forked from mrq/DL-Art-School
79 lines
3.5 KiB
Python
79 lines
3.5 KiB
Python
import importlib
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import logging
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import os
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import pkgutil
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import sys
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from collections import OrderedDict
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from inspect import isfunction, getmembers, signature
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logger = logging.getLogger('base')
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class RegisteredModelNameError(Exception):
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def __init__(self, name_error):
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super().__init__(f'Registered DLAS modules must start with `register_`. Incorrect registration: {name_error}')
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# Decorator that allows API clients to show DLAS how to build a nn.Module from an opt dict.
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# Functions with this decorator should have a specific naming format:
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# `register_<name>` where <name> is the name that will be used in configuration files to reference this model.
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# Functions with this decorator are expected to take a single argument:
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# - opt: A dict with the configuration options for building the module.
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# They should return:
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# - A torch.nn.Module object for the model being defined.
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def register_model(func):
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if func.__name__.startswith("register_"):
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func._dlas_model_name = func.__name__[9:]
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assert func._dlas_model_name
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else:
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raise RegisteredModelNameError(func.__name__)
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func._dlas_registered_model = True
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return func
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# this had some weird kludge that I don't understand needing to have a reference frame around the current working directory
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# it works better when you set it relative to this file instead
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# however, this has very different behavior when importing DLAS from outside the repo, rather than spawning a shell instance to a script within it
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# I can't be assed to deal with that headache at the moment, I just want something to work right now without needing to touch a shell
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# inject.py has a similar loader scheme, be sure to mirror it if you touch this too
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def find_registered_model_fns(base_path='models'):
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found_fns = {}
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path = os.path.normpath(os.path.join(os.path.dirname(os.path.realpath(__file__)), f'../{base_path}'))
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module_iter = pkgutil.walk_packages([path])
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for mod in module_iter:
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if mod.ispkg:
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EXCLUSION_LIST = ['flownet2']
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if mod.name not in EXCLUSION_LIST:
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found_fns.update(find_registered_model_fns(f'{base_path}/{mod.name}'))
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else:
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mod_name = f'{base_path}/{mod.name}'.replace('/', '.')
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importlib.import_module(mod_name)
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for mod_fn in getmembers(sys.modules[mod_name], isfunction):
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if hasattr(mod_fn[1], "_dlas_registered_model"):
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found_fns[mod_fn[1]._dlas_model_name] = mod_fn[1]
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return found_fns
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class CreateModelError(Exception):
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def __init__(self, name, available):
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super().__init__(f'Could not find the specified model name: {name}. Tip: If your model is in a'
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f' subdirectory, that directory must contain an __init__.py to be scanned. Available models:'
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f'{available}')
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def create_model(opt, opt_net, other_nets=None):
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which_model = opt_net['which_model']
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# For backwards compatibility.
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if not which_model:
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which_model = opt_net['which_model_G']
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if not which_model:
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which_model = opt_net['which_model_D']
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registered_fns = find_registered_model_fns()
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if which_model not in registered_fns.keys():
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raise CreateModelError(which_model, list(registered_fns.keys()))
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num_params = len(signature(registered_fns[which_model]).parameters)
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if num_params == 2:
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return registered_fns[which_model](opt_net, opt)
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else:
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return registered_fns[which_model](opt_net, opt, other_nets) |