Source code for torcheeg.models.transformer.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 ViT(nn.Module):
r'''
The Vision Transformer. 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: Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16x16 words: Transformers for image recognition at scale[J]. arXiv preprint arXiv:2010.11929, 2020.
- URL: https://arxiv.org/abs/2010.11929
- Related Project: https://github.com/lucidrains/vit-pytorch
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 ViT
from torch.utils.data import DataLoader
from torcheeg.datasets.constants import DEAP_CHANNEL_LOCATION_DICT
dataset = DEAPDataset(root_path='./data_preprocessed_python',
offline_transform=transforms.Compose([
transforms.MinMaxNormalize(axis=-1),
transforms.ToGrid(DEAP_CHANNEL_LOCATION_DICT)
]),
online_transform=transforms.Compose([
transforms.ToTensor(),
]),
label_transform=transforms.Compose([
transforms.Select('valence'),
transforms.Binary(5.0),
]))
model = ViT(chunk_size=128,
grid_size=(9, 9),
t_patch_size=32,
num_classes=2)
x, y = next(iter(DataLoader(dataset, batch_size=64)))
model(x)
It can also be used for the analysis of features such as DE, PSD, etc:
.. code-block:: python
dataset = DEAPDataset(io_path=f'./deap',
root_path='./data_preprocessed_python',
offline_transform=transforms.Compose([
transforms.BandDifferentialEntropy(sampling_rate=128),
transforms.ToGrid(DEAP_CHANNEL_LOCATION_DICT)
]),
online_transform=transforms.Compose([
transforms.ToTensor(),
]),
label_transform=transforms.Compose([
transforms.Select('valence'),
transforms.Binary(5.0),
]))
model = ViT(chunk_size=4,
grid_size=(9, 9),
t_patch_size=1,
num_classes=2)
Args:
chunk_size (int): Number of data points included in each EEG chunk as training or test samples. (default: :obj:`128`)
grid_size (tuple): Spatial dimensions of grid-like EEG representation. (default: :obj:`(9, 9)`)
t_patch_size (int): The size of each input patch at the temporal (chunk size) dimension. (default: :obj:`32`)
s_patch_size (tuple): The size (resolution) of each input patch at the spatial (grid size) dimension. (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:`0.0`)
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,
chunk_size: int = 128,
grid_size: Tuple[int, int] = (9, 9),
t_patch_size: int = 32,
s_patch_size: Tuple[int, int] = (3, 3),
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(ViT, self).__init__()
self.chunk_size = chunk_size
self.grid_size = grid_size
self.t_patch_size = t_patch_size
self.s_patch_size = s_patch_size
self.dropout = dropout
self.hid_channels = hid_channels
self.depth = depth
self.heads = heads
self.head_channels = head_channels
self.mlp_channels = mlp_channels
self.pool_func = pool_func
self.embed_dropout = embed_dropout
self.num_classes = num_classes
grid_height, grid_width = pair(grid_size)
patch_height, patch_width = pair(s_patch_size)
assert grid_height % patch_height == 0 and grid_width % patch_width == 0, f'EEG grid size {grid_size} must be divisible by the spatial patch size {s_patch_size}.'
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) * (
grid_height // patch_height) * (grid_width // patch_width)
patch_channels = t_patch_size * patch_height * patch_width
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 p0) (h p1) (w p2) -> b c h w (p1 p2 p0)',
p0=t_patch_size,
p1=patch_height,
p2=patch_width),
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, 128, 9, 9]`. Here, :obj:`n` corresponds to the batch size, :obj:`128` corresponds to :obj:`chunk_size`, and :obj:`(9, 9)` corresponds to :obj:`grid_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)
