Source code for lumin.nn.models.model_builder

from typing import Dict, Union, Any, Callable, Tuple, Optional, List, Iterator
import pickle
import math
import numpy as np
import warnings
from distutils.version import LooseVersion

import torch.nn as nn
import torch.optim as optim
from torch.tensor import Tensor
import torch

from .layers.activations import lookup_act
from .initialisations import lookup_normal_init
from .helpers import CatEmbedder
from .blocks.body import FullyConnected, AbsBody
from .blocks.head import CatEmbHead, AbsHead
from .blocks.tail import ClassRegMulti, AbsTail
from ..losses.basic_weighted import WeightedCCE, WeightedMSE
from ...utils.misc import to_device

__all__ = ['ModelBuilder']

- Better typing for nn._WeightedLoss

[docs]class ModelBuilder(object): r''' Class to build models to specified architecture on demand along with an optimiser. Arguments: objective: string representation of network objective, i.e. 'classification', 'regression', 'multiclass' n_out: number of outputs required cont_feats: list of names of continuous input features model_args: dictionary of dictionaries of keyword arguments to pass to head, body, and tail to control architrcture opt_args: dictionary of arguments to pass to optimiser. Missing kargs will be filled with default values. Currently, only ADAM (default), and SGD are available. cat_embedder: :class:`~lumin.nn.models.helpers.CatEmbedder` for embedding categorical inputs cont_subsample_rate: if between in range (0, 1), will randomly select a fraction of continuous features (rounded upwards) to use as inputs guaranteed_feats: if subsampling features, will always include the features listed here, which count towards the subsample fraction loss: either and uninstantiated loss class, or leave as 'auto' to select loss according to objective head: uninstantiated class which can receive input data and upscale it to model width body: uninstantiated class which implements the main bulk of the model's hidden layers tail: uninstantiated class which scales the body to the required number of outputs and implements any final activation function and output scaling lookup_init: function taking choice of activation function, number of inputs, and number of outputs an returning a function to initialise layer weights. lookup_act: function taking choice of activation function and returning an activation function layer pretrain_file: if set, will load saved parameters for entire network from saved model freeze_head: whether to start with the head parameters set to untrainable freeze_body: whether to start with the body parameters set to untrainable Examples:: >>> model_builder = ModelBuilder(objective='classifier', >>> cont_feats=cont_feats, n_out=1, >>> model_args={'body':{'depth':4, >>> 'width':100}}) >>> >>> min_targs = np.min(targets, axis=0).reshape(targets.shape[1],1) >>> max_targs = np.max(targets, axis=0).reshape(targets.shape[1],1) >>> min_targs[min_targs > 0] *=0.8 >>> min_targs[min_targs < 0] *=1.2 >>> max_targs[max_targs > 0] *=1.2 >>> max_targs[max_targs < 0] *=0.8 >>> y_range = np.hstack((min_targs, max_targs)) >>> model_builder = ModelBuilder( >>> objective='regression', cont_feats=cont_feats, n_out=6, >>> cat_embedder=CatEmbedder.from_fy(train_fy), >>> model_args={'body':{'depth':4, 'width':100}, >>> 'tail':{y_range=y_range}) >>> >>> model_builder = ModelBuilder(objective='multiclassifier', >>> cont_feats=cont_feats, n_out=5, >>> model_args={'body':{'width':100, >>> 'depth':6, >>> 'do':0.1, >>> 'res':True}}) >>> >>> model_builder = ModelBuilder(objective='classifier', >>> cont_feats=cont_feats, n_out=1, >>> model_args={'body':{'depth':4, >>> 'width':100}}, >>> opt_args={'opt':'sgd', >>> 'momentum':0.8, >>> 'weight_decay':1e-5}, >>> loss=partial(SignificanceLoss, >>> sig_weight=sig_weight, >>> bkg_weight=bkg_weight, >>> func=calc_ams_torch)) ''' # TODO: Make opt use partials rather than strings # TODO: Classmethod from_fy # TODO: Check examples # TODO: Factor out cat_embedder to head # TODO: Add matrix example # TODO: Add partial block example def __init__(self, objective:str, n_out:int, cont_feats:Optional[List[str]]=None, model_args:Optional[Dict[str,Dict[str,Any]]]=None, opt_args:Optional[Dict[str,Any]]=None, cat_embedder:Optional[CatEmbedder]=None, cont_subsample_rate:Optional[float]=None, guaranteed_feats:Optional[List[str]]=None, loss:Any='auto', head:Callable[[Any],AbsHead]=CatEmbHead, body:Callable[[Any],AbsBody]=FullyConnected, tail:Callable[[Any],AbsTail]=ClassRegMulti, lookup_init:Callable[[str,Optional[int],Optional[int]],Callable[[Tensor],None]]=lookup_normal_init, lookup_act:Callable[[str],nn.Module]=lookup_act, pretrain_file:Optional[str]=None, freeze_head:bool=False, freeze_body:bool=False, freeze_tail:bool=False): self.objective,self.cont_feats,self.n_out,self.cat_embedder = objective.lower(),cont_feats,n_out,cat_embedder self.cont_subsample_rate,self.guaranteed_feats = cont_subsample_rate,guaranteed_feats self.head,self.body,self.tail = head,body,tail self.lookup_init,self.lookup_act,self.pretrain_file, = lookup_init,lookup_act,pretrain_file self.freeze_head,self.freeze_body,self.freeze_tail = freeze_head,freeze_body,freeze_tail self._parse_loss(loss) self._parse_model_args(model_args) self._parse_opt_args(opt_args) self._subsample()
[docs] @classmethod def from_model_builder(cls, model_builder, pretrain_file:Optional[str]=None, freeze_head:bool=False, freeze_body:bool=False, freeze_tail:bool=False, loss:Optional[Any]=None, opt_args:Optional[Dict[str,Any]]=None): r''' Instantiate a :class:`~lumin.nn.models.model_builder.ModelBuilder` from an exisitng :class:`~lumin.nn.models.model_builder.ModelBuilder`, but with options to adjust loss, optimiser, pretraining, and module freezing Arguments: model_builder: existing :class:`~lumin.nn.models.model_builder.ModelBuilder` or filename for a pickled :class:`~lumin.nn.models.model_builder.ModelBuilder` pretrain_file: if set, will load saved parameters for entire network from saved model freeze_head: whether to start with the head parameters set to untrainable freeze_body: whether to start with the body parameters set to untrainable freeze_tail: whether to start with the tail parameters set to untrainable loss: either and uninstantiated loss class, or leave as 'auto' to select loss according to objective opt_args: dictionary of arguments to pass to optimiser. Missing kargs will be filled with default values. Choice of optimiser (`'opt'`) keyword can either be set by passing the string name (e.g. `'adam'` ), but only ADAM and SGD are available this way, or by passing an uninstantiated optimiser (e.g. torch.optim.Adam). If no optimser is set, then it defaults to ADAM. Additional keyword arguments can be set, and these will be passed tot he optimiser during instantiation Returns: Instantiated :class:`~lumin.nn.models.model_builder.ModelBuilder` Examples:: >>> new_model_builder = ModelBuilder.from_model_builder( >>> ModelBuidler) >>> >>> new_model_builder = ModelBuilder.from_model_builder( >>> ModelBuidler, loss=partial( >>> SignificanceLoss, sig_weight=sig_weight, >>> bkg_weight=bkg_weight, func=calc_ams_torch)) >>> >>> new_model_builder = ModelBuilder.from_model_builder( >>> 'weights/model_builder.pkl', >>> opt_args={'opt':'sgd', 'momentum':0.8, 'weight_decay':1e-5}) >>> >>> new_model_builder = ModelBuilder.from_model_builder( >>> 'weights/model_builder.pkl', >>> opt_args={'opt':torch.optim.Adam, ... 'momentum':0.8, ... 'weight_decay':1e-5}) ''' if isinstance(model_builder, str): with open(model_builder, 'rb') as fin: model_builder = pickle.load(fin) model_args = {'head': model_builder.head_kargs, 'body': model_builder.body_kargs, 'tail': model_builder.tail_kargs} if not hasattr(model_builder, 'cont_subsample_rate'): model_builder.cont_subsample_rate,model_builder.guaranteed_feats = None,None # <0.3.2 compat return cls(objective=model_builder.objective, cont_feats=model_builder.cont_feats, n_out=model_builder.n_out, cat_embedder=model_builder.cat_embedder, model_args=model_args, opt_args=opt_args if opt_args is not None else {}, cont_subsample_rate=model_builder.cont_subsample_rate, guaranteed_feats=model_builder.guaranteed_feats, loss=model_builder.loss if loss is None else loss, head=model_builder.head, body=model_builder.body, tail=model_builder.tail, pretrain_file=pretrain_file, freeze_head=freeze_head, freeze_body=freeze_body, freeze_tail=freeze_tail)
def _parse_loss(self, loss:Union[Any,'auto']='auto') -> None: if loss == 'auto': if 'class' in self.objective: if self.n_out > 1 and 'multiclass' in self.objective: self.loss = WeightedCCE else: self.loss = nn.BCELoss else: self.loss = WeightedMSE else: self.loss = loss def _parse_model_args(self, model_args:Optional[Dict[str,Any]]=None) -> None: if model_args is None: model_args = {} else: model_args = {k.lower(): model_args[k] for k in model_args} self.head_kargs = {} if model_args is None or 'head' not in model_args else model_args['head'] self.body_kargs = {} if model_args is None or 'body' not in model_args else model_args['body'] self.tail_kargs = {} if model_args is None or 'tail' not in model_args else model_args['tail'] def _parse_opt_args(self, opt_args:Optional[Dict[str,Any]]=None) -> None: if opt_args is None: opt_args = {} else: opt_args = {k.lower(): opt_args[k] for k in opt_args} self.opt_args = {k: opt_args[k] for k in opt_args if k != 'opt'} if 'opt' not in opt_args: if 'weight_decay' in opt_args and LooseVersion(torch.__version__) >= LooseVersion("1.2"): print('No optimiser specified but weight decay (L2) requested; defaulting to AdamW') self.opt = optim.AdamW else: print('No optimiser specified; defaulting to ADAM') self.opt = optim.Adam elif opt_args['opt'] == 'adamw': if LooseVersion(torch.__version__) >= LooseVersion("1.2"): self.opt = optim.AdamW else: raise Exception('AdamW requires PyTorch version >= 1.2.0') elif opt_args['opt'] == 'adam' and 'weight_decay' in opt_args: warnings.warn('According to arXiv:1711.05101, AdamW is recommended when using weight decay (L2) regularisation; continuing with Adam.') self.opt = optim.Adam else: self.opt = opt_args['opt'] if not isinstance(opt_args['opt'], str) else self._interp_opt(opt_args['opt']) def _subsample(self) -> None: if self.cont_subsample_rate is not None: self.use_conts = self.guaranteed_feats if self.guaranteed_feats is not None else [] n = math.ceil(len(self.cont_feats)*self.cont_subsample_rate)-len(self.use_conts) np.random.seed() # Is this necessary? self.use_conts += list(np.random.choice([f for f in self.cont_feats if f not in self.use_conts], size=n, replace=False)) self.n_cont_in = len(self.use_conts) cont_idxs = np.array([self.cont_feats.index(f) for f in self.use_conts]) cat_idxs = len(self.cont_feats)+np.arange(self.cat_embedder.n_cat_in) self.input_mask = np.hstack((cont_idxs, cat_idxs)) self.input_mask.sort() else: self.use_conts = self.cont_feats self.n_cont_in = len(self.cont_feats) self.input_mask = None @staticmethod def _interp_opt(opt:str) -> Callable[[Iterator, Optional[Any]], optim.Optimizer]: opt = opt.lower() if opt == 'adam': return optim.Adam elif opt == 'sgd': return optim.SGD elif opt == 'adamw': return optim.AdamW else: raise ValueError(f"Optimiser {opt} not interpretable from string, please pass as class") def _build_opt(self, model:nn.Module) -> optim.Optimizer: if isinstance(self.opt, str): self.opt = self._interp_opt(self.opt) # Backwards compatability with pre-v0.3.1 saves return self.opt(model.parameters(), **self.opt_args)
[docs] def set_lr(self, lr:float) -> None: r''' Set learning rate for all model parameters Arguments: lr: learning rate ''' self.opt_args['lr'] = lr
[docs] def get_head(self) -> AbsHead: r''' Construct head module Returns: Instantiated head nn.Module ''' if not hasattr(self, 'use_conts'): self.use_conts = self.cont_feats # Backwards compatability with pre-v0.3.2 saves self.input_mask = None return self.head(cont_feats=self.use_conts, cat_embedder=self.cat_embedder, lookup_init=self.lookup_init, freeze=self.freeze_head, **self.head_kargs)
[docs] def get_body(self, n_in:int, feat_map:List[str]) -> AbsBody: r''' Construct body module Returns: Instantiated body nn.Module ''' return self.body(n_in=n_in, feat_map=feat_map, lookup_init=self.lookup_init, lookup_act=self.lookup_act, freeze=self.freeze_body, **self.body_kargs)
[docs] def get_tail(self, n_in:int) -> nn.Module: r''' Construct tail module Returns: Instantiated tail nn.Module ''' return self.tail(n_in=n_in, n_out=self.n_out, objective=self.objective, lookup_init=self.lookup_init, freeze=self.freeze_tail, **self.tail_kargs)
[docs] def build_model(self) -> nn.Module: r''' Construct entire network module Returns: Instantiated nn.Module ''' head = self.get_head() body = self.get_body(head.get_out_size(), head.feat_map) tail = self.get_tail(body.get_out_size()) return nn.Sequential(head, body, tail)
[docs] def load_pretrained(self, model:nn.Module): r''' Load model weights from pretrained file Arguments: model: instantiated model, i.e. return of :meth:`~lumin.nn.models.model_builder.ModelBuilder.build_model` Returns: model with weights loaded ''' state = torch.load(self.pretrain_file, map_location='cpu') print('Loading pretrained model') return model.load_state_dict(state['model'])
[docs] def get_model(self) -> Tuple[nn.Module, optim.Optimizer, Any]: r''' Construct model, loss, and optimiser, optionally loading pretrained weights Returns: Instantiated network, optimiser linked to model parameters, uninstantiated loss, and optional input mask ''' model = self.build_model() if self.pretrain_file is not None: self.load_pretrained(model) model = to_device(model) opt = self._build_opt(model) return model, opt, self.loss, self.input_mask
[docs] def get_out_size(self) -> int: r''' Get number of outputs of model Returns: number of outputs of network ''' return self.tail.get_out_size()
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