Ross Wightman
6 years ago
parent
8512436436
commit
171c0b88b6
@ -1,2 +1,2 @@
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from .version import __version__
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from .models import create_model
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from .models import create_model, list_models, is_model, list_modules, model_entrypoint
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@ -0,0 +1,44 @@
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from .registry import is_model, is_model_in_modules, model_entrypoint
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from .helpers import load_checkpoint
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def create_model(
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model_name,
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pretrained=False,
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num_classes=1000,
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in_chans=3,
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checkpoint_path='',
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**kwargs):
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"""Create a model
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Args:
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model_name (str): name of model to instantiate
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pretrained (bool): load pretrained ImageNet-1k weights if true
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num_classes (int): number of classes for final fully connected layer (default: 1000)
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in_chans (int): number of input channels / colors (default: 3)
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checkpoint_path (str): path of checkpoint to load after model is initialized
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Keyword Args:
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drop_rate (float): dropout rate for training (default: 0.0)
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global_pool (str): global pool type (default: 'avg')
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**: other kwargs are model specific
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"""
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margs = dict(pretrained=pretrained, num_classes=num_classes, in_chans=in_chans)
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# Not all models have support for batchnorm params passed as args, only gen_efficientnet variants
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supports_bn_params = is_model_in_modules(model_name, ['gen_efficientnet'])
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if not supports_bn_params and any([x in kwargs for x in ['bn_tf', 'bn_momentum', 'bn_eps']]):
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kwargs.pop('bn_tf', None)
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kwargs.pop('bn_momentum', None)
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kwargs.pop('bn_eps', None)
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if is_model(model_name):
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create_fn = model_entrypoint(model_name)
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model = create_fn(**margs, **kwargs)
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else:
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raise RuntimeError('Unknown model (%s)' % model_name)
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if checkpoint_path:
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load_checkpoint(model, checkpoint_path)
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return model
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@ -1,42 +0,0 @@
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from .inception_v4 import *
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from .inception_resnet_v2 import *
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from .densenet import *
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from .resnet import *
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from .dpn import *
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from .senet import *
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from .xception import *
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from .pnasnet import *
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from .gen_efficientnet import *
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from .inception_v3 import *
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from .gluon_resnet import *
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from .helpers import load_checkpoint
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def create_model(
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model_name,
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pretrained=False,
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num_classes=1000,
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in_chans=3,
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checkpoint_path='',
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**kwargs):
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margs = dict(pretrained=pretrained, num_classes=num_classes, in_chans=in_chans)
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# Not all models have support for batchnorm params passed as args, only gen_efficientnet variants
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supports_bn_params = model_name in gen_efficientnet_model_names()
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if not supports_bn_params and any([x in kwargs for x in ['bn_tf', 'bn_momentum', 'bn_eps']]):
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kwargs.pop('bn_tf', None)
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kwargs.pop('bn_momentum', None)
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kwargs.pop('bn_eps', None)
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if model_name in globals():
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create_fn = globals()[model_name]
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model = create_fn(**margs, **kwargs)
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else:
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raise RuntimeError('Unknown model (%s)' % model_name)
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if checkpoint_path:
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load_checkpoint(model, checkpoint_path)
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return model
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@ -0,0 +1,78 @@
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import sys
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import re
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import fnmatch
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from collections import defaultdict
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__all__ = ['list_models', 'is_model', 'model_entrypoint', 'list_modules', 'is_model_in_modules']
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_module_to_models = defaultdict(set)
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_model_to_module = {}
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_model_entrypoints = {}
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def register_model(fn):
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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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if hasattr(mod, '__all__'):
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mod.__all__.append(fn.__name__)
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else:
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mod.__all__ = [fn.__name__]
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_model_entrypoints[fn.__name__] = fn
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_model_to_module[fn.__name__] = module_name
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_module_to_models[module_name].add(fn.__name__)
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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(filter='', module=''):
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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 sub-module (ie 'gen_efficientnet')
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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 module:
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models = list(_module_to_models[module])
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else:
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models = _model_entrypoints.keys()
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if filter:
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models = fnmatch.filter(models, filter)
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return list(sorted(models, key=_natural_key))
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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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return model_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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return _model_entrypoints[model_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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assert isinstance(module_names, (tuple, list, set))
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return any(model_name in _module_to_models[n] for n in module_names)
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