Source code for torcheeg.models.transformer.arjun_vit
from typing import Tuple
import torch
from einops import rearrange, repeat
from einops.layers.torch import Rearrange
from torch import nn
def pair(t):
return t if isinstance(t, tuple) else (t, t)
class PreNorm(nn.Module):
def __init__(self, in_channels: int, fn: nn.Module):
super(PreNorm, self).__init__()
self.norm = nn.LayerNorm(in_channels)
self.fn = fn
def forward(self, x, **kwargs):
return self.fn(self.norm(x), **kwargs)
class FeedForward(nn.Module):
def __init__(self,
in_channels: int,
hid_channels: int,
dropout: float = 0.):
super(FeedForward, self).__init__()
self.net = nn.Sequential(nn.Linear(in_channels, hid_channels),
nn.GELU(), nn.Dropout(dropout),
nn.Linear(hid_channels, in_channels),
nn.Dropout(dropout))
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.net(x)
class Attention(nn.Module):
def __init__(self,
hid_channels: int,
heads: int = 8,
head_channels: int = 64,
dropout: float = 0.):
super(Attention, self).__init__()
inner_channels = head_channels * heads
project_out = not (heads == 1 and head_channels == hid_channels)
self.heads = heads
self.scale = head_channels**-0.5
self.attend = nn.Softmax(dim=-1)
self.dropout = nn.Dropout(dropout)
self.to_qkv = nn.Linear(hid_channels, inner_channels * 3, bias=False)
self.to_out = nn.Sequential(
nn.Linear(inner_channels, hid_channels),
nn.Dropout(dropout)) if project_out else nn.Identity()
def forward(self, x: torch.Tensor) -> torch.Tensor:
qkv = self.to_qkv(x).chunk(3, dim=-1)
q, k, v = map(
lambda t: rearrange(t, 'b n (h d) -> b h n d', h=self.heads), qkv)
dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale
attn = self.attend(dots)
attn = self.dropout(attn)
out = torch.matmul(attn, v)
out = rearrange(out, 'b h n d -> b n (h d)')
return self.to_out(out)
class Transformer(nn.Module):
def __init__(self,
hid_channels: int,
depth: int,
heads: int,
head_channels: int,
mlp_channels: int,
dropout: float = 0.):
super(Transformer, self).__init__()
self.layers = nn.ModuleList([])
for _ in range(depth):
self.layers.append(
nn.ModuleList([
PreNorm(
hid_channels,
Attention(hid_channels,
heads=heads,
head_channels=head_channels,
dropout=dropout)),
PreNorm(
hid_channels,
FeedForward(hid_channels, mlp_channels,
dropout=dropout))
]))
def forward(self, x: torch.Tensor) -> torch.Tensor:
for attn, ff in self.layers:
x = attn(x) + x
x = ff(x) + x
return x
[docs]class ArjunViT(nn.Module):
r'''
Arjun et al. employ a variation of the Transformer, the Vision Transformer to process EEG signals for emotion recognition. For more details, please refer to the following information.
It is worth noting that this model is not designed for EEG analysis, but shows good performance and can serve as a good research start.
- Paper: Arjun A, Rajpoot A S, Panicker M R. Introducing attention mechanism for eeg signals: Emotion recognition with vision transformers[C]//2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2021: 5723-5726.
- URL: https://ieeexplore.ieee.org/abstract/document/9629837
Below is a recommended suite for use in emotion recognition tasks:
.. code-block:: python
from torcheeg.datasets import DEAPDataset
from torcheeg import transforms
from torcheeg.models import ArjunViT
from torch.utils.data import DataLoader
dataset = DEAPDataset(root_path='./data_preprocessed_python',
offline_transform=transforms.Compose([
transforms.MeanStdNormalize(),
transforms.To2d()
]),
online_transform=transforms.Compose([
transforms.ToTensor(),
]),
label_transform=transforms.Compose([
transforms.Select('valence'),
transforms.Binary(5.0),
]))
model = ArjunViT(chunk_size=128,
t_patch_size=50,
num_electrodes=32,
num_classes=2)
x, y = next(iter(DataLoader(dataset, batch_size=64)))
model(x)
Args:
num_electrodes (int): The number of electrodes. (default: :obj:`32`)
chunk_size (int): Number of data points included in each EEG chunk. (default: :obj:`128`)
t_patch_size (int): The size of each input patch at the temporal (chunk size) dimension. (default: :obj:`32`)
patch_size (tuple): The size (resolution) of each input patch. (default: :obj:`(3, 3)`)
hid_channels (int): The feature dimension of embeded patch. (default: :obj:`32`)
depth (int): The number of attention layers for each transformer block. (default: :obj:`3`)
heads (int): The number of attention heads for each attention layer. (default: :obj:`4`)
head_channels (int): The dimension of each attention head for each attention layer. (default: :obj:`8`)
mlp_channels (int): The number of hidden nodes in the fully connected layer of each transformer block. (default: :obj:`64`)
num_classes (int): The number of classes to predict. (default: :obj:`2`)
embed_dropout (float): Probability of an element to be zeroed in the dropout layers of the embedding layers. (default: :obj:`0.0`)
dropout (float): Probability of an element to be zeroed in the dropout layers of the transformer layers. (default: :obj:`0.0`)
pool_func (str): The pool function before the classifier, optionally including :obj:`cls` and :obj:`mean`, where :obj:`cls` represents selecting classification-related token and :obj:`mean` represents the average pooling. (default: :obj:`cls`)
'''
def __init__(self,
num_electrodes: int = 32,
chunk_size: int = 128,
t_patch_size: int = 32,
hid_channels: int = 32,
depth: int = 3,
heads: int = 4,
head_channels: int = 64,
mlp_channels: int = 64,
num_classes: int = 2,
embed_dropout: float = 0.,
dropout: float = 0.,
pool_func: str = 'cls'):
super(ArjunViT, self).__init__()
self.num_electrodes = num_electrodes
self.chunk_size = chunk_size
self.t_patch_size = t_patch_size
self.hid_channels = hid_channels
self.depth = depth
self.heads = heads
self.head_channels = head_channels
self.mlp_channels = mlp_channels
self.num_classes = num_classes
self.embed_dropout = embed_dropout
self.dropout = dropout
self.pool_func = pool_func
assert chunk_size % t_patch_size == 0, f'EEG chunk size {chunk_size} must be divisible by the temporal patch size {t_patch_size}.'
num_patches = chunk_size // t_patch_size
patch_channels = num_electrodes * t_patch_size
assert pool_func in {
'cls', 'mean'
}, 'pool_func must be either cls (cls token) or mean (mean pooling)'
self.to_patch_embedding = nn.Sequential(
Rearrange('b c (w p) -> b w (c p)', p=t_patch_size),
nn.Linear(patch_channels, hid_channels),
)
self.pos_embedding = nn.Parameter(
torch.randn(1, num_patches + 1, hid_channels))
self.cls_token = nn.Parameter(torch.randn(1, 1, hid_channels))
self.dropout = nn.Dropout(embed_dropout)
self.transformer = Transformer(hid_channels, depth, heads,
head_channels, mlp_channels, dropout)
self.pool_func = pool_func
self.mlp_head = nn.Sequential(nn.LayerNorm(hid_channels),
nn.Linear(hid_channels, num_classes))
[docs] def forward(self, x: torch.Tensor) -> torch.Tensor:
r'''
Args:
x (torch.Tensor): EEG signal representation, the ideal input shape is :obj:`[n, 32, 128]`. Here, :obj:`n` corresponds to the batch size, :obj:`32` corresponds to :obj:`num_electrodes`, and :obj:`chunk_size` corresponds to :obj:`chunk_size`.
Returns:
torch.Tensor[number of sample, number of classes]: the predicted probability that the samples belong to the classes.
'''
x = self.to_patch_embedding(x)
x = rearrange(x, 'b ... d -> b (...) d')
b, n, _ = x.shape
cls_tokens = repeat(self.cls_token, '1 1 d -> b 1 d', b=b)
x = torch.cat((cls_tokens, x), dim=1)
x += self.pos_embedding[:, :(n + 1)]
x = self.dropout(x)
x = self.transformer(x)
x = x.mean(dim=1) if self.pool_func == 'mean' else x[:, 0]
return self.mlp_head(x)
