Audeo/Roll2MidiNet.py at master · shlizee/Audeo · GitHub

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
import torch.nn as nn
import torch.nn.functional as F
import torch
##############################
#           U-NET
##############################
class UNetDown(nn.Module):
    def __init__(self, in_size, out_size, normalize=True, dropout=0.0):
        super(UNetDown, self).__init__()
        model = [nn.Conv2d(in_size, out_size, 3, stride=1, padding=1, bias=False)]
        if normalize:
            model.append(nn.BatchNorm2d(out_size, 0.8))
        model.append(nn.LeakyReLU(0.2))
        if dropout:
            model.append(nn.Dropout(dropout))

        self.model = nn.Sequential(*model)

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


class UNetUp(nn.Module):
    def __init__(self, in_size, out_size, dropout=0.0):
        super(UNetUp, self).__init__()
        model = [
            nn.ConvTranspose2d(in_size, out_size, 3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(out_size, 0.8),
            nn.ReLU(inplace=True),
        ]
        if dropout:
            model.append(nn.Dropout(dropout))

        self.model = nn.Sequential(*model)

    def forward(self, x, skip_input):
        x = self.model(x)
        out = torch.cat((x, skip_input), 1)
        return out


class Generator(nn.Module):
    def __init__(self, input_shape):
        super(Generator, self).__init__()
        channels, _ , _ = input_shape
        self.down1 = UNetDown(channels, 64, normalize=False)
        self.down2 = UNetDown(64, 128)
        self.down3 = UNetDown(128, 256, dropout=0.5)
        self.down4 = UNetDown(256, 512, dropout=0.5)
        self.down5 = UNetDown(512, 1024, dropout=0.5)
        self.down6 = UNetDown(1024, 1024, dropout=0.5)

        self.up1 = UNetUp(1024, 512, dropout=0.5)
        self.up2 = UNetUp(1024+512, 256, dropout=0.5)
        self.up3 = UNetUp(512+256, 128, dropout=0.5)
        self.up4 = UNetUp(256+128, 64)
        self.up5 = UNetUp(128+64, 16)
        self.conv1d = nn.Conv2d(80, 1, kernel_size=1)

    def forward(self, x):
        # U-Net generator with skip connections from encoder to decoder
        d1 = self.down1(x)

        d2 = self.down2(d1)

        d3 = self.down3(d2)

        d4 = self.down4(d3)

        d5 = self.down5(d4)

        d6 = self.down6(d5)

        u1 = self.up1(d6, d5)

        u2 = self.up2(u1, d4)

        u3 = self.up3(u2, d3)

        u4 = self.up4(u3, d2)

        u5 = self.up5(u4, d1)

        out = self.conv1d(u5)

        out = F.sigmoid(out)
        return out


class Discriminator(nn.Module):
    def __init__(self, input_shape):
        super(Discriminator, self).__init__()

        channels, height, width = input_shape #1 51 50

        # Calculate output of image discriminator (PatchGAN)
        patch_h, patch_w = int(height / 2 ** 3)+1, int(width / 2 ** 3)+1
        self.output_shape = (1, patch_h, patch_w)

        def discriminator_block(in_filters, out_filters, stride, normalize):
            """Returns layers of each discriminator block"""
            layers = [nn.Conv2d(in_filters, out_filters, 3, stride, 1)]
            if normalize:
                layers.append(nn.InstanceNorm2d(out_filters))
            layers.append(nn.LeakyReLU(0.2, inplace=True))
            return layers

        layers = []
        in_filters = channels
        for out_filters, stride, normalize in [(64, 2, False), (128, 2, True), (256, 2, True), (512, 1, True)]:
            layers.extend(discriminator_block(in_filters, out_filters, stride, normalize))
            in_filters = out_filters

        layers.append(nn.Conv2d(out_filters, 1, 3, 1, 1))

        self.model = nn.Sequential(*layers)

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

def weights_init_normal(m):
    classname = m.__class__.__name__
    if classname.find("Conv") != -1:
        torch.nn.init.normal_(m.weight.data, 0.0, 0.02)
    elif classname.find("BatchNorm2d") != -1:
        torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
        torch.nn.init.constant_(m.bias.data, 0.0)

if __name__ == "__main__":
    input_shape = (1,51, 100)
    gnet = Generator(input_shape)
    dnet = Discriminator(input_shape)
    print(dnet.output_shape)
    imgs = torch.rand((64,1,51,100))
    gen = gnet(imgs)
    print(gen.shape)
    dis = dnet(gen)
    print(dis.shape)