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import torch | |
from contextlib import contextmanager | |
class Linear(torch.nn.Linear): | |
def reset_parameters(self): | |
return None | |
class Conv2d(torch.nn.Conv2d): | |
def reset_parameters(self): | |
return None | |
class Conv3d(torch.nn.Conv3d): | |
def reset_parameters(self): | |
return None | |
def conv_nd(dims, *args, **kwargs): | |
if dims == 2: | |
return Conv2d(*args, **kwargs) | |
elif dims == 3: | |
return Conv3d(*args, **kwargs) | |
else: | |
raise ValueError(f"unsupported dimensions: {dims}") | |
def use_comfy_ops(device=None, dtype=None): # Kind of an ugly hack but I can't think of a better way | |
old_torch_nn_linear = torch.nn.Linear | |
force_device = device | |
force_dtype = dtype | |
def linear_with_dtype(in_features: int, out_features: int, bias: bool = True, device=None, dtype=None): | |
if force_device is not None: | |
device = force_device | |
if force_dtype is not None: | |
dtype = force_dtype | |
return Linear(in_features, out_features, bias=bias, device=device, dtype=dtype) | |
torch.nn.Linear = linear_with_dtype | |
try: | |
yield | |
finally: | |
torch.nn.Linear = old_torch_nn_linear | |