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Use new loss code, Remove unused code, Remove old types, Ban VisionTensor naming

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henryruhs
2025-03-11 14:43:10 +01:00
parent 63e4bea3cd
commit ed0f6ae897
6 changed files with 54 additions and 217 deletions
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face_swapper/src/networks/attribute_modulator.py
+4 -4
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@@ -1,7 +1,7 @@
import torch
from torch import Tensor, nn
from ..types import Embedding, TargetAttributes
from ..types import Attributes, Embedding
class AADGenerator(nn.Module):
@@ -17,7 +17,7 @@ class AADGenerator(nn.Module):
self.res_block_7 = AADResBlock(128, 64, 64, id_channels, num_blocks)
self.res_block_8 = AADResBlock(64, 3, 64, id_channels, num_blocks)
def forward(self, target_attributes : TargetAttributes, source_embedding : Embedding) -> Tensor:
def forward(self, target_attributes : Attributes, source_embedding : Embedding) -> Tensor:
feature_map = self.upsample(source_embedding)
feature_map_1 = nn.functional.interpolate(self.res_block_1(feature_map, target_attributes[0], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False)
feature_map_2 = nn.functional.interpolate(self.res_block_2(feature_map_1, target_attributes[1], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False)
@@ -59,10 +59,10 @@ class AADSequential(nn.Module):
super().__init__()
self.layers = nn.ModuleList(args)
def forward(self, feature_map : Tensor, attribute_embedding : Embedding, id_embedding : Embedding) -> Tensor:
def forward(self, feature_map : Tensor, attribute_embedding : Embedding, identity_embedding : Embedding) -> Tensor:
for layer in self.layers:
if isinstance(layer, AADLayer):
feature_map = layer(feature_map, attribute_embedding, id_embedding)
feature_map = layer(feature_map, attribute_embedding, identity_embedding)
else:
feature_map = layer(feature_map)
return feature_map