进行py文件中函数调用时，只能调用其中一个函数，请问下是为什么？（只能调用resolve_data_config，无法调用resolve_model_data_config）
报如下错误：
AttributeError: module 'timm.data.config' has no attribute 'resolve_model_data_config'. Did you mean: 'resolve_data_config'?
以下为所调用函数的来源：
import logging
from .constants import *
_logger = logging.getLogger(__name__)
def resolve_data_config(
args=None,
pretrained_cfg=None,
model=None,
use_test_size=False,
verbose=False
):
assert model or args or pretrained_cfg, "At least one of model, args, or pretrained_cfg required for data config."
args = args or {}
pretrained_cfg = pretrained_cfg or {}
if not pretrained_cfg and model is not None and hasattr(model, 'pretrained_cfg'):
pretrained_cfg = model.pretrained_cfg
data_config = {}
# Resolve input/image size
in_chans = 3
if args.get('chans', None) is not None:
in_chans = args['chans']
input_size = (in_chans, 224, 224)
if args.get('input_size', None) is not None:
assert isinstance(args['input_size'], (tuple, list))
assert len(args['input_size']) == 3
input_size = tuple(args['input_size'])
in_chans = input_size[0] # input_size overrides in_chans
elif args.get('img_size', None) is not None:
assert isinstance(args['img_size'], int)
input_size = (in_chans, args['img_size'], args['img_size'])
else:
if use_test_size and pretrained_cfg.get('test_input_size', None) is not None:
input_size = pretrained_cfg['test_input_size']
elif pretrained_cfg.get('input_size', None) is not None:
input_size = pretrained_cfg['input_size']
data_config['input_size'] = input_size
# resolve interpolation method
data_config['interpolation'] = 'bicubic'
if args.get('interpolation', None):
data_config['interpolation'] = args['interpolation']
elif pretrained_cfg.get('interpolation', None):
data_config['interpolation'] = pretrained_cfg['interpolation']
# resolve dataset + model mean for normalization
data_config['mean'] = IMAGENET_DEFAULT_MEAN
if args.get('mean', None) is not None:
mean = tuple(args['mean'])
if len(mean) == 1:
mean = tuple(list(mean) * in_chans)
else:
assert len(mean) == in_chans
data_config['mean'] = mean
elif pretrained_cfg.get('mean', None):
data_config['mean'] = pretrained_cfg['mean']
# resolve dataset + model std deviation for normalization
data_config['std'] = IMAGENET_DEFAULT_STD
if args.get('std', None) is not None:
std = tuple(args['std'])
if len(std) == 1:
std = tuple(list(std) * in_chans)
else:
assert len(std) == in_chans
data_config['std'] = std
elif pretrained_cfg.get('std', None):
data_config['std'] = pretrained_cfg['std']
# resolve default inference crop
crop_pct = DEFAULT_CROP_PCT
if args.get('crop_pct', None):
crop_pct = args['crop_pct']
else:
if use_test_size and pretrained_cfg.get('test_crop_pct', None):
crop_pct = pretrained_cfg['test_crop_pct']
elif pretrained_cfg.get('crop_pct', None):
crop_pct = pretrained_cfg['crop_pct']
data_config['crop_pct'] = crop_pct
# resolve default crop percentage
crop_mode = DEFAULT_CROP_MODE
if args.get('crop_mode', None):
crop_mode = args['crop_mode']
elif pretrained_cfg.get('crop_mode', None):
crop_mode = pretrained_cfg['crop_mode']
data_config['crop_mode'] = crop_mode
if verbose:
_logger.info('Data processing configuration for current model + dataset:')
for n, v in data_config.items():
_logger.info('\t%s: %s' % (n, str(v)))
return data_config
def resolve_model_data_config(
model,
args=None,
pretrained_cfg=None,
use_test_size=False,
verbose=False,
):
""" Resolve Model Data Config
This is equivalent to resolve_data_config() but with arguments re-ordered to put model first.
Args:
model (nn.Module): the model instance
args (dict): command line arguments / configuration in dict form (overrides pretrained_cfg)
pretrained_cfg (dict): pretrained model config (overrides pretrained_cfg attached to model)
use_test_size (bool): use the test time input resolution (if one exists) instead of default train resolution
verbose (bool): enable extra logging of resolved values
Returns:
dictionary of config
"""
return resolve_data_config(
args=args,
pretrained_cfg=pretrained_cfg,
model=model,
use_test_size=use_test_size,
verbose=verbose,
)
看源码，resolve_model_data_config里面也是在调用resolve_data_config
那你直接调用resolve_data_config就好，为什么纠结另一个调用不了呢
该回答引用GPTᴼᴾᴱᴺᴬᴵ
从您提供的代码中，我注意到这里有两个函数 resolve_data_config 和 resolve_model_data_config，但是您只能调用 resolve_data_config 函数，无法调用 resolve_model_data_config 函数，且报错信息提示 timm.data.config 模块没有名为 resolve_model_data_config 的属性。
根据代码，两个函数的参数列表和返回值都一样，但是它们的参数顺序不同。resolve_data_config 函数的第一个参数是 args，而 resolve_model_data_config 函数的第一个参数是 model。所以在调用函数时，需要注意传入参数的顺序，确保传入的参数位置与函数定义一致。
具体来说，调用 resolve_data_config 函数时，需要按照以下顺序传入参数：
resolve_data_config(args=args, pretrained_cfg=pretrained_cfg, model=model, use_test_size=use_test_size, verbose=verbose)
而调用 resolve_model_data_config 函数时，需要按照以下顺序传入参数：
resolve_model_data_config(model=model, args=args, pretrained_cfg=pretrained_cfg, use_test_size=use_test_size, verbose=verbose)
如果还有问题，请随时提出，我会尽力帮您解决。