File size: 3,464 Bytes
d643072 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 |
# Copyright 2024 MIT Han Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# SPDX-License-Identifier: Apache-2.0
import collections
import os
from inspect import signature
from typing import Any, Callable, Optional, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
__all__ = [
"is_parallel",
"get_device",
"get_same_padding",
"resize",
"build_kwargs_from_config",
"load_state_dict_from_file",
"get_submodule_weights",
]
def is_parallel(model: nn.Module) -> bool:
return isinstance(model, (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel))
def get_device(model: nn.Module) -> torch.device:
return model.parameters().__next__().device
def get_dtype(model: nn.Module) -> torch.dtype:
return model.parameters().__next__().dtype
def get_same_padding(kernel_size: Union[int, tuple[int, ...]]) -> Union[int, tuple[int, ...]]:
if isinstance(kernel_size, tuple):
return tuple([get_same_padding(ks) for ks in kernel_size])
else:
assert kernel_size % 2 > 0, "kernel size should be odd number"
return kernel_size // 2
def resize(
x: torch.Tensor,
size: Optional[Any] = None,
scale_factor: Optional[list[float]] = None,
mode: str = "bicubic",
align_corners: Optional[bool] = False,
) -> torch.Tensor:
if mode in {"bilinear", "bicubic"}:
return F.interpolate(
x,
size=size,
scale_factor=scale_factor,
mode=mode,
align_corners=align_corners,
)
elif mode in {"nearest", "area"}:
return F.interpolate(x, size=size, scale_factor=scale_factor, mode=mode)
else:
raise NotImplementedError(f"resize(mode={mode}) not implemented.")
def build_kwargs_from_config(config: dict, target_func: Callable) -> dict[str, Any]:
valid_keys = list(signature(target_func).parameters)
kwargs = {}
for key in config:
if key in valid_keys:
kwargs[key] = config[key]
return kwargs
def load_state_dict_from_file(file: str, only_state_dict=True) -> dict[str, torch.Tensor]:
file = os.path.realpath(os.path.expanduser(file))
checkpoint = torch.load(file, map_location="cpu", weights_only=True)
if only_state_dict and "state_dict" in checkpoint:
checkpoint = checkpoint["state_dict"]
return checkpoint
def get_submodule_weights(weights: collections.OrderedDict, prefix: str):
submodule_weights = collections.OrderedDict()
len_prefix = len(prefix)
for key, weight in weights.items():
if key.startswith(prefix):
submodule_weights[key[len_prefix:]] = weight
return submodule_weights
def get_dtype_from_str(dtype: str) -> torch.dtype:
if dtype == "fp32":
return torch.float32
if dtype == "fp16":
return torch.float16
if dtype == "bf16":
return torch.bfloat16
raise NotImplementedError(f"dtype {dtype} is not supported")
|