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How to use get_padding method in Lemoncheesecake

Best Python code snippet using lemoncheesecake

torch_model.py

Source:torch_model.py

...4from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d5from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm6# from utils import init_weights, get_padding7LRELU_SLOPE = 0.18def get_padding(kernel_size, dilation=1):9    return int((kernel_size * dilation - dilation) / 2)10def init_weights(m, mean=0.0, std=0.01):11    classname = m.__class__.__name__12    if classname.find("Conv") != -1:13        m.weight.data.normal_(mean, std)14class ResBlock1(torch.nn.Module):15    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):16        super(ResBlock1, self).__init__()17        self.h = h18        self.convs1 = nn.ModuleList(19            [20                weight_norm(21                    Conv1d(22                        channels,23                        channels,24                        kernel_size,25                        1,26                        dilation=dilation[0],27                        padding=get_padding(kernel_size, dilation[0]),28                    )29                ),30                weight_norm(31                    Conv1d(32                        channels,33                        channels,34                        kernel_size,35                        1,36                        dilation=dilation[1],37                        padding=get_padding(kernel_size, dilation[1]),38                    )39                ),40                weight_norm(41                    Conv1d(42                        channels,43                        channels,44                        kernel_size,45                        1,46                        dilation=dilation[2],47                        padding=get_padding(kernel_size, dilation[2]),48                    )49                ),50            ]51        )52        self.convs1.apply(init_weights)53        self.convs2 = nn.ModuleList(54            [55                weight_norm(56                    Conv1d(57                        channels,58                        channels,59                        kernel_size,60                        1,61                        dilation=1,62                        padding=get_padding(kernel_size, 1),63                    )64                ),65                weight_norm(66                    Conv1d(67                        channels,68                        channels,69                        kernel_size,70                        1,71                        dilation=1,72                        padding=get_padding(kernel_size, 1),73                    )74                ),75                weight_norm(76                    Conv1d(77                        channels,78                        channels,79                        kernel_size,80                        1,81                        dilation=1,82                        padding=get_padding(kernel_size, 1),83                    )84                ),85            ]86        )87        self.convs2.apply(init_weights)88    def forward(self, x):89        for c1, c2 in zip(self.convs1, self.convs2):90            xt = F.leaky_relu(x, LRELU_SLOPE)91            xt = c1(xt)92            xt = F.leaky_relu(xt, LRELU_SLOPE)93            xt = c2(xt)94            x = xt + x95        return x96    def remove_weight_norm(self):97        for l in self.convs1:98            remove_weight_norm(l)99        for l in self.convs2:100            remove_weight_norm(l)101class ResBlock2(torch.nn.Module):102    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)):103        super(ResBlock2, self).__init__()104        self.h = h105        self.convs = nn.ModuleList(106            [107                weight_norm(108                    Conv1d(109                        channels,110                        channels,111                        kernel_size,112                        1,113                        dilation=dilation[0],114                        padding=get_padding(kernel_size, dilation[0]),115                    )116                ),117                weight_norm(118                    Conv1d(119                        channels,120                        channels,121                        kernel_size,122                        1,123                        dilation=dilation[1],124                        padding=get_padding(kernel_size, dilation[1]),125                    )126                ),127            ]128        )129        self.convs.apply(init_weights)130    def forward(self, x):131        for c in self.convs:132            xt = F.leaky_relu(x, LRELU_SLOPE)133            xt = c(xt)134            x = xt + x135        return x136    def remove_weight_norm(self):137        for l in self.convs:138            remove_weight_norm(l)139class Generator(torch.nn.Module):140    def __init__(self, h):141        super(Generator, self).__init__()142        self.h = h143        self.num_kernels = len(h.resblock_kernel_sizes)144        self.num_upsamples = len(h.upsample_rates)145        self.conv_pre = weight_norm(146            Conv1d(80, h.upsample_initial_channel, 7, 1, padding=3)147        )148        resblock = ResBlock1 if h.resblock == "1" else ResBlock2149        self.ups = nn.ModuleList()150        for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):151            self.ups.append(152                weight_norm(153                    ConvTranspose1d(154                        h.upsample_initial_channel // (2**i),155                        h.upsample_initial_channel // (2 ** (i + 1)),156                        k,157                        u,158                        padding=(k - u) // 2,159                    )160                )161            )162        self.resblocks = nn.ModuleList()163        for i in range(len(self.ups)):164            ch = h.upsample_initial_channel // (2 ** (i + 1))165            for j, (k, d) in enumerate(166                zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)167            ):168                self.resblocks.append(resblock(h, ch, k, d))169        self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))170        self.ups.apply(init_weights)171        self.conv_post.apply(init_weights)172    def forward(self, x):173        x = self.conv_pre(x)174        for i in range(self.num_upsamples):175            x = F.leaky_relu(x, LRELU_SLOPE)176            x = self.ups[i](x)177            xs = None178            for j in range(self.num_kernels):179                if xs is None:180                    xs = self.resblocks[i * self.num_kernels + j](x)181                else:182                    xs += self.resblocks[i * self.num_kernels + j](x)183            x = xs / self.num_kernels184        x = F.leaky_relu(x)185        x = self.conv_post(x)186        x = torch.tanh(x)187        return x188    def remove_weight_norm(self):189        print("Removing weight norm...")190        for l in self.ups:191            remove_weight_norm(l)192        for l in self.resblocks:193            l.remove_weight_norm()194        remove_weight_norm(self.conv_pre)195        remove_weight_norm(self.conv_post)196class DiscriminatorP(torch.nn.Module):197    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):198        super(DiscriminatorP, self).__init__()199        self.period = period200        norm_f = weight_norm if use_spectral_norm == False else spectral_norm201        self.convs = nn.ModuleList(202            [203                norm_f(204                    Conv2d(205                        1,206                        32,207                        (kernel_size, 1),208                        (stride, 1),209                        padding=(get_padding(5, 1), 0),210                    )211                ),212                norm_f(213                    Conv2d(214                        32,215                        128,216                        (kernel_size, 1),217                        (stride, 1),218                        padding=(get_padding(5, 1), 0),219                    )220                ),221                norm_f(222                    Conv2d(223                        128,224                        512,225                        (kernel_size, 1),226                        (stride, 1),227                        padding=(get_padding(5, 1), 0),228                    )229                ),230                norm_f(231                    Conv2d(232                        512,233                        1024,234                        (kernel_size, 1),235                        (stride, 1),236                        padding=(get_padding(5, 1), 0),237                    )238                ),239                norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))),240            ]241        )242        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))243    def forward(self, x):244        fmap = []245        # 1d to 2d246        b, c, t = x.shape247        if t % self.period != 0:  # pad first248            n_pad = self.period - (t % self.period)249            x = F.pad(x, (0, n_pad), "reflect")250            t = t + n_pad...

spec2vec.py

Source:spec2vec.py

...8        self.conv1 = nn.Conv2d(9            2,10            96,11            kernel_size=(1, 7),12            padding=self.get_padding((1, 7), (1, 1)),13            dilation=(1, 1),14        )15        self.conv2 = nn.Conv2d(16            96,17            96,18            kernel_size=(7, 1),19            padding=self.get_padding((7, 1), (1, 1)),20            dilation=(1, 1),21        )22        self.conv3 = nn.Conv2d(23            96,24            96,25            kernel_size=(5, 5),26            padding=self.get_padding((5, 5), (1, 1)),27            dilation=(1, 1),28        )29        self.conv4 = nn.Conv2d(30            96,31            96,32            kernel_size=(5, 5),33            padding=self.get_padding((5, 5), (2, 1)),34            dilation=(2, 1),35        )36        self.conv5 = nn.Conv2d(37            96,38            96,39            kernel_size=(5, 5),40            padding=self.get_padding((5, 5), (4, 1)),41            dilation=(4, 1),42        )43        self.conv6 = nn.Conv2d(44            96,45            96,46            kernel_size=(5, 5),47            padding=self.get_padding((5, 5), (8, 1)),48            dilation=(8, 1),49        )50        self.conv7 = nn.Conv2d(51            96,52            96,53            kernel_size=(5, 5),54            padding=self.get_padding((5, 5), (16, 1)),55            dilation=(16, 1),56        )57        self.conv8 = nn.Conv2d(58            96,59            96,60            kernel_size=(5, 5),61            padding=self.get_padding((5, 5), (32, 1)),62            dilation=(32, 1),63        )64        self.conv9 = nn.Conv2d(65            96,66            96,67            kernel_size=(5, 5),68            padding=self.get_padding((5, 5), (1, 1)),69            dilation=(1, 1),70        )71        self.conv10 = nn.Conv2d(72            96,73            96,74            kernel_size=(5, 5),75            padding=self.get_padding((5, 5), (2, 2)),76            dilation=(2, 2),77        )78        self.conv11 = nn.Conv2d(79            96,80            96,81            kernel_size=(5, 5),82            padding=self.get_padding((5, 5), (4, 4)),83            dilation=(4, 4),84        )85        self.conv12 = nn.Conv2d(86            96,87            96,88            kernel_size=(5, 5),89            padding=self.get_padding((5, 5), (8, 8)),90            dilation=(8, 8),91        )92        self.conv13 = nn.Conv2d(93            96,94            96,95            kernel_size=(5, 5),96            padding=self.get_padding((5, 5), (16, 16)),97            dilation=(16, 16),98        )99        self.conv14 = nn.Conv2d(100            96,101            96,102            kernel_size=(5, 5),103            padding=self.get_padding((5, 5), (32, 32)),104            dilation=(32, 32),105        )106        self.conv15 = nn.Conv2d(107            96,108            last_shape,109            kernel_size=(1, 1),110            padding=self.get_padding((1, 1), (1, 1)),111            dilation=(1, 1),112        )113        # Batch normalization layers114        self.batch_norm1 = nn.BatchNorm2d(96)115        self.batch_norm2 = nn.BatchNorm2d(96)116        self.batch_norm3 = nn.BatchNorm2d(96)117        self.batch_norm4 = nn.BatchNorm2d(96)118        self.batch_norm5 = nn.BatchNorm2d(96)119        self.batch_norm6 = nn.BatchNorm2d(96)120        self.batch_norm7 = nn.BatchNorm2d(96)121        self.batch_norm8 = nn.BatchNorm2d(96)122        self.batch_norm9 = nn.BatchNorm2d(96)123        self.batch_norm10 = nn.BatchNorm2d(96)124        self.batch_norm11 = nn.BatchNorm2d(96)125        self.batch_norm11 = nn.BatchNorm2d(96)126        self.batch_norm12 = nn.BatchNorm2d(96)127        self.batch_norm13 = nn.BatchNorm2d(96)128        self.batch_norm14 = nn.BatchNorm2d(96)129        self.batch_norm15 = nn.BatchNorm2d(last_shape)130    def get_padding(self, kernel_size, dilation):131        padding = (132            ((dilation[0]) * (kernel_size[0] - 1)) // 2,133            ((dilation[1]) * (kernel_size[1] - 1)) // 2,134        )135        return padding136    def forward(self, input_audio):137        # input audio will be (2,256,256)138        output_layer = F.leaky_relu(self.batch_norm1(self.conv1(input_audio)), negative_slope=0.1)139        output_layer = F.leaky_relu(self.batch_norm2(self.conv2(output_layer)), negative_slope=0.1)140        output_layer = F.leaky_relu(self.batch_norm3(self.conv3(output_layer)), negative_slope=0.1)141        output_layer = F.leaky_relu(self.batch_norm4(self.conv4(output_layer)), negative_slope=0.1)142        output_layer = F.leaky_relu(self.batch_norm5(self.conv5(output_layer)), negative_slope=0.1)143        output_layer = F.leaky_relu(self.batch_norm6(self.conv6(output_layer)), negative_slope=0.1)144        output_layer = F.leaky_relu(self.batch_norm7(self.conv7(output_layer)), negative_slope=0.1)...

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