mirror of
https://github.com/facefusion/facefusion-labs.git
synced 2026-05-22 23:59:40 +02:00
henryruhs
43 lines
1.3 KiB
Python
43 lines
1.3 KiB
Python
from configparser import ConfigParser
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from typing import Tuple
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from torch import Tensor, nn
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from ..networks.aad import AAD
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from ..networks.unet import UNet
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from ..networks.masknet import MaskNet
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from ..types import Embedding, Feature, Mask
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class Generator(nn.Module):
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def __init__(self, config_parser : ConfigParser) -> None:
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super().__init__()
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self.encoder = UNet(config_parser)
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self.generator = AAD(config_parser)
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self.masker = MaskNet(config_parser)
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self.encoder.apply(init_weight)
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self.generator.apply(init_weight)
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self.masker.apply(init_weight)
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def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tuple[Tensor, Mask]:
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target_features = self.encode_features(target_tensor)
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output_tensor = self.generator(source_embedding, target_features)
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target_feature = target_features[-1]
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output_mask = self.masker(target_tensor, target_feature)
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return output_tensor, output_mask
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def encode_features(self, input_tensor : Tensor) -> Tuple[Feature, ...]:
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return self.encoder(input_tensor)
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def init_weight(module : nn.Module) -> None:
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if isinstance(module, nn.Linear):
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module.weight.data.normal_(std = 0.001)
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module.bias.data.zero_()
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if isinstance(module, nn.Conv2d):
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nn.init.xavier_normal_(module.weight.data)
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if isinstance(module, nn.ConvTranspose2d):
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nn.init.xavier_normal_(module.weight.data)
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