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210 lines
7.7 KiB
210 lines
7.7 KiB
""" Model Registry
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Hacked together by / Copyright 2020 Ross Wightman
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"""
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import fnmatch
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import re
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import sys
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from collections import defaultdict, deque
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from copy import deepcopy
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from typing import List, Optional, Union, Tuple
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from .pretrained import PretrainedCfg, DefaultCfg, split_model_name_tag
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__all__ = [
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'list_models', 'is_model', 'model_entrypoint', 'list_modules', 'is_model_in_modules',
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'get_pretrained_cfg_value', 'is_model_pretrained', 'get_arch_name']
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_module_to_models = defaultdict(set) # dict of sets to check membership of model in module
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_model_to_module = {} # mapping of model names to module names
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_model_entrypoints = {} # mapping of model names to architecture entrypoint fns
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_model_has_pretrained = set() # set of model names that have pretrained weight url present
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_model_default_cfgs = dict() # central repo for model arch -> default cfg objects
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_model_pretrained_cfgs = dict() # central repo for model arch + tag -> pretrained cfgs
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_model_with_tags = defaultdict(list) # shortcut to map each model arch to all model + tag names
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def get_arch_name(model_name: str) -> Tuple[str, Optional[str]]:
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return split_model_name_tag(model_name)[0]
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def register_model(fn):
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# lookup containing module
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mod = sys.modules[fn.__module__]
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module_name_split = fn.__module__.split('.')
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module_name = module_name_split[-1] if len(module_name_split) else ''
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# add model to __all__ in module
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model_name = fn.__name__
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if hasattr(mod, '__all__'):
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mod.__all__.append(model_name)
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else:
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mod.__all__ = [model_name]
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# add entries to registry dict/sets
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_model_entrypoints[model_name] = fn
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_model_to_module[model_name] = module_name
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_module_to_models[module_name].add(model_name)
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if hasattr(mod, 'default_cfgs') and model_name in mod.default_cfgs:
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# this will catch all models that have entrypoint matching cfg key, but miss any aliasing
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# entrypoints or non-matching combos
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cfg = mod.default_cfgs[model_name]
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if not isinstance(cfg, DefaultCfg):
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# new style default cfg dataclass w/ multiple entries per model-arch
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assert isinstance(cfg, dict)
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# old style cfg dict per model-arch
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cfg = PretrainedCfg(**cfg)
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cfg = DefaultCfg(tags=deque(['']), cfgs={'': cfg})
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for tag_idx, tag in enumerate(cfg.tags):
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is_default = tag_idx == 0
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pretrained_cfg = cfg.cfgs[tag]
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if is_default:
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_model_pretrained_cfgs[model_name] = pretrained_cfg
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if pretrained_cfg.has_weights:
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# add tagless entry if it's default and has weights
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_model_has_pretrained.add(model_name)
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if tag:
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model_name_tag = '.'.join([model_name, tag])
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_model_pretrained_cfgs[model_name_tag] = pretrained_cfg
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if pretrained_cfg.has_weights:
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# add model w/ tag if tag is valid
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_model_has_pretrained.add(model_name_tag)
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_model_with_tags[model_name].append(model_name_tag)
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else:
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_model_with_tags[model_name].append(model_name) # has empty tag (to slowly remove these instances)
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_model_default_cfgs[model_name] = cfg
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return fn
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def _natural_key(string_):
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return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())]
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def list_models(
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filter: Union[str, List[str]] = '',
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module: str = '',
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pretrained=False,
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exclude_filters: str = '',
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name_matches_cfg: bool = False,
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include_tags: Optional[bool] = None,
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):
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""" Return list of available model names, sorted alphabetically
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Args:
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filter (str) - Wildcard filter string that works with fnmatch
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module (str) - Limit model selection to a specific submodule (ie 'vision_transformer')
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pretrained (bool) - Include only models with valid pretrained weights if True
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exclude_filters (str or list[str]) - Wildcard filters to exclude models after including them with filter
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name_matches_cfg (bool) - Include only models w/ model_name matching default_cfg name (excludes some aliases)
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include_tags (Optional[boo]) - Include pretrained tags in model names (model.tag). If None, defaults
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set to True when pretrained=True else False (default: None)
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Example:
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model_list('gluon_resnet*') -- returns all models starting with 'gluon_resnet'
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model_list('*resnext*, 'resnet') -- returns all models with 'resnext' in 'resnet' module
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"""
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if include_tags is None:
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# FIXME should this be default behaviour? or default to include_tags=True?
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include_tags = pretrained
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if module:
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all_models = list(_module_to_models[module])
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else:
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all_models = _model_entrypoints.keys()
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if include_tags:
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# expand model names to include names w/ pretrained tags
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models_with_tags = []
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for m in all_models:
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models_with_tags.extend(_model_with_tags[m])
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all_models = models_with_tags
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if filter:
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models = []
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include_filters = filter if isinstance(filter, (tuple, list)) else [filter]
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for f in include_filters:
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include_models = fnmatch.filter(all_models, f) # include these models
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if len(include_models):
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models = set(models).union(include_models)
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else:
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models = all_models
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if exclude_filters:
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if not isinstance(exclude_filters, (tuple, list)):
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exclude_filters = [exclude_filters]
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for xf in exclude_filters:
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exclude_models = fnmatch.filter(models, xf) # exclude these models
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if len(exclude_models):
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models = set(models).difference(exclude_models)
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if pretrained:
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models = _model_has_pretrained.intersection(models)
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if name_matches_cfg:
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models = set(_model_pretrained_cfgs).intersection(models)
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return list(sorted(models, key=_natural_key))
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def list_pretrained(
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filter: Union[str, List[str]] = '',
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exclude_filters: str = '',
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):
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return list_models(
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filter=filter,
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pretrained=True,
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exclude_filters=exclude_filters,
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include_tags=True,
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)
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def is_model(model_name):
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""" Check if a model name exists
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"""
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arch_name = get_arch_name(model_name)
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return arch_name in _model_entrypoints
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def model_entrypoint(model_name):
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"""Fetch a model entrypoint for specified model name
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"""
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arch_name = get_arch_name(model_name)
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return _model_entrypoints[arch_name]
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def list_modules():
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""" Return list of module names that contain models / model entrypoints
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"""
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modules = _module_to_models.keys()
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return list(sorted(modules))
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def is_model_in_modules(model_name, module_names):
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"""Check if a model exists within a subset of modules
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Args:
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model_name (str) - name of model to check
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module_names (tuple, list, set) - names of modules to search in
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"""
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arch_name = get_arch_name(model_name)
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assert isinstance(module_names, (tuple, list, set))
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return any(arch_name in _module_to_models[n] for n in module_names)
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def is_model_pretrained(model_name):
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return model_name in _model_has_pretrained
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def get_pretrained_cfg(model_name):
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if model_name in _model_pretrained_cfgs:
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return deepcopy(_model_pretrained_cfgs[model_name])
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raise RuntimeError(f'No pretrained config exists for model {model_name}.')
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def get_pretrained_cfg_value(model_name, cfg_key):
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""" Get a specific model default_cfg value by key. None if key doesn't exist.
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"""
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if model_name in _model_pretrained_cfgs:
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return getattr(_model_pretrained_cfgs[model_name], cfg_key, None)
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raise RuntimeError(f'No pretrained config exist for model {model_name}.') |