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396 lines
16 KiB
396 lines
16 KiB
import dataclasses
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import logging
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from copy import deepcopy
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from typing import Optional, Dict, Callable, Any, Tuple
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from torch import nn as nn
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from torch.hub import load_state_dict_from_url
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from timm.models._features import FeatureListNet, FeatureHookNet
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from timm.models._features_fx import FeatureGraphNet
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from timm.models._helpers import load_state_dict
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from timm.models._hub import has_hf_hub, download_cached_file, load_state_dict_from_hf
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from timm.models._manipulate import adapt_input_conv
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from timm.models._pretrained import PretrainedCfg
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from timm.models._prune import adapt_model_from_file
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from timm.models._registry import get_pretrained_cfg
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_logger = logging.getLogger(__name__)
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# Global variables for rarely used pretrained checkpoint download progress and hash check.
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# Use set_pretrained_download_progress / set_pretrained_check_hash functions to toggle.
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_DOWNLOAD_PROGRESS = False
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_CHECK_HASH = False
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def _resolve_pretrained_source(pretrained_cfg):
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cfg_source = pretrained_cfg.get('source', '')
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pretrained_url = pretrained_cfg.get('url', None)
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pretrained_file = pretrained_cfg.get('file', None)
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hf_hub_id = pretrained_cfg.get('hf_hub_id', None)
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# resolve where to load pretrained weights from
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load_from = ''
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pretrained_loc = ''
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if cfg_source == 'hf-hub' and has_hf_hub(necessary=True):
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# hf-hub specified as source via model identifier
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load_from = 'hf-hub'
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assert hf_hub_id
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pretrained_loc = hf_hub_id
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else:
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# default source == timm or unspecified
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if pretrained_file:
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load_from = 'file'
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pretrained_loc = pretrained_file
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elif pretrained_url:
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load_from = 'url'
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pretrained_loc = pretrained_url
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elif hf_hub_id and has_hf_hub(necessary=True):
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# hf-hub available as alternate weight source in default_cfg
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load_from = 'hf-hub'
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pretrained_loc = hf_hub_id
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if load_from == 'hf-hub' and pretrained_cfg.get('hf_hub_filename', None):
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# if a filename override is set, return tuple for location w/ (hub_id, filename)
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pretrained_loc = pretrained_loc, pretrained_cfg['hf_hub_filename']
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return load_from, pretrained_loc
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def set_pretrained_download_progress(enable=True):
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""" Set download progress for pretrained weights on/off (globally). """
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global _DOWNLOAD_PROGRESS
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_DOWNLOAD_PROGRESS = enable
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def set_pretrained_check_hash(enable=True):
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""" Set hash checking for pretrained weights on/off (globally). """
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global _CHECK_HASH
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_CHECK_HASH = enable
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def load_custom_pretrained(
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model: nn.Module,
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pretrained_cfg: Optional[Dict] = None,
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load_fn: Optional[Callable] = None,
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):
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r"""Loads a custom (read non .pth) weight file
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Downloads checkpoint file into cache-dir like torch.hub based loaders, but calls
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a passed in custom load fun, or the `load_pretrained` model member fn.
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If the object is already present in `model_dir`, it's deserialized and returned.
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The default value of `model_dir` is ``<hub_dir>/checkpoints`` where
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`hub_dir` is the directory returned by :func:`~torch.hub.get_dir`.
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Args:
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model: The instantiated model to load weights into
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pretrained_cfg (dict): Default pretrained model cfg
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load_fn: An external standalone fn that loads weights into provided model, otherwise a fn named
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'laod_pretrained' on the model will be called if it exists
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"""
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pretrained_cfg = pretrained_cfg or getattr(model, 'pretrained_cfg', None)
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if not pretrained_cfg:
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_logger.warning("Invalid pretrained config, cannot load weights.")
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return
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load_from, pretrained_loc = _resolve_pretrained_source(pretrained_cfg)
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if not load_from:
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_logger.warning("No pretrained weights exist for this model. Using random initialization.")
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return
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if load_from == 'hf-hub': # FIXME
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_logger.warning("Hugging Face hub not currently supported for custom load pretrained models.")
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elif load_from == 'url':
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pretrained_loc = download_cached_file(
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pretrained_loc,
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check_hash=_CHECK_HASH,
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progress=_DOWNLOAD_PROGRESS
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)
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if load_fn is not None:
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load_fn(model, pretrained_loc)
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elif hasattr(model, 'load_pretrained'):
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model.load_pretrained(pretrained_loc)
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else:
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_logger.warning("Valid function to load pretrained weights is not available, using random initialization.")
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def load_pretrained(
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model: nn.Module,
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pretrained_cfg: Optional[Dict] = None,
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num_classes: int = 1000,
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in_chans: int = 3,
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filter_fn: Optional[Callable] = None,
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strict: bool = True,
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):
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""" Load pretrained checkpoint
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Args:
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model (nn.Module) : PyTorch model module
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pretrained_cfg (Optional[Dict]): configuration for pretrained weights / target dataset
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num_classes (int): num_classes for target model
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in_chans (int): in_chans for target model
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filter_fn (Optional[Callable]): state_dict filter fn for load (takes state_dict, model as args)
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strict (bool): strict load of checkpoint
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"""
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pretrained_cfg = pretrained_cfg or getattr(model, 'pretrained_cfg', None)
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if not pretrained_cfg:
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_logger.warning("Invalid pretrained config, cannot load weights.")
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return
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load_from, pretrained_loc = _resolve_pretrained_source(pretrained_cfg)
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if load_from == 'file':
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_logger.info(f'Loading pretrained weights from file ({pretrained_loc})')
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state_dict = load_state_dict(pretrained_loc)
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elif load_from == 'url':
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_logger.info(f'Loading pretrained weights from url ({pretrained_loc})')
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state_dict = load_state_dict_from_url(
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pretrained_loc,
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map_location='cpu',
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progress=_DOWNLOAD_PROGRESS,
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check_hash=_CHECK_HASH,
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)
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elif load_from == 'hf-hub':
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_logger.info(f'Loading pretrained weights from Hugging Face hub ({pretrained_loc})')
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if isinstance(pretrained_loc, (list, tuple)):
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state_dict = load_state_dict_from_hf(*pretrained_loc)
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else:
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state_dict = load_state_dict_from_hf(pretrained_loc)
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else:
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_logger.warning("No pretrained weights exist or were found for this model. Using random initialization.")
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return
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if filter_fn is not None:
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# for backwards compat with filter fn that take one arg, try one first, the two
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try:
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state_dict = filter_fn(state_dict)
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except TypeError:
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state_dict = filter_fn(state_dict, model)
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input_convs = pretrained_cfg.get('first_conv', None)
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if input_convs is not None and in_chans != 3:
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if isinstance(input_convs, str):
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input_convs = (input_convs,)
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for input_conv_name in input_convs:
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weight_name = input_conv_name + '.weight'
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try:
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state_dict[weight_name] = adapt_input_conv(in_chans, state_dict[weight_name])
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_logger.info(
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f'Converted input conv {input_conv_name} pretrained weights from 3 to {in_chans} channel(s)')
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except NotImplementedError as e:
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del state_dict[weight_name]
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strict = False
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_logger.warning(
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f'Unable to convert pretrained {input_conv_name} weights, using random init for this layer.')
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classifiers = pretrained_cfg.get('classifier', None)
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label_offset = pretrained_cfg.get('label_offset', 0)
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if classifiers is not None:
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if isinstance(classifiers, str):
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classifiers = (classifiers,)
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if num_classes != pretrained_cfg['num_classes']:
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for classifier_name in classifiers:
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# completely discard fully connected if model num_classes doesn't match pretrained weights
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state_dict.pop(classifier_name + '.weight', None)
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state_dict.pop(classifier_name + '.bias', None)
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strict = False
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elif label_offset > 0:
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for classifier_name in classifiers:
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# special case for pretrained weights with an extra background class in pretrained weights
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classifier_weight = state_dict[classifier_name + '.weight']
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state_dict[classifier_name + '.weight'] = classifier_weight[label_offset:]
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classifier_bias = state_dict[classifier_name + '.bias']
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state_dict[classifier_name + '.bias'] = classifier_bias[label_offset:]
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model.load_state_dict(state_dict, strict=strict)
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def pretrained_cfg_for_features(pretrained_cfg):
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pretrained_cfg = deepcopy(pretrained_cfg)
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# remove default pretrained cfg fields that don't have much relevance for feature backbone
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to_remove = ('num_classes', 'classifier', 'global_pool') # add default final pool size?
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for tr in to_remove:
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pretrained_cfg.pop(tr, None)
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return pretrained_cfg
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def _filter_kwargs(kwargs, names):
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if not kwargs or not names:
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return
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for n in names:
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kwargs.pop(n, None)
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def _update_default_kwargs(pretrained_cfg, kwargs, kwargs_filter):
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""" Update the default_cfg and kwargs before passing to model
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Args:
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pretrained_cfg: input pretrained cfg (updated in-place)
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kwargs: keyword args passed to model build fn (updated in-place)
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kwargs_filter: keyword arg keys that must be removed before model __init__
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"""
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# Set model __init__ args that can be determined by default_cfg (if not already passed as kwargs)
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default_kwarg_names = ('num_classes', 'global_pool', 'in_chans')
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if pretrained_cfg.get('fixed_input_size', False):
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# if fixed_input_size exists and is True, model takes an img_size arg that fixes its input size
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default_kwarg_names += ('img_size',)
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for n in default_kwarg_names:
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# for legacy reasons, model __init__args uses img_size + in_chans as separate args while
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# pretrained_cfg has one input_size=(C, H ,W) entry
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if n == 'img_size':
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input_size = pretrained_cfg.get('input_size', None)
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if input_size is not None:
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assert len(input_size) == 3
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kwargs.setdefault(n, input_size[-2:])
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elif n == 'in_chans':
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input_size = pretrained_cfg.get('input_size', None)
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if input_size is not None:
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assert len(input_size) == 3
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kwargs.setdefault(n, input_size[0])
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else:
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default_val = pretrained_cfg.get(n, None)
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if default_val is not None:
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kwargs.setdefault(n, pretrained_cfg[n])
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# Filter keyword args for task specific model variants (some 'features only' models, etc.)
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_filter_kwargs(kwargs, names=kwargs_filter)
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def resolve_pretrained_cfg(
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variant: str,
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pretrained_cfg=None,
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pretrained_cfg_overlay=None,
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) -> PretrainedCfg:
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model_with_tag = variant
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pretrained_tag = None
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if pretrained_cfg:
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if isinstance(pretrained_cfg, dict):
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# pretrained_cfg dict passed as arg, validate by converting to PretrainedCfg
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pretrained_cfg = PretrainedCfg(**pretrained_cfg)
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elif isinstance(pretrained_cfg, str):
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pretrained_tag = pretrained_cfg
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pretrained_cfg = None
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# fallback to looking up pretrained cfg in model registry by variant identifier
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if not pretrained_cfg:
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if pretrained_tag:
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model_with_tag = '.'.join([variant, pretrained_tag])
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pretrained_cfg = get_pretrained_cfg(model_with_tag)
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if not pretrained_cfg:
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_logger.warning(
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f"No pretrained configuration specified for {model_with_tag} model. Using a default."
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f" Please add a config to the model pretrained_cfg registry or pass explicitly.")
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pretrained_cfg = PretrainedCfg() # instance with defaults
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pretrained_cfg_overlay = pretrained_cfg_overlay or {}
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if not pretrained_cfg.architecture:
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pretrained_cfg_overlay.setdefault('architecture', variant)
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pretrained_cfg = dataclasses.replace(pretrained_cfg, **pretrained_cfg_overlay)
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return pretrained_cfg
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def build_model_with_cfg(
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model_cls: Callable,
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variant: str,
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pretrained: bool,
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pretrained_cfg: Optional[Dict] = None,
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pretrained_cfg_overlay: Optional[Dict] = None,
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model_cfg: Optional[Any] = None,
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feature_cfg: Optional[Dict] = None,
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pretrained_strict: bool = True,
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pretrained_filter_fn: Optional[Callable] = None,
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kwargs_filter: Optional[Tuple[str]] = None,
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**kwargs,
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):
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""" Build model with specified default_cfg and optional model_cfg
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This helper fn aids in the construction of a model including:
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* handling default_cfg and associated pretrained weight loading
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* passing through optional model_cfg for models with config based arch spec
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* features_only model adaptation
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* pruning config / model adaptation
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Args:
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model_cls (nn.Module): model class
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variant (str): model variant name
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pretrained (bool): load pretrained weights
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pretrained_cfg (dict): model's pretrained weight/task config
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model_cfg (Optional[Dict]): model's architecture config
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feature_cfg (Optional[Dict]: feature extraction adapter config
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pretrained_strict (bool): load pretrained weights strictly
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pretrained_filter_fn (Optional[Callable]): filter callable for pretrained weights
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kwargs_filter (Optional[Tuple]): kwargs to filter before passing to model
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**kwargs: model args passed through to model __init__
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"""
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pruned = kwargs.pop('pruned', False)
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features = False
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feature_cfg = feature_cfg or {}
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# resolve and update model pretrained config and model kwargs
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pretrained_cfg = resolve_pretrained_cfg(
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variant,
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pretrained_cfg=pretrained_cfg,
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pretrained_cfg_overlay=pretrained_cfg_overlay
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)
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# FIXME converting back to dict, PretrainedCfg use should be propagated further, but not into model
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pretrained_cfg = pretrained_cfg.to_dict()
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_update_default_kwargs(pretrained_cfg, kwargs, kwargs_filter)
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# Setup for feature extraction wrapper done at end of this fn
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if kwargs.pop('features_only', False):
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features = True
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feature_cfg.setdefault('out_indices', (0, 1, 2, 3, 4))
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if 'out_indices' in kwargs:
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feature_cfg['out_indices'] = kwargs.pop('out_indices')
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# Instantiate the model
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if model_cfg is None:
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model = model_cls(**kwargs)
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else:
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model = model_cls(cfg=model_cfg, **kwargs)
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model.pretrained_cfg = pretrained_cfg
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model.default_cfg = model.pretrained_cfg # alias for backwards compat
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if pruned:
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model = adapt_model_from_file(model, variant)
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# For classification models, check class attr, then kwargs, then default to 1k, otherwise 0 for feats
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num_classes_pretrained = 0 if features else getattr(model, 'num_classes', kwargs.get('num_classes', 1000))
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if pretrained:
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if pretrained_cfg.get('custom_load', False):
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load_custom_pretrained(
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model,
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pretrained_cfg=pretrained_cfg,
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)
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else:
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load_pretrained(
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model,
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pretrained_cfg=pretrained_cfg,
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num_classes=num_classes_pretrained,
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in_chans=kwargs.get('in_chans', 3),
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filter_fn=pretrained_filter_fn,
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strict=pretrained_strict,
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)
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# Wrap the model in a feature extraction module if enabled
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if features:
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feature_cls = FeatureListNet
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if 'feature_cls' in feature_cfg:
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feature_cls = feature_cfg.pop('feature_cls')
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if isinstance(feature_cls, str):
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feature_cls = feature_cls.lower()
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if 'hook' in feature_cls:
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feature_cls = FeatureHookNet
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elif feature_cls == 'fx':
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feature_cls = FeatureGraphNet
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else:
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assert False, f'Unknown feature class {feature_cls}'
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model = feature_cls(model, **feature_cfg)
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model.pretrained_cfg = pretrained_cfg_for_features(pretrained_cfg) # add back default_cfg
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model.default_cfg = model.pretrained_cfg # alias for backwards compat
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return model
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