/
meanteacher.py
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/
meanteacher.py
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import logging
from typing import List, Any, Optional, Union, Callable
import numpy as np
import torch
import torch.nn as nn
from tqdm.auto import trange
from .utils import exp_rampup, consistency_loss, BatchNormController
from ..backbone import BackBone
from ..basemodel import BaseTorchClassModel
from ..config import Config
from ..dataset import sample_batch, BaseDataset
from ..utils import cross_entropy_with_probs
logger = logging.getLogger(__name__)
class EMA:
"""
Implementation from https://fyubang.com/2019/06/01/ema/
"""
def __init__(self, model, decay):
self.model = model
self.decay = decay
self.shadow = {}
self.backup = {}
def load(self, ema_model):
for name, param in ema_model.named_parameters():
self.shadow[name] = param.data.clone()
def register(self):
for name, param in self.model.named_parameters():
if param.requires_grad:
self.shadow[name] = param.data.clone()
def update(self):
for name, param in self.model.named_parameters():
if param.requires_grad:
assert name in self.shadow
new_average = (1.0 - self.decay) * param.data + self.decay * self.shadow[name]
self.shadow[name] = new_average.clone()
def apply_shadow(self):
for name, param in self.model.named_parameters():
if param.requires_grad:
assert name in self.shadow
self.backup[name] = param.data
param.data = self.shadow[name]
def restore(self):
for name, param in self.model.named_parameters():
if param.requires_grad:
assert name in self.backup
param.data = self.backup[name]
self.backup = {}
class MeanTeacher(BaseTorchClassModel):
def __init__(self,
ema_m: Optional[float] = 0.999,
lamb: Optional[float] = 0.4,
rampup_epochs: Optional[int] = 50,
batch_size: Optional[int] = 32,
real_batch_size: Optional[int] = 32,
test_batch_size: Optional[int] = 16,
n_steps: Optional[int] = 10000,
grad_norm: Optional[float] = -1,
use_lr_scheduler: Optional[bool] = False,
**kwargs: Any
):
super().__init__()
self.hyperparas = {
'ema_m' : ema_m, # momentum of exponential moving average
'lamb' : lamb, # weight of unsupervised loss to supervised loss
'rampup_epochs' : rampup_epochs, # rampup epochs for weight of unsupervised loss
'batch_size' : batch_size,
'real_batch_size' : real_batch_size,
'test_batch_size' : test_batch_size,
'n_steps' : n_steps,
'grad_norm' : grad_norm,
'use_lr_scheduler': use_lr_scheduler,
'binary_mode' : False,
}
self.model: Optional[BackBone] = None
self.ema: Optional[EMA] = None
self.config = Config(
self.hyperparas,
use_optimizer=True,
use_lr_scheduler=use_lr_scheduler,
use_backbone=True,
use_label_model=False,
**kwargs
)
self.is_bert = self.config.backbone_config['name'] == 'BERT'
if self.is_bert:
self.tokenizer = AutoTokenizer.from_pretrained(self.config.backbone_config['paras']['model_name'])
def fit(self,
dataset_train: BaseDataset,
labeled_data_idx: List,
y_train: Optional[np.ndarray] = None,
dataset_valid: Optional[BaseDataset] = None,
y_valid: Optional[np.ndarray] = None,
include_labeled_as_unlabeled: Optional[bool] = False,
evaluation_step: Optional[int] = 100,
metric: Optional[Union[str, Callable]] = 'acc',
direction: Optional[str] = 'auto',
patience: Optional[int] = 20,
tolerance: Optional[float] = -1.0,
device: Optional[torch.device] = None,
verbose: Optional[bool] = True,
**kwargs: Any):
if not verbose:
logger.setLevel(logging.ERROR)
config = self.config.update(**kwargs)
hyperparas = self.config.hyperparas
logger.info(config)
n_steps = hyperparas['n_steps']
if hyperparas['real_batch_size'] == -1 or hyperparas['batch_size'] < hyperparas['real_batch_size'] or not self.is_bert:
hyperparas['real_batch_size'] = hyperparas['batch_size']
accum_steps = hyperparas['batch_size'] // hyperparas['real_batch_size']
n_steps_per_epoch = len(dataset_train) // hyperparas['real_batch_size']
lamb = hyperparas['lamb']
rampup_epochs = hyperparas['rampup_epochs']
if include_labeled_as_unlabeled:
labeled_dataset = dataset_train.create_subset(labeled_data_idx)
unlabeled_dataset = dataset_train
else:
labeled_dataset, unlabeled_dataset = dataset_train.create_split(labeled_data_idx)
if y_train is None:
y_train = dataset_train.labels
y_train = torch.Tensor([y_train[i] for i in labeled_data_idx]).to(device)
model = self._init_model(
dataset=dataset_train,
n_class=dataset_train.n_class,
config=config,
is_bert=self.is_bert
)
self.model = model.to(device)
self.ema = EMA(self.model, hyperparas['ema_m'])
self.ema.register()
bn_controller = BatchNormController()
unlabeled_train_dataloader = self._init_train_dataloader(
unlabeled_dataset,
n_steps=0,
config=config,
drop_last=True
)
unlabeled_train_dataloader = sample_batch(unlabeled_train_dataloader)
labeled_train_dataloader = self._init_train_dataloader(
labeled_dataset,
n_steps=n_steps,
config=config
)
optimizer, scheduler = self._init_optimizer_and_lr_scheduler(model, config)
valid_flag = self._init_valid_step(dataset_valid, y_valid, metric, direction, patience, tolerance)
history = {}
last_step_log = {}
try:
with trange(n_steps, desc="[TRAIN] MeanTeacher", unit="steps", disable=not verbose, ncols=200, position=0, leave=True) as pbar:
cnt = 0
step = 0
model.train()
optimizer.zero_grad()
for labeled_batch in labeled_train_dataloader:
outputs = model(labeled_batch)
batch_idx = labeled_batch['ids'].to(device)
target = y_train[batch_idx]
loss_sup = cross_entropy_with_probs(outputs, target)
unlabeled_batch = next(unlabeled_train_dataloader)
bn_controller.freeze_bn(self.model)
outputs1 = self.model(unlabeled_batch)
bn_controller.unfreeze_bn(self.model)
self.ema.apply_shadow()
with torch.no_grad():
bn_controller.freeze_bn(self.model)
outputs2 = self.model(unlabeled_batch)
bn_controller.unfreeze_bn(self.model)
self.ema.restore()
loss_unsup = consistency_loss(outputs1, outputs2) # MSE loss for unlabeled data
loss = loss_sup + lamb * exp_rampup(cnt // n_steps_per_epoch, rampup_epochs) * loss_unsup
loss.backward()
cnt += 1
if cnt % accum_steps == 0:
# Clip the norm of the gradients.
if hyperparas['grad_norm'] > 0:
nn.utils.clip_grad_norm_(model.parameters(), hyperparas['grad_norm'])
optimizer.step()
if scheduler is not None:
scheduler.step()
self.ema.update()
optimizer.zero_grad()
step += 1
if valid_flag and step % evaluation_step == 0:
metric_value, early_stop_flag, info = self._valid_step(step)
if early_stop_flag:
logger.info(info)
break
history[step] = {
'loss' : loss.item(),
'loss_sup' : loss_sup.item(),
'loss_unsup' : loss_unsup.item(),
f'val_{metric}' : metric_value,
f'best_val_{metric}': self.best_metric_value,
'best_step' : self.best_step,
}
last_step_log.update(history[step])
last_step_log['loss'] = loss.item()
last_step_log['loss_sup'] = loss_sup.item()
last_step_log['loss_unsup'] = loss_unsup.item()
pbar.update()
pbar.set_postfix(ordered_dict=last_step_log)
if step >= n_steps:
break
except KeyboardInterrupt:
logger.info(f'KeyboardInterrupt! do not terminate the process in case need to save the best model')
self._finalize()
return history
def predict_proba(self, *args: Any, **kwargs: Any):
self.ema.apply_shadow()
probas = super().predict_proba(*args, **kwargs)
self.ema.restore()
return probas