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40 lines
1.2 KiB
40 lines
1.2 KiB
import torch |
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from contextlib import contextmanager |
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class Linear(torch.nn.Linear): |
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def reset_parameters(self): |
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return None |
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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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class Conv3d(torch.nn.Conv3d): |
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def reset_parameters(self): |
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return None |
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def conv_nd(dims, *args, **kwargs): |
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if dims == 2: |
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return Conv2d(*args, **kwargs) |
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elif dims == 3: |
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return Conv3d(*args, **kwargs) |
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else: |
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raise ValueError(f"unsupported dimensions: {dims}") |
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@contextmanager |
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def use_comfy_ops(device=None, dtype=None): # 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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force_device = device |
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force_dtype = dtype |
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def linear_with_dtype(in_features: int, out_features: int, bias: bool = True, device=None, dtype=None): |
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if force_device is not None: |
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device = force_device |
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if force_dtype is not None: |
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dtype = force_dtype |
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return Linear(in_features, out_features, bias=bias, device=device, dtype=dtype) |
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torch.nn.Linear = linear_with_dtype |
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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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