MindEye2.py

import torch
import torch.nn as nn

class MindEyeModule(nn.Module):
    def __init__(self):
        super(MindEyeModule, self).__init__()
    def forward(self, x):
        return x


class RidgeRegression(torch.nn.Module):
    # make sure to add weight_decay when initializing optimizer
    def __init__(self, input_sizes, out_features, seq_len=1): 
        super(RidgeRegression, self).__init__()
        self.seq_len = seq_len
        self.out_features = out_features
        self.linears = torch.nn.ModuleList([
                torch.nn.Linear(input_size, out_features) for input_size in input_sizes
            ])
    def forward(self, x, subj_idx=0):
        out = torch.cat([self.linears[subj_idx](x[:,seq]).unsqueeze(1) for seq in range(self.seq_len)], dim=1)
        return out

    
from functools import partial

class BrainNetwork(nn.Module):
    def __init__(self, h=4096, in_dim=15724, out_dim=768, seq_len=1, n_blocks=4, drop=.15, 
                 clip_size=768, clip_scale=1):
        super().__init__()
        self.seq_len = seq_len
        self.h = h
        self.clip_size = clip_size
        self.clip_scale = clip_scale
        
        self.mixer_blocks1 = nn.ModuleList([
            self.mixer_block1(h, drop) for _ in range(n_blocks)
        ])
        self.mixer_blocks2 = nn.ModuleList([
            self.mixer_block2(seq_len, drop) for _ in range(n_blocks)
        ])
        
        # Output linear layer
        self.backbone_linear = nn.Linear(h * seq_len, out_dim, bias=True) 
        if self.clip_scale>0:
            self.clip_proj = self.projector(clip_size, clip_size, h=clip_size)
            
    def projector(self, in_dim, out_dim, h=2048):
        return nn.Sequential(
            nn.LayerNorm(in_dim),
            nn.GELU(),
            nn.Linear(in_dim, h),
            nn.LayerNorm(h),
            nn.GELU(),
            nn.Linear(h, h),
            nn.LayerNorm(h),
            nn.GELU(),
            nn.Linear(h, out_dim)
        )
    
    def mlp(self, in_dim, out_dim, drop):
        return nn.Sequential(
            nn.Linear(in_dim, out_dim),
            nn.GELU(),
            nn.Dropout(drop),
            nn.Linear(out_dim, out_dim),
        )
    
    def mixer_block1(self, h, drop):
        return nn.Sequential(
            nn.LayerNorm(h),
            self.mlp(h, h, drop),  # Token mixing
        )

    def mixer_block2(self, seq_len, drop):
        return nn.Sequential(
            nn.LayerNorm(seq_len),
            self.mlp(seq_len, seq_len, drop)  # Channel mixing
        )
        
    def forward(self, x):
        # make empty tensors
        c,b = torch.Tensor([0.]), torch.Tensor([[0.],[0.]])
        
        # Mixer blocks
        residual1 = x
        residual2 = x.permute(0,2,1)
        for block1, block2 in zip(self.mixer_blocks1,self.mixer_blocks2):
            x = block1(x) + residual1
            residual1 = x
            x = x.permute(0,2,1)
            
            x = block2(x) + residual2
            residual2 = x
            x = x.permute(0,2,1)
            
        x = x.reshape(x.size(0), -1)
        backbone = self.backbone_linear(x).reshape(len(x), -1, self.clip_size)
        if self.clip_scale>0:
            c = self.clip_proj(backbone)
        
        return backbone, c, b