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thanhvc3 2024-04-28 15:24:05 +07:00
parent fddea4769f
commit 6fc56b920f
1 changed files with 11 additions and 2 deletions
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13
models.py
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@ -597,7 +597,7 @@ class FouriER(torch.nn.Module):
for idx, block in enumerate(self.network): for idx, block in enumerate(self.network):
try: try:
x = block(x, graph) x = block((x, graph))
except: except:
x = block(x) x = block(x)
# output only the features of last layer for image classification # output only the features of last layer for image classification
@ -758,7 +758,7 @@ def basic_blocks(dim, index, layers,
use_layer_scale=use_layer_scale, use_layer_scale=use_layer_scale,
layer_scale_init_value=layer_scale_init_value, layer_scale_init_value=layer_scale_init_value,
)) ))
blocks = nn.Sequential(*blocks) blocks = SeqModel(*blocks)
return blocks return blocks
@ -923,6 +923,15 @@ def window_reverse(windows, window_size, H, W):
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, -1, H, W) x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, -1, H, W)
return x return x
class SeqModel(nn.Sequential):
def forward(self, *inputs):
for module in self._modules.values():
if type(inputs) == tuple:
inputs = module(*inputs)
else:
inputs = module(inputs)
return inputs
def propagate(x: torch.Tensor, weight: torch.Tensor, def propagate(x: torch.Tensor, weight: torch.Tensor,
index_kept: torch.Tensor, index_prop: torch.Tensor, index_kept: torch.Tensor, index_prop: torch.Tensor,
standard: str = "None", alpha: Optional[float] = 0, standard: str = "None", alpha: Optional[float] = 0,