module.py

import torch
import torch.nn as nn
import numpy as np
from einops import rearrange, repeat

class Residual(nn.Module):
    def __init__(self, fn):
        super().__init__()
        self.fn = fn
    def forward(self, x, **kwargs):
        return self.fn(x, **kwargs) + x

class PreNorm(nn.Module):
    def __init__(self, dim, fn):
        super().__init__()
        self.norm = nn.LayerNorm(dim)
        self.fn = fn
    def forward(self, x, **kwargs):
        return self.fn(self.norm(x), **kwargs)

class FeedForward(nn.Module):
    def __init__(self, dim, hidden_dim, dropout = 0.):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(dim, hidden_dim),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(hidden_dim, dim),
            nn.Dropout(dropout)
        )
    def forward(self, x):
        return self.net(x)

class Attention(nn.Module):
    def __init__(self, dim, heads, dim_head, dropout):
        super().__init__()
        inner_dim = dim_head * heads
        self.heads = heads
        self.scale = dim_head ** -0.5

        self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
        self.to_out = nn.Sequential(
            nn.Linear(inner_dim, dim),
            nn.Dropout(dropout)
        )
    def forward(self, x, mask = None):
        # x:[b,n,dim]
        b, n, _, h = *x.shape, self.heads

        # get qkv tuple:([b,n,head_num*head_dim],[...],[...])
        qkv = self.to_qkv(x).chunk(3, dim = -1)
        # split q,k,v from [b,n,head_num*head_dim] -> [b,head_num,n,head_dim]
        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), qkv)
        # transpose(k) * q / sqrt(head_dim) -> [b,head_num,n,n]
        dots = torch.einsum('bhid,bhjd->bhij', q, k) * self.scale
        mask_value = -torch.finfo(dots.dtype).max

        # mask value: -inf
        if mask is not None:
            mask = F.pad(mask.flatten(1), (1, 0), value = True)
            assert mask.shape[-1] == dots.shape[-1], 'mask has incorrect dimensions'
            mask = mask[:, None, :] * mask[:, :, None]
            dots.masked_fill_(~mask, mask_value)
            del mask

        # softmax normalization -> attention matrix
        attn = dots.softmax(dim=-1)
        # value * attention matrix -> output
        out = torch.einsum('bhij,bhjd->bhid', attn, v)
        # cat all output -> [b, n, head_num*head_dim]
        out = rearrange(out, 'b h n d -> b n (h d)')
        out = self.to_out(out)
        return out

class CrossAttention(nn.Module):
    def __init__(self, dim, heads, dim_head, dropout):
        super().__init__()
        inner_dim = dim_head *  heads
        project_out = not (heads == 1 and dim_head == dim)

        self.heads = heads
        self.scale = dim_head ** -0.5

        self.to_k = nn.Linear(dim, inner_dim , bias=False)
        self.to_v = nn.Linear(dim, inner_dim , bias = False)
        self.to_q = nn.Linear(dim, inner_dim, bias = False)

        self.to_out = nn.Sequential(
            nn.Linear(inner_dim, dim),
            nn.Dropout(dropout)
        ) if project_out else nn.Identity()

    def forward(self, x_qkv):
        b, n, _, h = *x_qkv.shape, self.heads

        k = self.to_k(x_qkv)
        k = rearrange(k, 'b n (h d) -> b h n d', h = h)

        v = self.to_v(x_qkv)
        v = rearrange(v, 'b n (h d) -> b h n d', h = h)

        q = self.to_q(x_qkv[:, 0].unsqueeze(1))
        q = rearrange(q, 'b n (h d) -> b h n d', h = h)

        dots = torch.einsum('b h i d, b h j d -> b h i j', q, k) * self.scale

        attn = dots.softmax(dim=-1)

        out = torch.einsum('b h i j, b h j d -> b h i d', attn, v)
        out = rearrange(out, 'b h n d -> b n (h d)')
        out =  self.to_out(out)
        return out

class Transformer(nn.Module):
    def __init__(self, dim, depth, heads, dim_head, mlp_head, dropout, num_channel):
        super().__init__()

        self.layers = nn.ModuleList([])
        for _ in range(depth):
            self.layers.append(nn.ModuleList([
                Residual(PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout))),
                Residual(PreNorm(dim, FeedForward(dim, mlp_head, dropout = dropout)))
            ]))

        self.skipcat = nn.ModuleList([])
        for _ in range(depth-2):
            self.skipcat.append(nn.Conv2d(num_channel+1, num_channel+1, [1, 2], 1, 0))

    def forward(self, x, mask = None):
        for attn, ff in self.layers:
            x = attn(x, mask = mask)
            x = ff(x)
        return x

class SSTransformer(nn.Module):
    def __init__(self, dim, depth, heads, dim_head, mlp_head, b_dim, b_depth, b_heads, b_dim_head, b_mlp_head, num_patches, dropout):
        super().__init__()

        self.layers = nn.ModuleList([])
        self.k_layers = nn.ModuleList([])
        self.channels_to_embedding = nn.Linear(num_patches, b_dim)
        self.cls_token = nn.Parameter(torch.randn(1, 1, b_dim))
        for _ in range(depth):
            self.layers.append(nn.ModuleList([
                Residual(PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout))),
                Residual(PreNorm(dim, FeedForward(dim, mlp_head, dropout = dropout)))
            ]))
        for _ in range(b_depth):
            self.k_layers.append(nn.ModuleList([
                Residual(PreNorm(b_dim, Attention(dim=b_dim, heads=b_heads, dim_head=b_dim_head, dropout = dropout))),
                Residual(PreNorm(b_dim, FeedForward(b_dim, b_mlp_head, dropout = dropout)))
            ]))

    def forward(self, x, mask = None):
        for attn, ff in self.layers:
            x = attn(x, mask = mask)
            x = ff(x)
        x = rearrange(x, 'b n d -> b d n')
        x = self.channels_to_embedding(x)
        b, d, n = x.shape
        cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b)
        x = torch.cat((cls_tokens, x), dim = 1)
        for attn, ff in self.k_layers:
            x = attn(x, mask = mask)
            x = ff(x)
        return x

class SSTransformer_pyramid(nn.Module):
    def __init__(self, dim, depth, heads, dim_head, mlp_head, b_dim, b_depth, b_heads, b_dim_head, b_mlp_head, num_patches, dropout):
        super().__init__()

        self.layers = nn.ModuleList([])
        self.k_layers = nn.ModuleList([])
        self.channels_to_embedding = nn.Linear(num_patches, b_dim)
        self.cls_token = nn.Parameter(torch.randn(1, 1, b_dim))
        for _ in range(depth):
            self.layers.append(nn.ModuleList([
                Residual(PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout))),
                Residual(PreNorm(dim, FeedForward(dim, mlp_head, dropout = dropout)))
            ]))
        for _ in range(b_depth):
            self.k_layers.append(nn.ModuleList([
                Residual(PreNorm(b_dim, Attention(dim=b_dim, heads=b_heads, dim_head=b_dim_head, dropout = dropout))),
                Residual(PreNorm(b_dim, FeedForward(b_dim, b_mlp_head, dropout = dropout)))
            ]))

    def forward(self, x, mask = None):
        for attn, ff in self.layers:
            x = attn(x, mask = mask)
            x = ff(x)
        out_feature = x
        x = rearrange(x, 'b n d -> b d n')
        x = self.channels_to_embedding(x)
        b, d, n = x.shape
        cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b)
        x = torch.cat((cls_tokens, x), dim = 1)
        for attn, ff in self.k_layers:
            x = attn(x, mask = mask)
            x = ff(x)
        return x, out_feature

class ViT(nn.Module):
    def __init__(self, image_size, near_band, num_patches, num_classes, dim, depth, heads, mlp_dim, pool='cls', channel_dim=1, dim_head = 16, dropout=0., emb_dropout=0., mode='ViT'):
        super().__init__()

        patch_dim = image_size ** 2 * near_band

        self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
        self.patch_to_embedding = nn.Linear(channel_dim, dim)
        self.cls_token = nn.Parameter(torch.randn(1, 1, dim))

        self.dropout = nn.Dropout(emb_dropout)
        self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout, num_patches, mode)

        self.pool = pool
        self.to_latent = nn.Identity()

        self.mlp_head = nn.Sequential(
            nn.LayerNorm(dim),
            nn.Linear(dim, num_classes)
        )
    def forward(self, x, mask = None):
        # patchs[batch, patch_num, patch_size*patch_size*c]  [batch,200,145*145]
        # x = rearrange(x, 'b c h w -> b c (h w)')
        ## embedding every patch vector to embedding size: [batch, patch_num, embedding_size]

        x = self.patch_to_embedding(x) #[b,n,dim]
        b, n, _ = x.shape

        # add position embedding
        cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b) #[b,1,dim]
        x = torch.cat((cls_tokens, x), dim = 1) #[b,n+1,dim]
        x += self.pos_embedding[:, :(n + 1)]
        x = self.dropout(x)
        # transformer: x[b,n + 1,dim] -> x[b,n + 1,dim]
        x = self.transformer(x, mask)
        # classification: using cls_token output
        x = self.to_latent(x[:,0])

        # MLP classification layer
        return self.mlp_head(x)

class SSFormer_v4(nn.Module):
    def __init__(self, dim, depth, heads, dim_head, mlp_head, b_dim, b_depth, b_heads, b_dim_head, b_mlp_head, num_patches, dropout, mode):
        super().__init__()

        self.layers = nn.ModuleList([])
        self.k_layers = nn.ModuleList([])
        self.channels_to_embedding = nn.Linear(num_patches, b_dim)
        self.cls_token = nn.Parameter(torch.randn(1, 1, b_dim))
        for _ in range(depth):
            self.layers.append(nn.ModuleList([
                Residual(PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout))),
                Residual(PreNorm(dim, FeedForward(dim, mlp_head, dropout = dropout)))
            ]))
        for _ in range(b_depth):
            self.k_layers.append(nn.ModuleList([
                Residual(PreNorm(b_dim, Attention(dim=b_dim, heads=b_heads, dim_head=b_dim_head, dropout = dropout))),
                Residual(PreNorm(b_dim, FeedForward(b_dim, b_mlp_head, dropout = dropout)))
            ]))
        self.mode = mode

    def forward(self, x, c, mask = None):
        for attn, ff in self.layers:
            x = attn(x, mask = mask)
            x = ff(x)
        x = rearrange(x, 'b n d -> b d n')
        x = self.channels_to_embedding(x)
        b, d, n = x.shape
        cls_tokens = repeat(c, '() n d -> b n d', b = b)
        x = torch.cat((cls_tokens, x), dim = 1)
        for attn, ff in self.k_layers:
            x = attn(x, mask = mask)
            x = ff(x)
        return x