Spaces:
Sleeping
Sleeping
File size: 9,292 Bytes
e3641b1 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 |
from collections import OrderedDict
from importlib import import_module
import os
import random
import re
import warnings
from typing import Union, Any
import numpy as np
import torch
from torch import distributed as dist
import torch.nn as nn
from torch.nn.parallel import DataParallel, DistributedDataParallel
from .dist_util import get_dist_info
MODULE_WRAPPERS = [DataParallel, DistributedDataParallel]
MODEL_ABBR_MAP = {
's': 'small',
'b': 'base',
'l': 'large',
'h': 'huge'
}
def infer_dataset_by_path(model_path: str) -> Union[str, Any]:
model = os.path.basename(model_path)
p = r'-([a-zA-Z0-9_]+)\.[pth, onnx, engine]'
m = re.search(p, model)
if not m:
raise ValueError('Could not infer the dataset from ckpt name, specify it')
return m.group(1)
def dyn_model_import(dataset: str, model: str):
config_name = f'configs.ViTPose_{dataset}'
imp = import_module(config_name)
model = f'model_{MODEL_ABBR_MAP[model]}'
return getattr(imp, model)
def init_random_seed(seed=None, device='cuda'):
"""Initialize random seed.
If the seed is not set, the seed will be automatically randomized,
and then broadcast to all processes to prevent some potential bugs.
Args:
seed (int, Optional): The seed. Default to None.
device (str): The device where the seed will be put on.
Default to 'cuda'.
Returns:
int: Seed to be used.
"""
if seed is not None:
return seed
# Make sure all ranks share the same random seed to prevent
# some potential bugs. Please refer to
# https://github.com/open-mmlab/mmdetection/issues/6339
rank, world_size = get_dist_info()
seed = np.random.randint(2**31)
if world_size == 1:
return seed
if rank == 0:
random_num = torch.tensor(seed, dtype=torch.int32, device=device)
else:
random_num = torch.tensor(0, dtype=torch.int32, device=device)
dist.broadcast(random_num, src=0)
return random_num.item()
def set_random_seed(seed: int,
deterministic: bool = False,
use_rank_shift: bool = False) -> None:
"""Set random seed.
Args:
seed (int): Seed to be used.
deterministic (bool): Whether to set the deterministic option for
CUDNN backend, i.e., set `torch.backends.cudnn.deterministic`
to True and `torch.backends.cudnn.benchmark` to False.
Default: False.
rank_shift (bool): Whether to add rank number to the random seed to
have different random seed in different threads. Default: False.
"""
if use_rank_shift:
rank, _ = get_dist_info()
seed += rank
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
if deterministic:
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def is_module_wrapper(module: nn.Module) -> bool:
""" Check if module wrrapper exists recursively """
def is_module_in_wrapper(module, module_wrapper):
module_wrappers = tuple(module_wrapper.module_dict.values())
if isinstance(module, module_wrappers):
return True
for child in module_wrapper.children.values():
if is_module_in_wrapper(module, child):
return True
return is_module_in_wrapper(module, MODULE_WRAPPERS)
def load_state_dict(module, state_dict, strict=False, logger=None):
"""Load state_dict to a module.
This method is modified from :meth:`torch.nn.Module.load_state_dict`.
Default value for ``strict`` is set to ``False`` and the message for
param mismatch will be shown even if strict is False.
Args:
module (Module): Module that receives the state_dict.
state_dict (OrderedDict): Weights.
strict (bool): whether to strictly enforce that the keys
in :attr:`state_dict` match the keys returned by this module's
:meth:`~torch.nn.Module.state_dict` function. Default: ``False``.
logger (:obj:`logging.Logger`, optional): Logger to log the error
message. If not specified, print function will be used.
"""
unexpected_keys = []
all_missing_keys = []
err_msg = []
metadata = getattr(state_dict, '_metadata', None)
state_dict = state_dict.copy()
if metadata is not None:
state_dict._metadata = metadata
# use _load_from_state_dict to enable checkpoint version control
def load(module, prefix=''):
# recursively check parallel module in case that the model has a
# complicated structure, e.g., nn.Module(nn.Module(DDP))
if is_module_wrapper(module):
module = module.module
local_metadata = {} if metadata is None else metadata.get(
prefix[:-1], {})
module._load_from_state_dict(state_dict, prefix, local_metadata, True,
all_missing_keys, unexpected_keys,
err_msg)
for name, child in module._modules.items():
if child is not None:
load(child, prefix + name + '.')
load(module)
load = None # break load->load reference cycle
# ignore "num_batches_tracked" of BN layers
missing_keys = [
key for key in all_missing_keys if 'num_batches_tracked' not in key
]
if unexpected_keys:
err_msg.append('unexpected key in source '
f'state_dict: {", ".join(unexpected_keys)}\n')
if missing_keys:
err_msg.append(
f'missing keys in source state_dict: {", ".join(missing_keys)}\n')
rank, _ = get_dist_info()
if len(err_msg) > 0 and rank == 0:
err_msg.insert(
0, 'The model and loaded state dict do not match exactly\n')
err_msg = '\n'.join(err_msg)
if strict:
raise RuntimeError(err_msg)
elif logger is not None:
logger.warning(err_msg)
else:
print(err_msg)
def load_checkpoint(model,
filename,
map_location='cpu',
strict=False,
logger=None):
"""Load checkpoint from a file or URI.
Args:
model (Module): Module to load checkpoint.
filename (str): Accept local filepath, URL, ``torchvision://xxx``,
``open-mmlab://xxx``.
map_location (str): Same as :func:`torch.load`.
strict (bool): Whether to allow different params for the model and
checkpoint.
logger (:mod:`logging.Logger` or None): The logger for error message.
Returns:
dict or OrderedDict: The loaded checkpoint.
"""
checkpoint = torch.load(filename, map_location=map_location)
# OrderedDict is a subclass of dict
if not isinstance(checkpoint, dict):
raise RuntimeError(
f'No state_dict found in checkpoint file {filename}')
# get state_dict from checkpoint
if 'state_dict' in checkpoint:
state_dict_tmp = checkpoint['state_dict']
else:
state_dict_tmp = checkpoint
state_dict = OrderedDict()
# strip prefix of state_dict
for k, v in state_dict_tmp.items():
if k.startswith('module.backbone.'):
state_dict[k[16:]] = v
elif k.startswith('module.'):
state_dict[k[7:]] = v
elif k.startswith('backbone.'):
state_dict[k[9:]] = v
else:
state_dict[k] = v
# load state_dict
load_state_dict(model, state_dict, strict, logger)
return checkpoint
def resize(input,
size=None,
scale_factor=None,
mode='nearest',
align_corners=None,
warning=True):
if warning:
if size is not None and align_corners:
input_h, input_w = int(input.shape[0]), int(input.shape[1])
output_h, output_w = int(size[0]), int(size[1])
if output_h > input_h or output_w > output_h:
if ((output_h > 1 and output_w > 1 and input_h > 1
and input_w > 1) and (output_h - 1) % (input_h - 1)
and (output_w - 1) % (input_w - 1)):
warnings.warn(
f'When align_corners={align_corners}, '
'the output would more aligned if '
f'input size {(input_h, input_w)} is `x+1` and '
f'out size {(output_h, output_w)} is `nx+1`')
def constant_init(module: nn.Module, val: float, bias: float = 0) -> None:
if hasattr(module, 'weight') and module.weight is not None:
nn.init.constant_(module.weight, val)
if hasattr(module, 'bias') and module.bias is not None:
nn.init.constant_(module.bias, bias)
def normal_init(module: nn.Module,
mean: float = 0,
std: float = 1,
bias: float = 0) -> None:
if hasattr(module, 'weight') and module.weight is not None:
nn.init.normal_(module.weight, mean, std)
if hasattr(module, 'bias') and module.bias is not None:
nn.init.constant_(module.bias, bias)
|