Source code for torcheeg.models.transformer.simple_vit
from typing import Tuple
import numpy as np
import torch
from einops import rearrange
from einops.layers.torch import Rearrange
from torch import nn
def pair(t):
return t if isinstance(t, tuple) else (t, t)
def get_emb(sin_inp):
emb = torch.stack((sin_inp.sin(), sin_inp.cos()), dim=-1)
return torch.flatten(emb, -2, -1)
class PositionEmbedding3D(nn.Module):
def __init__(self, in_channels, temporature: float = 10000.0):
super(PositionEmbedding3D, self).__init__()
self.in_channels = in_channels
self.temporature = temporature
in_channels = int(np.ceil(in_channels / 6) * 2)
if in_channels % 2:
in_channels += 1
self.in_channels = in_channels
inv_freq = 1.0 / (temporature**(
torch.arange(0, in_channels, 2).float() / in_channels))
self.register_buffer("inv_freq", inv_freq)
self.cached_penc = None
def forward(self, x: torch.Tensor) -> torch.Tensor:
if len(x.shape) != 5:
raise RuntimeError(
"The input must be five-dimensional to perform thres-dimensional position embedding!"
)
if self.cached_penc is not None and self.cached_penc.shape == x.shape:
return self.cached_penc
self.cached_penc = None
batch_size, a, b, c, orig_ch = x.shape
pos_a = torch.arange(a, device=x.device).type(self.inv_freq.type())
pos_b = torch.arange(b, device=x.device).type(self.inv_freq.type())
pos_c = torch.arange(c, device=x.device).type(self.inv_freq.type())
sin_inp_a = torch.einsum("i,j->ij", pos_a, self.inv_freq)
sin_inp_b = torch.einsum("i,j->ij", pos_b, self.inv_freq)
sin_inp_c = torch.einsum("i,j->ij", pos_c, self.inv_freq)
emb_a = get_emb(sin_inp_a).unsqueeze(1).unsqueeze(1)
emb_b = get_emb(sin_inp_b).unsqueeze(1)
emb_c = get_emb(sin_inp_c)
emb = torch.zeros((a, b, c, self.in_channels * 3),
device=x.device).type(x.type())
emb[:, :, :, :self.in_channels] = emb_a
emb[:, :, :, self.in_channels:2 * self.in_channels] = emb_b
emb[:, :, :, 2 * self.in_channels:] = emb_c
self.cached_penc = emb[None, :, :, :, :orig_ch].repeat(
batch_size, 1, 1, 1, 1)
return self.cached_penc
class FeedForward(nn.Module):
def __init__(self, in_channels: int, hid_channels: int):
super().__init__()
self.net = nn.Sequential(
nn.LayerNorm(in_channels),
nn.Linear(in_channels, hid_channels),
nn.GELU(),
nn.Linear(hid_channels, in_channels),
)
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):
super().__init__()
inner_channels = head_channels * heads
self.heads = heads
self.scale = head_channels**-0.5
self.norm = nn.LayerNorm(hid_channels)
self.attend = nn.Softmax(dim=-1)
self.to_qkv = nn.Linear(hid_channels, inner_channels * 3, bias=False)
self.to_out = nn.Linear(inner_channels, hid_channels, bias=False)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.norm(x)
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)
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):
super().__init__()
self.layers = nn.ModuleList([])
for _ in range(depth):
self.layers.append(
nn.ModuleList([
Attention(hid_channels,
heads=heads,
head_channels=head_channels),
FeedForward(hid_channels, mlp_channels)
]))
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 SimpleViT(nn.Module):
r'''
A Simple and Effective Vision Transformer (SimpleViT). The authors of Vision Transformer (ViT) present a few minor modifications and dramatically improve the performance of plain ViT models. 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: Beyer L, Zhai X, Kolesnikov A. Better plain ViT baselines for ImageNet-1k[J]. arXiv preprint arXiv:2205.01580, 2022.
- URL: https://arxiv.org/abs/2205.01580
- Related Project: https://github.com/lucidrains/vit-pytorch
Below is a recommended suite for use in emotion recognition tasks:
.. code-block:: python
dataset = DEAPDataset(io_path=f'./deap',
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 = SimpleViT(chunk_size=128,
grid_size=(9, 9),
t_patch_size=32,
num_classes=2)
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({
"delta": [1, 4],
"theta": [4, 8],
"alpha": [8, 14],
"beta": [14, 31],
"gamma": [31, 49]
}),
transforms.ToGrid(DEAP_CHANNEL_LOCATION_DICT)
]),
online_transform=transforms.Compose([
transforms.ToTensor(),
]),
label_transform=transforms.Compose([
transforms.Select('valence'),
transforms.Binary(5.0),
]))
model = SimpleViT(chunk_size=5,
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)`)
patch_size (tuple): The size (resolution) of each input patch. (default: :obj:`(3, 3)`)
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:`2`)
'''
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 = 8,
mlp_channels: int = 64,
num_classes: int = 2):
super(SimpleViT, 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.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
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}.'
patch_channels = t_patch_size * patch_height * patch_width
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.position_embedding = PositionEmbedding3D(hid_channels)
self.transformer = Transformer(hid_channels, depth, heads,
head_channels, mlp_channels)
self.linear_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.
'''
b, *_ = x.shape
x = self.to_patch_embedding(x)
pe = self.position_embedding(x)
x = rearrange(x + pe, 'b ... d -> b (...) d')
x = self.transformer(x)
x = x.mean(dim=1)
return self.linear_head(x)
