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| import torch | |
| import torch.nn as nn | |
| from src.audio2pose_models.networks import ResidualConv, Upsample | |
| class ResUnet(nn.Module): | |
| def __init__(self, channel=1, filters=[32, 64, 128, 256]): | |
| super(ResUnet, self).__init__() | |
| self.input_layer = nn.Sequential( | |
| nn.Conv2d(channel, filters[0], kernel_size=3, padding=1), | |
| nn.BatchNorm2d(filters[0]), | |
| nn.ReLU(), | |
| nn.Conv2d(filters[0], filters[0], kernel_size=3, padding=1), | |
| ) | |
| self.input_skip = nn.Sequential( | |
| nn.Conv2d(channel, filters[0], kernel_size=3, padding=1) | |
| ) | |
| self.residual_conv_1 = ResidualConv(filters[0], filters[1], stride=(2,1), padding=1) | |
| self.residual_conv_2 = ResidualConv(filters[1], filters[2], stride=(2,1), padding=1) | |
| self.bridge = ResidualConv(filters[2], filters[3], stride=(2,1), padding=1) | |
| self.upsample_1 = Upsample(filters[3], filters[3], kernel=(2,1), stride=(2,1)) | |
| self.up_residual_conv1 = ResidualConv(filters[3] + filters[2], filters[2], stride=1, padding=1) | |
| self.upsample_2 = Upsample(filters[2], filters[2], kernel=(2,1), stride=(2,1)) | |
| self.up_residual_conv2 = ResidualConv(filters[2] + filters[1], filters[1], stride=1, padding=1) | |
| self.upsample_3 = Upsample(filters[1], filters[1], kernel=(2,1), stride=(2,1)) | |
| self.up_residual_conv3 = ResidualConv(filters[1] + filters[0], filters[0], stride=1, padding=1) | |
| self.output_layer = nn.Sequential( | |
| nn.Conv2d(filters[0], 1, 1, 1), | |
| nn.Sigmoid(), | |
| ) | |
| def forward(self, x): | |
| # Encode | |
| x1 = self.input_layer(x) + self.input_skip(x) | |
| x2 = self.residual_conv_1(x1) | |
| x3 = self.residual_conv_2(x2) | |
| # Bridge | |
| x4 = self.bridge(x3) | |
| # Decode | |
| x4 = self.upsample_1(x4) | |
| x5 = torch.cat([x4, x3], dim=1) | |
| x6 = self.up_residual_conv1(x5) | |
| x6 = self.upsample_2(x6) | |
| x7 = torch.cat([x6, x2], dim=1) | |
| x8 = self.up_residual_conv2(x7) | |
| x8 = self.upsample_3(x8) | |
| x9 = torch.cat([x8, x1], dim=1) | |
| x10 = self.up_residual_conv3(x9) | |
| output = self.output_layer(x10) | |
| return output |