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from __future__ import annotations
import functools
import importlib
import os
from functools import partial
from inspect import isfunction
import fsspec
import torch
from einops import repeat
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode does not change anymore."""
return self
def get_string_from_tuple(s):
try:
# check if the string starts and ends with parentheses
if s[0] == "(" and s[-1] == ")":
# convert the string to a tuple
t = eval(s)
# check if the type of t is tuple
if isinstance(t, tuple):
return t[0]
else:
pass
except:
pass
return s
def is_power_of_two(n):
"""Return True if n is a power of 2, otherwise return False."""
if n <= 0:
return False
else:
return (n & (n - 1)) == 0
def autocast(f, enabled=True):
def do_autocast(*args, **kwargs):
with torch.cuda.amp.autocast(
enabled=enabled,
dtype=torch.get_autocast_gpu_dtype(),
cache_enabled=torch.is_autocast_cache_enabled()
):
return f(*args, **kwargs)
return do_autocast
def load_partial_from_config(config):
return partial(get_obj_from_str(config["target"]), **config.get("params", dict()))
def repeat_as_img_seq(x, num_frames):
if x is not None:
if isinstance(x, list):
new_x = list()
for item_x in x:
new_x += [item_x] * num_frames
return new_x
else:
x = x.unsqueeze(1)
x = repeat(x, "b 1 ... -> (b t) ...", t=num_frames)
return x
else:
return None
def partialclass(cls, *args, **kwargs):
class NewCls(cls):
__init__ = functools.partialmethod(cls.__init__, *args, **kwargs)
return NewCls
def make_path_absolute(path):
fs, p = fsspec.core.url_to_fs(path)
if fs.protocol == "file":
return os.path.abspath(p)
else:
return path
def ismap(x):
if not isinstance(x, torch.Tensor):
return False
else:
return (len(x.shape) == 4) and (x.shape[1] > 3)
def isimage(x):
if not isinstance(x, torch.Tensor):
return False
else:
return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)
def isheatmap(x):
if not isinstance(x, torch.Tensor):
return False
else:
return x.ndim == 2
def isneighbors(x):
if not isinstance(x, torch.Tensor):
return False
else:
return x.ndim == 5 and (x.shape[2] == 3 or x.shape[2] == 1)
def exists(x):
return x is not None
def expand_dims_like(x, y):
while x.dim() != y.dim():
x = x.unsqueeze(-1)
return x
def default(val, d):
if exists(val):
return val
else:
return d() if isfunction(d) else d
def mean_flat(tensor):
"""
https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86
Take the mean over all non-batch dimensions.
"""
return tensor.mean(dim=list(range(1, len(tensor.shape))))
def count_params(model, verbose=False):
total_params = sum(p.numel() for p in model.parameters())
if verbose:
print(f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params")
return total_params
def instantiate_from_config(config):
if "target" not in config:
if config == "__is_first_stage__":
return None
elif config == "__is_unconditional__":
return None
else:
raise KeyError("Expected key `target` to instantiate")
else:
return get_obj_from_str(config["target"])(**config.get("params", dict()))
def get_obj_from_str(string, reload=False, invalidate_cache=True):
module, cls = string.rsplit(".", 1)
if invalidate_cache:
importlib.invalidate_caches()
if reload:
module_imp = importlib.import_module(module)
importlib.reload(module_imp)
return getattr(importlib.import_module(module, package=None), cls)
def append_zero(x):
return torch.cat((x, x.new_zeros([1])))
def append_dims(x, target_dims):
"""Appends dimensions to the end of a tensor until it has target_dims dimensions."""
dims_to_append = target_dims - x.ndim
if dims_to_append < 0:
raise ValueError(f"Input has {x.ndim} dims but target_dims is {target_dims}, which is less")
return x[(...,) + (None,) * dims_to_append]
def get_configs_path() -> str:
"""Get the `configs` directory."""
this_dir = os.path.dirname(__file__)
candidates = (
os.path.join(this_dir, "configs"),
os.path.join(this_dir, "..", "configs")
)
for candidate in candidates:
candidate = os.path.abspath(candidate)
if os.path.isdir(candidate):
return candidate
raise FileNotFoundError(f"Could not find configs in {candidates}")
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