Source code for torcheeg.trainers.domain_adaption.dan
import math
from itertools import chain, cycle
from typing import List, Tuple, Optional
import numpy as np
import torch
import torch.nn as nn
import torchmetrics
from torch.utils.data.dataloader import DataLoader
from ..classification_trainer import ClassificationTrainer
def guassian_kernel(X_source,
X_target,
kernel_mul=2.0,
num_kernels=5,
sigma=None):
n_samples = int(X_source.shape[0]) + int(X_target.shape[0])
total = torch.cat([X_source, X_target], dim=0)
total0 = total.unsqueeze(0).expand(int(total.size(0)), int(total.size(0)),
int(total.size(1)))
total1 = total.unsqueeze(1).expand(int(total.size(0)), int(total.size(0)),
int(total.size(1)))
L2_distance = ((total0 - total1)**2).sum(2)
if sigma:
bandwidth = sigma
else:
bandwidth = torch.sum(L2_distance.data) / (n_samples**2 - n_samples)
bandwidth /= kernel_mul**(num_kernels // 2)
bandwidth_list = [bandwidth * (kernel_mul**i) for i in range(num_kernels)]
kernel_val = [
torch.exp(-L2_distance / bandwidth_temp)
for bandwidth_temp in bandwidth_list
]
return sum(kernel_val)
def maximum_mean_discrepancy(X_source,
X_target,
kernel_mul=2.0,
num_kernels=5,
sigma=None):
batch_size = int(X_source.shape[0])
kernels = guassian_kernel(X_source,
X_target,
kernel_mul=kernel_mul,
num_kernels=num_kernels,
sigma=sigma)
XX = kernels[:batch_size, :batch_size]
YY = kernels[batch_size:, batch_size:]
XY = kernels[:batch_size, batch_size:]
YX = kernels[batch_size:, :batch_size]
loss = torch.mean(XX + YY - XY - YX)
return loss
[docs]class DANTrainer(ClassificationTrainer):
r'''
The individual differences and nonstationary of EEG signals make it difficult for deep learning models trained on the training set of subjects to correctly classify test samples from unseen subjects, since the training set and test set come from different data distributions. Domain adaptation is used to address the problem of distribution drift between training and test sets and thus achieves good performance in subject-independent (cross-subject) scenarios. This class supports the implementation of Deep Adaptation Network (DAN) for deep domain adaptation.
NOTE: DAN belongs to unsupervised domain adaptation methods, which only use labeled source and unlabeled target data. This means that the target dataset does not have to return labels.
- Paper: Long M, Cao Y, Wang J, et al. Learning transferable features with deep adaptation networks[C]//International conference on machine learning. PMLR, 2015: 97-105.
- URL: https://proceedings.mlr.press/v37/long15
- Related Project: https://github.com/jindongwang/transferlearning/blob/cfaf1174dff7390a861cc4abd5ede37dfa1063f5/code/deep/DAN/DAN.py
.. code-block:: python
trainer = DANTrainer(extractor, classifier)
trainer.fit(source_loader, target_loader, val_loader)
trainer.test(test_loader)
The class provides the following hook functions for inserting additional implementations in the training, validation and testing lifecycle:
- :obj:`before_training_epoch`: executed before each epoch of training starts
- :obj:`before_training_step`: executed before each batch of training starts
- :obj:`on_training_step`: the training process for each batch
- :obj:`after_training_step`: execute after the training of each batch
- :obj:`after_training_epoch`: executed after each epoch of training
- :obj:`before_validation_epoch`: executed before each round of validation starts
- :obj:`before_validation_step`: executed before the validation of each batch
- :obj:`on_validation_step`: validation process for each batch
- :obj:`after_validation_step`: executed after the validation of each batch
- :obj:`after_validation_epoch`: executed after each round of validation
- :obj:`before_test_epoch`: executed before each round of test starts
- :obj:`before_test_step`: executed before the test of each batch
- :obj:`on_test_step`: test process for each batch
- :obj:`after_test_step`: executed after the test of each batch
- :obj:`after_test_epoch`: executed after each round of test
If you want to customize some operations, you just need to inherit the class and override the hook function:
.. code-block:: python
class MyDANTrainer(DANTrainer):
def before_training_epoch(self, epoch_id: int, num_epochs: int):
# Do something here.
super().before_training_epoch(epoch_id, num_epochs)
If you want to use multiple GPUs for parallel computing, you need to specify the GPU indices you want to use in the python file:
.. code-block:: python
trainer = DANTrainer(model, device_ids=[1, 2, 7])
trainer.fit(train_loader, val_loader)
trainer.test(test_loader)
Then, you can use the :obj:`torch.distributed.launch` or :obj:`torchrun` to run your python file.
.. code-block:: shell
python -m torch.distributed.launch \
--nproc_per_node=3 \
--nnodes=1 \
--node_rank=0 \
--master_addr="localhost" \
--master_port=2345 \
your_python_file.py
Here, :obj:`nproc_per_node` is the number of GPUs you specify.
Args:
extractor (nn.Module): The feature extraction model, learning the feature representation of EEG signal by forcing the correlation matrixes of source and target data close.
classifier (nn.Module): The classification model, learning the classification task with source labeled data based on the feature of the feature extraction model. The dimension of its output should be equal to the number of categories in the dataset. The output layer does not need to have a softmax activation function.
lambd (float): The weight of DAN loss to trade-off between the classification loss and DAN loss. (defualt: :obj:`1.0`)
adaption_factor (bool): Whether to adjust the cross-domain-related loss term using the fitness factor, which was first proposed in DANN but works in many cases. (defualt: :obj:`True`)
num_classes (int, optional): The number of categories in the dataset. If :obj:`None`, the number of categories will be inferred from the attribute :obj:`num_classes` of the model. (defualt: :obj:`None`)
lr (float): The learning rate. (defualt: :obj:`0.0001`)
weight_decay (float): The weight decay (L2 penalty). (defualt: :obj:`0.0`)
device_ids (list): Use cpu if the list is empty. If the list contains indices of multiple GPUs, it needs to be launched with :obj:`torch.distributed.launch` or :obj:`torchrun`. (defualt: :obj:`[]`)
ddp_sync_bn (bool): Whether to replace batch normalization in network structure with cross-GPU synchronized batch normalization. Only valid when the length of :obj:`device_ids` is greater than one. (defualt: :obj:`True`)
ddp_replace_sampler (bool): Whether to replace sampler in dataloader with :obj:`DistributedSampler`. Only valid when the length of :obj:`device_ids` is greater than one. (defualt: :obj:`True`)
ddp_val (bool): Whether to use multi-GPU acceleration for the validation set. For experiments where data input order is sensitive, :obj:`ddp_val` should be set to :obj:`False`. Only valid when the length of :obj:`device_ids` is greater than one. (defualt: :obj:`True`)
ddp_test (bool): Whether to use multi-GPU acceleration for the test set. For experiments where data input order is sensitive, :obj:`ddp_test` should be set to :obj:`False`. Only valid when the length of :obj:`device_ids` is greater than one. (defualt: :obj:`True`)
.. automethod:: fit
.. automethod:: test
'''
def __init__(self,
extractor: nn.Module,
classifier: nn.Module,
lambd: float = 1.0,
adaption_factor: bool = False,
num_classes: Optional[int] = None,
lr: float = 1e-4,
weight_decay: float = 0.0,
device_ids: List[int] = [],
ddp_sync_bn: bool = True,
ddp_replace_sampler: bool = True,
ddp_val: bool = True,
ddp_test: bool = True):
# call BasicTrainer
# pylint: disable=bad-super-call
super(ClassificationTrainer,
self).__init__(modules={
'extractor': extractor,
'classifier': classifier
},
device_ids=device_ids,
ddp_sync_bn=ddp_sync_bn,
ddp_replace_sampler=ddp_replace_sampler,
ddp_val=ddp_val,
ddp_test=ddp_test)
self.lr = lr
self.weight_decay = weight_decay
self.lambd = lambd
self.adaption_factor = adaption_factor
if not num_classes is None:
self.num_classes = num_classes
elif hasattr(classifier, 'num_classes'):
self.num_classes = classifier.num_classes
else:
raise ValueError('The number of classes is not specified.')
self.optimizer = torch.optim.Adam(chain(extractor.parameters(),
classifier.parameters()),
lr=lr,
weight_decay=weight_decay)
self.loss_fn = nn.CrossEntropyLoss()
# init metric
self.train_loss = torchmetrics.MeanMetric().to(self.device)
self.train_accuracy = torchmetrics.Accuracy(
task='multiclass', num_classes=self.num_classes, top_k=1).to(self.device)
self.val_loss = torchmetrics.MeanMetric().to(self.device)
self.val_accuracy = torchmetrics.Accuracy(
task='multiclass', num_classes=self.num_classes, top_k=1).to(self.device)
self.test_loss = torchmetrics.MeanMetric().to(self.device)
self.test_accuracy = torchmetrics.Accuracy(
task='multiclass', num_classes=self.num_classes, top_k=1).to(self.device)
def on_training_step(self, source_loader: DataLoader,
target_loader: DataLoader, batch_id: int,
num_batches: int, epoch_id: int, num_epochs: int,
**kwargs):
self.train_accuracy.reset()
self.train_loss.reset()
self.optimizer.zero_grad()
X_source = source_loader[0].to(self.device)
y_source = source_loader[1].to(self.device)
X_target = target_loader[0].to(self.device)
X_source_feat = self.modules['extractor'](X_source)
y_source_pred = self.modules['classifier'](X_source_feat)
X_target_feat = self.modules['extractor'](X_target)
if self.adaption_factor:
p = float(batch_id +
epoch_id * num_batches) / num_epochs / num_batches
gamma = 2. / (1. + np.exp(-10 * p)) - 1
else:
gamma = 1.0
dan_loss = self.lambd * gamma * maximum_mean_discrepancy(
X_source_feat, X_target_feat)
task_loss = self.loss_fn(y_source_pred, y_source)
loss = task_loss + dan_loss
loss.backward()
self.optimizer.step()
# log five times
log_step = math.ceil(num_batches / 5)
if batch_id % log_step == 0:
self.train_loss.update(loss)
self.train_accuracy.update(y_source_pred.argmax(1), y_source)
train_loss = self.train_loss.compute()
train_accuracy = 100 * self.train_accuracy.compute()
# if not distributed, world_size is 1
batch_id = batch_id * self.world_size
num_batches = num_batches * self.world_size
if self.is_main:
self.log(
f"loss: {train_loss:>8f}, accuracy: {train_accuracy:>0.1f}% [{batch_id:>5d}/{num_batches:>5d}]"
)
[docs] def fit(self,
source_loader: DataLoader,
target_loader: DataLoader,
val_loader: DataLoader,
num_epochs: int = 1,
**kwargs):
r'''
Args:
source_loader (DataLoader): Iterable DataLoader for traversing the data batch from the source domain (torch.utils.data.dataloader.DataLoader, torch_geometric.loader.DataLoader, etc).
target_loader (DataLoader): Iterable DataLoader for traversing the training data batch from the target domain (torch.utils.data.dataloader.DataLoader, torch_geometric.loader.DataLoader, etc). The target dataset does not have to return labels.
val_loader (DataLoader): Iterable DataLoader for traversing the validation data batch (torch.utils.data.dataloader.DataLoader, torch_geometric.loader.DataLoader, etc).
num_epochs (int): training epochs. (defualt: :obj:`1`)
'''
source_loader = self.on_reveive_dataloader(source_loader, mode='train')
target_loader = self.on_reveive_dataloader(target_loader, mode='train')
val_loader = self.on_reveive_dataloader(val_loader, mode='val')
for t in range(num_epochs):
if hasattr(source_loader, 'need_to_set_epoch'):
source_loader.sampler.set_epoch(t)
if hasattr(target_loader, 'need_to_set_epoch'):
target_loader.sampler.set_epoch(t)
if hasattr(val_loader, 'need_to_set_epoch'):
val_loader.sampler.set_epoch(t)
num_batches = max(len(source_loader), len(target_loader))
# set model to train mode
for k, m in self.modules.items():
self.modules[k].train()
zip_loader = zip(source_loader, cycle(target_loader)) if len(
source_loader) > len(target_loader) else zip(
cycle(source_loader), target_loader)
# hook
self.before_training_epoch(t + 1, num_epochs, **kwargs)
for batch_id, (cur_source_loader,
cur_target_loader) in enumerate(zip_loader):
# hook
self.before_training_step(batch_id, num_batches, **kwargs)
# hook
self.on_training_step(cur_source_loader, cur_target_loader,
batch_id, num_batches, t, num_epochs,
**kwargs)
# hook
self.after_training_step(batch_id, num_batches, **kwargs)
# hook
self.after_training_epoch(t + 1, num_epochs, **kwargs)
# set model to val mode
for k, m in self.modules.items():
self.modules[k].eval()
num_batches = len(val_loader)
# hook
self.before_validation_epoch(t + 1, num_epochs, **kwargs)
for batch_id, val_batch in enumerate(val_loader):
# hook
self.before_validation_step(batch_id, num_batches, **kwargs)
# hook
self.on_validation_step(val_batch, batch_id, num_batches,
**kwargs)
# hook
self.after_validation_step(batch_id, num_batches, **kwargs)
# hook
self.after_validation_epoch(t + 1, num_epochs, **kwargs)
return self
[docs] def test(self, test_loader: DataLoader, **kwargs):
r'''
Args:
test_loader (DataLoader): Iterable DataLoader for traversing the test data batch (torch.utils.data.dataloader.DataLoader, torch_geometric.loader.DataLoader, etc).
'''
super().test(test_loader=test_loader, **kwargs)
def on_validation_step(self, val_batch: Tuple, batch_id: int,
num_batches: int, **kwargs):
X = val_batch[0].to(self.device)
y = val_batch[1].to(self.device)
feat = self.modules['extractor'](X)
pred = self.modules['classifier'](feat)
self.val_loss.update(self.loss_fn(pred, y))
self.val_accuracy.update(pred.argmax(1), y)
def on_test_step(self, test_batch: Tuple, batch_id: int, num_batches: int,
**kwargs):
X = test_batch[0].to(self.device)
y = test_batch[1].to(self.device)
feat = self.modules['extractor'](X)
pred = self.modules['classifier'](feat)
self.test_loss.update(self.loss_fn(pred, y))
self.test_accuracy.update(pred.argmax(1), y)
