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32 lines
1.1 KiB
32 lines
1.1 KiB
import torch |
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from contextlib import contextmanager |
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class Linear(torch.nn.Module): |
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def __init__(self, in_features: int, out_features: int, bias: bool = True, |
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device=None, dtype=None) -> None: |
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factory_kwargs = {'device': device, 'dtype': dtype} |
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super().__init__() |
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self.in_features = in_features |
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self.out_features = out_features |
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self.weight = torch.nn.Parameter(torch.empty((out_features, in_features), **factory_kwargs)) |
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if bias: |
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self.bias = torch.nn.Parameter(torch.empty(out_features, **factory_kwargs)) |
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else: |
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self.register_parameter('bias', None) |
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def forward(self, input): |
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return torch.nn.functional.linear(input, self.weight, self.bias) |
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class Conv2d(torch.nn.Conv2d): |
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def reset_parameters(self): |
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return None |
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@contextmanager |
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def use_comfy_ops(): # Kind of an ugly hack but I can't think of a better way |
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old_torch_nn_linear = torch.nn.Linear |
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torch.nn.Linear = Linear |
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try: |
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yield |
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finally: |
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torch.nn.Linear = old_torch_nn_linear
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