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