main/library/predictors/RMVPE/deepunet.py

import torch

import torch.nn as nn

class ConvBlockRes(nn.Module):
    def __init__(self, in_channels, out_channels, momentum=0.01):
        super(ConvBlockRes, self).__init__()
        self.conv = nn.Sequential(nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False), nn.BatchNorm2d(out_channels, momentum=momentum), nn.ReLU(), nn.Conv2d(in_channels=out_channels, out_channels=out_channels, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False), nn.BatchNorm2d(out_channels, momentum=momentum), nn.ReLU())
        if in_channels != out_channels:
            self.shortcut = nn.Conv2d(in_channels, out_channels, (1, 1))
            self.is_shortcut = True
        else: self.is_shortcut = False

    def forward(self, x):
        return (self.conv(x) + self.shortcut(x)) if self.is_shortcut else (self.conv(x) + x)

class ResEncoderBlock(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, n_blocks=1, momentum=0.01):
        super(ResEncoderBlock, self).__init__()
        self.n_blocks = n_blocks
        self.conv = nn.ModuleList()
        self.conv.append(ConvBlockRes(in_channels, out_channels, momentum))

        for _ in range(n_blocks - 1):
            self.conv.append(ConvBlockRes(out_channels, out_channels, momentum))

        self.kernel_size = kernel_size
        if self.kernel_size is not None: self.pool = nn.AvgPool2d(kernel_size=kernel_size)

    def forward(self, x):
        for i in range(self.n_blocks):
            x = self.conv[i](x)

        if self.kernel_size is not None: return x, self.pool(x)
        else: return x

class Encoder(nn.Module):
    def __init__(self, in_channels, in_size, n_encoders, kernel_size, n_blocks, out_channels=16, momentum=0.01):
        super(Encoder, self).__init__()
        self.n_encoders = n_encoders
        self.bn = nn.BatchNorm2d(in_channels, momentum=momentum)
        self.layers = nn.ModuleList()

        for _ in range(self.n_encoders):
            self.layers.append(ResEncoderBlock(in_channels, out_channels, kernel_size, n_blocks, momentum=momentum))
            in_channels = out_channels
            out_channels *= 2
            in_size //= 2
            
        self.out_size = in_size
        self.out_channel = out_channels

    def forward(self, x):
        concat_tensors = []
        x = self.bn(x)

        for layer in self.layers:
            t, x = layer(x)
            concat_tensors.append(t)

        return x, concat_tensors

class Intermediate(nn.Module):
    def __init__(self, in_channels, out_channels, n_inters, n_blocks, momentum=0.01):
        super(Intermediate, self).__init__()
        self.layers = nn.ModuleList()
        self.layers.append(ResEncoderBlock(in_channels, out_channels, None, n_blocks, momentum))

        for _ in range(n_inters - 1):
            self.layers.append(ResEncoderBlock(out_channels, out_channels, None, n_blocks, momentum))

    def forward(self, x):
        for layer in self.layers:
            x = layer(x)

        return x

class ResDecoderBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride, n_blocks=1, momentum=0.01):
        super(ResDecoderBlock, self).__init__()
        out_padding = (0, 1) if stride == (1, 2) else (1, 1)
        self.conv1 = nn.Sequential(nn.ConvTranspose2d(in_channels=in_channels, out_channels=out_channels, kernel_size=(3, 3), stride=stride, padding=(1, 1), output_padding=out_padding, bias=False), nn.BatchNorm2d(out_channels, momentum=momentum), nn.ReLU())
        self.conv2 = nn.ModuleList()
        self.conv2.append(ConvBlockRes(out_channels * 2, out_channels, momentum))

        for _ in range(n_blocks - 1):
            self.conv2.append(ConvBlockRes(out_channels, out_channels, momentum))

    def forward(self, x, concat_tensor):
        x = torch.cat((self.conv1(x), concat_tensor), dim=1)
        for conv2 in self.conv2:
            x = conv2(x)

        return x

class Decoder(nn.Module):
    def __init__(self, in_channels, n_decoders, stride, n_blocks, momentum=0.01):
        super(Decoder, self).__init__()
        self.layers = nn.ModuleList()

        for _ in range(n_decoders):
            out_channels = in_channels // 2
            self.layers.append(ResDecoderBlock(in_channels, out_channels, stride, n_blocks, momentum))
            in_channels = out_channels

    def forward(self, x, concat_tensors):
        for i, layer in enumerate(self.layers):
            x = layer(x, concat_tensors[-1 - i])

        return x

class DeepUnet(nn.Module):
    def __init__(self, kernel_size, n_blocks, en_de_layers=5, inter_layers=4, in_channels=1, en_out_channels=16):
        super(DeepUnet, self).__init__()
        self.encoder = Encoder(in_channels, 128, en_de_layers, kernel_size, n_blocks, en_out_channels)
        self.intermediate = Intermediate(self.encoder.out_channel // 2, self.encoder.out_channel, inter_layers, n_blocks)
        self.decoder = Decoder(self.encoder.out_channel, en_de_layers, kernel_size, n_blocks)

    def forward(self, x):
        x, concat_tensors = self.encoder(x)
        return self.decoder(self.intermediate(x), concat_tensors)