You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
48 lines
1.3 KiB
48 lines
1.3 KiB
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 |
|
|
|
class GroupNorm(torch.nn.GroupNorm): |
|
def reset_parameters(self): |
|
return None |
|
|
|
class LayerNorm(torch.nn.LayerNorm): |
|
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}") |
|
|
|
@contextmanager |
|
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
|
|
|