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on
Zero
Running
on
Zero
File size: 1,547 Bytes
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import torch
from einops import rearrange
def isinstance_str(x: object, cls_name: str):
"""
Checks whether x has any class *named* cls_name in its ancestry.
Doesn't require access to the class's implementation.
Useful for patching!
"""
for _cls in x.__class__.__mro__:
if _cls.__name__ == cls_name:
return True
return False
def init_generator(device: torch.device, fallback: torch.Generator=None):
"""
Forks the current default random generator given device.
"""
if device.type == "cpu":
return torch.Generator(device="cpu").set_state(torch.get_rng_state())
elif device.type == "cuda":
return torch.Generator(device=device).set_state(torch.cuda.get_rng_state())
else:
if fallback is None:
return init_generator(torch.device("cpu"))
else:
return fallback
def join_frame(x, fsize):
""" Join multi-frame tokens """
x = rearrange(x, "(B F) N C -> B (F N) C", F=fsize)
return x
def split_frame(x, fsize):
""" Split multi-frame tokens """
x = rearrange(x, "B (F N) C -> (B F) N C", F=fsize)
return x
def func_warper(funcs):
""" Warp a function sequence """
def fn(x, **kwarg):
for func in funcs:
x = func(x, **kwarg)
return x
return fn
def join_warper(fsize):
def fn(x):
x = join_frame(x, fsize)
return x
return fn
def split_warper(fsize):
def fn(x):
x = split_frame(x, fsize)
return x
return fn
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