""" Meta-learning modules based on: https://github.com/tristandeleu/pytorch-meta MIT License Copyright (c) 2019-2020 Tristan Deleu Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ import itertools import re from collections import OrderedDict import torch.nn as nn from shap_e.util.collections import AttrDict __all__ = [ "MetaModule", "subdict", "superdict", "leveldict", "leveliter", "batch_meta_parameters", "batch_meta_state_dict", ] def subdict(dictionary, key=None): if dictionary is None: return None if (key is None) or (key == ""): return dictionary key_re = re.compile(r"^{0}\.(.+)".format(re.escape(key))) return AttrDict( OrderedDict( (key_re.sub(r"\1", k), value) for (k, value) in dictionary.items() if key_re.match(k) is not None ) ) def superdict(dictionary, key=None): if dictionary is None: return None if (key is None) or (key == ""): return dictionary return AttrDict(OrderedDict((key + "." + k, value) for (k, value) in dictionary.items())) def leveldict(dictionary, depth=0): return AttrDict(leveliter(dictionary, depth=depth)) def leveliter(dictionary, depth=0): """ depth == 0 is root """ for key, value in dictionary.items(): if key.count(".") == depth: yield key, value class MetaModule(nn.Module): """ Base class for PyTorch meta-learning modules. These modules accept an additional argument `params` in their `forward` method. Notes ----- Objects inherited from `MetaModule` are fully compatible with PyTorch modules from `torch.nn.Module`. The argument `params` is a dictionary of tensors, with full support of the computation graph (for differentiation). Based on SIREN's torchmeta with some additional features/changes. All meta weights must not have the batch dimension, as they are later tiled to the given batch size after unsqueezing the first dimension (e.g. a weight of dimension [d_out x d_in] is tiled to have the dimension [batch x d_out x d_in]). Requiring all meta weights to have a batch dimension of 1 (e.g. [1 x d_out x d_in] from the earlier example) could be a more natural choice, but this results in silent failures. """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self._meta_state_dict = set() self._meta_params = set() def register_meta_buffer(self, name: str, param: nn.Parameter): """ Registers a trainable or nontrainable parameter as a meta buffer. This can be later retrieved by meta_state_dict """ self.register_buffer(name, param) self._meta_state_dict.add(name) def register_meta_parameter(self, name: str, parameter: nn.Parameter): """ Registers a meta parameter so it is included in named_meta_parameters and meta_state_dict. """ self.register_parameter(name, parameter) self._meta_params.add(name) self._meta_state_dict.add(name) def register_meta(self, name: str, parameter: nn.Parameter, trainable: bool = True): if trainable: self.register_meta_parameter(name, parameter) else: self.register_meta_buffer(name, parameter) def register(self, name: str, parameter: nn.Parameter, meta: bool, trainable: bool = True): if meta: if trainable: self.register_meta_parameter(name, parameter) else: self.register_meta_buffer(name, parameter) else: if trainable: self.register_parameter(name, parameter) else: self.register_buffer(name, parameter) def named_meta_parameters(self, prefix="", recurse=True): """ Returns an iterator over all the names and meta parameters """ def meta_iterator(module): meta = module._meta_params if isinstance(module, MetaModule) else set() for name, param in module._parameters.items(): if name in meta: yield name, param gen = self._named_members( meta_iterator, prefix=prefix, recurse=recurse, ) for name, param in gen: yield name, param def named_nonmeta_parameters(self, prefix="", recurse=True): def _iterator(module): meta = module._meta_params if isinstance(module, MetaModule) else set() for name, param in module._parameters.items(): if name not in meta: yield name, param gen = self._named_members( _iterator, prefix=prefix, recurse=recurse, ) for name, param in gen: yield name, param def nonmeta_parameters(self, prefix="", recurse=True): for _, param in self.named_nonmeta_parameters(prefix=prefix, recurse=recurse): yield param def meta_state_dict(self, prefix="", recurse=True): """ Returns an iterator over all the names and meta parameters/buffers. One difference between module.state_dict() is that this preserves requires_grad, because we may want to compute the gradient w.r.t. meta buffers, but don't necessarily update them automatically. """ def meta_iterator(module): meta = module._meta_state_dict if isinstance(module, MetaModule) else set() for name, param in itertools.chain(module._buffers.items(), module._parameters.items()): if name in meta: yield name, param gen = self._named_members( meta_iterator, prefix=prefix, recurse=recurse, ) return dict(gen) def update(self, params=None): """ Updates the parameter list before the forward prop so that if `params` is None or doesn't have a certain key, the module uses the default parameter/buffer registered in the module. """ if params is None: params = AttrDict() params = AttrDict(params) named_params = set([name for name, _ in self.named_parameters()]) for name, param in self.named_parameters(): params.setdefault(name, param) for name, param in self.state_dict().items(): if name not in named_params: params.setdefault(name, param) return params def batch_meta_parameters(net, batch_size): params = AttrDict() for name, param in net.named_meta_parameters(): params[name] = param.clone().unsqueeze(0).repeat(batch_size, *[1] * len(param.shape)) return params def batch_meta_state_dict(net, batch_size): state_dict = AttrDict() meta_parameters = set([name for name, _ in net.named_meta_parameters()]) for name, param in net.meta_state_dict().items(): state_dict[name] = param.clone().unsqueeze(0).repeat(batch_size, *[1] * len(param.shape)) return state_dict