {'kernel_size': 3, 'stride': 1, 'padding': 1}

        self.conv_0 = spectral_norm(nn.Conv2d(input_nc,  output_nc, **kwargs_down), use_spect)
        self.conv_1 = spectral_norm(nn.Conv2d(output_nc, output_nc, **kwargs_fine), use_spect)


        self.norm_0 = ADAIN(input_nc, feature_nc)
        self.norm_1 = ADAIN(output_nc, feature_nc)
        self.actvn = nonlinearity

    def forward(self, x, z):
        x = self.conv_0(self.actvn(self.norm_0(x, z)))
        x = self.conv_1(self.actvn(self.norm_1(x, z)))
        return x

class ADAINDecoderBlock(nn.Module):
    def __init__(self, input_nc, output_nc, hidden_nc, feature_nc, use_transpose=True, nonlinearity=nn.LeakyReLU(), use_spect=False):
        super(ADAINDecoderBlock, self).__init__()        
        # Attributes
        self.actvn = nonlinearity
        hidden_nc = min(input_nc, output_nc) if hidden_nc is None else hidden_nc

        kwargs_fine = {'kernel_size':3, 'stride':1, 'padding':1}
        if use_transpose:
            kwargs_up = {'kernel_size':3, 'stride':2, 'padding':1, 'output_padding':1}
        else:
            kwargs_up = {'kernel_size':3, 'stride':1, 'padding':1}

        # create conv layers
        self.conv_0 = spectral_norm(nn.Conv2d(input_nc, hidden_nc, **kwargs_fine), use_spect)
        if use_transpose:
            self.conv_1 = spectral_norm(nn.ConvTranspose2d(hidden_nc, output_nc, **kwargs_up), use_spect)
            self.conv_s = spectral_norm(nn.ConvTranspose2d(input_nc, output_nc, **kwargs_up), use_spect)
        else:
            self.conv_1 = nn.Sequential(spectral_norm(nn.Conv2d(hidden_nc, output_nc, **kwargs_up), use_spect),
                                        nn.Upsample(scale_factor=2))
            self.conv_s = nn.Sequential(spectral_norm(nn.Conv2d(input_nc, output_nc, **kwargs_up), use_spect),
                                        nn.Upsample(scale_factor=2))
        # define normalization layers
        self.norm_0 = ADAIN(input_nc, feature_nc)
        self.norm_1 = ADAIN(hidden_nc, feature_nc)
        self.norm_s = ADAIN(input_nc, feature_nc)
        
    def forward(self, x, z):
        x_s = self.shortcut(x, z)
        dx = self.conv_0(self.actvn(self.norm_0(x, z)))
        dx = self.conv_1(self.actvn(self.norm_1(dx, z)))
        out = x_s + dx
        return out

    def shortcut(self, x, z):
        x_s = self.conv_s(self.actvn(self.norm_s(x, z)))
        return x_s              


def spectral_norm(module, use_spect=True):
    """use spectral normal layer to stable the training process"""
    if use_spect:
        return SpectralNorm(module)
    else:
        return module


class ADAIN(nn.Module):
    def __init__(self, norm_nc, feature_nc):
        super().__init__()

        self.param_free_norm = nn.InstanceNorm2d(norm_nc, affine=False)

        nhidden = 128
        use_bias=True

        self.mlp_shared = nn.Sequential(
            nn.Linear(feature_nc, nhidden, bias=use_bias),            
            nn.ReLU()
        )
        self.mlp_gamma = nn.Linear(nhidden, norm_nc, bias=use_bias)    
        self.mlp_beta = nn.Linear(nhidden, norm_nc, bias=use_bias)    

    def forward(self, x, feature):

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

        # Part 2. produce scaling and bias conditioned on feature
        feature = feature.view(feature.size(0), -1)
        actv = self.mlp_shared(feature)
        gamma = self.mlp_gamma(actv)
        beta = self.mlp_beta(actv)

        # apply scale and bias
        gamma = gamma.view(*gamma.size()[:2], 1,1)
        beta = beta.view(*beta.size()[:2], 1,1)
        out = normalized * (1 + gamma) + beta
        return out


class FineEncoder(nn.Module):
    """docstring for Encoder"""
    def __init__(self, image_nc, ngf, img_f, layers, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
        super(FineEncoder, self).__init__()
        self.layers = layers
        self.first = FirstBlock2d(image_nc, ngf, norm_layer, nonlinearity, use_spect)
        for i in range(layers):
            in_channels = min(ngf*(2**i), img_f)
            out_channels = min(ngf*(2**(i+1)), img_f)
            model = DownBlock2d(in_channels, out_channels, norm_layer, nonlinearity, use_spect)
            setattr(self, 'down' + str(i), model)
        self.output_nc = out_channels

    def forward(self, x):
        x = self.first(x)
        out=[x]
        for i in range(self.layers):
            model = getattr(self, 'down'+str(i))
            x = model(x)
            out.append(x)
        return out

class FineDecoder(nn.Module):
    """docstring for FineDecoder"""
    def __init__(self, image_nc, feature_nc, ngf, img_f, layers, num_block, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
        super(FineDecoder, self).__init__()
        self.layers = layers
        for i in range(layers)[::-1]:
            in_channels = min(ngf*(2**(i+1)), img_f)
            out_channels = min(ngf*(2**i), img_f)
            up = UpBlock2d(in_channels, out_channels, norm_layer, nonlinearity, use_spect)
            res = FineADAINResBlocks(num_block, in_channels, feature_nc, norm_layer, nonlinearity, use_spect)
            jump = Jump(out_channels, norm_layer, nonlinearity, use_spect)

            setattr(self, 'up' + str(i), up)
            setattr(self, 'res' + str(i), res)            
            setattr(self, 'jump' + str(i), jump)

        self.final = FinalBlock2d(out_channels, image_nc, use_spect, 'tanh')

        self.output_nc = out_channels

    def forward(self, x, z):
        out = x.pop()
        for i in range(self.layers)[::-1]:
            res_model = getattr(self, 'res' + str(i))
            up_model = getattr(self, 'up' + str(i))
            jump_model = getattr(self, 'jump' + str(i))
            out = res_model(out, z)
            out = up_model(out)
            out = jump_model(x.pop()) + out
        out_image = self.final(out)
        return out_image

class FirstBlock2d(nn.Module):
    """
    Downsampling block for use in encoder.
    """
    def __init__(self, input_nc, output_nc, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
        super(FirstBlock2d, self).__init__()
        kwargs = {'kernel_size': 7, 'stride': 1, 'padding': 3}
        conv = spectral_norm(nn.Conv2d(input_nc, output_nc, **kwargs), use_spect)

        if type(norm_layer) == type(None):
            self.model = nn.Sequential(conv, nonlinearity)
        else:
            self.model = nn.Sequential(conv, norm_layer(output_nc), nonlinearity)


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

class DownBlock2d(nn.Module):
    def __init__(self, input_nc, output_nc, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
        super(DownBlock2d, self).__init__()


        kwargs = {'kernel_size': 3, 'stride': 1, 'padding': 1}
        conv = spectral_norm(nn.Conv2d(input_nc, output_nc, **kwargs), use_spect)
        pool = nn.AvgPool2d(kernel_size=(2, 2))

        if type(norm_layer) == type(None):
            self.model = nn.Sequential(conv, nonlinearity, pool)
        else:
            self.model = nn.Sequential(conv, norm_layer(output_nc), nonlinearity, pool)

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

class UpBlock2d(nn.Module):
    def __init__(self, input_nc, output_nc, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
        super(UpBlock2d, self).__init__()
        kwargs = {'kernel_size': 3, 'stride': 1, 'padding': 1}
        conv = spectral_norm(nn.Conv2d(input_nc, output_nc, **kwargs), use_spect)
        if type(norm_layer) == type(None):
            self.model = nn.Sequential(conv, nonlinearity)
        else:
            self.model = nn.Sequential(conv, norm_layer(output_nc), nonlinearity)

    def forward(self, x):
        out = self.model(F.interpolate(x, scale_factor=2))
        return out

class FineADAINResBlocks(nn.Module):
    def __init__(self, num_block, input_nc, feature_nc, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
        super(FineADAINResBlocks, self).__init__()                                
        self.num_block = num_block
        for i in range(num_block):
            model = FineADAINResBlock2d(input_nc, feature_nc, norm_layer, nonlinearity, use_spect)
            setattr(self, 'res'+str(i), model)

    def forward(self, x, z):
        for i in range(self.num_block):
            model = getattr(self, 'res'+str(i))
            x = model(x, z)
        return x     

class Jump(nn.Module):
    def __init__(self, input_nc, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
        super(Jump, self).__init__()
        kwargs = {'kernel_size': 3, 'stride': 1, 'padding': 1}
        conv = spectral_norm(nn.Conv2d(input_nc, input_nc, **kwargs), use_spect)

        if type(norm_layer) == type(None):
            self.model = nn.Sequential(conv, nonlinearity)
        else:
            self.model = nn.Sequential(conv, norm_layer(input_nc), nonlinearity)

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

class FineADAINResBlock2d(nn.Module):
    """
    Define an Residual block for different types
    """
    def __init__(self, input_nc, feature_nc, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
        super(FineADAINResBlock2d, self).__init__()

        kwargs = {'kernel_size': 3, 'stride': 1, 'padding': 1}

        self.conv1 = spectral_norm(nn.Conv2d(input_nc, input_nc, **kwargs), use_spect)
        self.conv2 = spectral_norm(nn.Conv2d(input_nc, input_nc, **kwargs), use_spect)
        self.norm1 = ADAIN(input_nc, feature_nc)
        self.norm2 = ADAIN(input_nc, feature_nc)

        self.actvn = nonlinearity


    def forward(self, x, z):
        dx = self.actvn(self.norm1(self.conv1(x), z))
        dx = self.norm2(self.conv2(x), z)
        out = dx + x
        return out        

class FinalBlock2d(nn.Module):
    """
    Define the output layer
    """
    def __init__(self, input_nc, output_nc, use_spect=False, tanh_or_sigmoid='tanh'):
        super(FinalBlock2d, self).__init__()

        kwargs = {'kernel_size': 7, 'stride': 1, 'padding':3}
        conv = spectral_norm(nn.Conv2d(input_nc, output_nc, **kwargs), use_spect)

        if tanh_or_sigmoid == 'sigmoid':
            out_nonlinearity = nn.Sigmoid()
        else:
            out_nonlinearity = nn.Tanh()            

        self.model = nn.Sequential(conv, out_nonlinearity)
    def forward(self, x):
        out = self.model(x)
        return out