models/cnn_models/transformer.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, repeat

MIN_NUM_PATCHES = 16

"""
This is a new remote sensing super-resolution method based on the prevalent transformer

ref:
https://github.com/lucidrains/vit-pytorch/blob/main/vit_pytorch/vit_pytorch.py
"""

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 Residual2(nn.Module):
    def __init__(self, fn):
        super().__init__()
        self.fn = fn

    def forward(self, x, m=None, **kwargs):
        return self.fn(x, m, **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 PreNorm2(nn.Module):
    def __init__(self, dim, fn):
        super().__init__()
        self.norm = nn.LayerNorm(dim)
        self.fn = fn

    def forward(self, x, m=None, **kwargs):
        x = self.norm(x)
        if m is not None: m = self.norm(m)
        return self.fn(x, m, **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 = 8, dim_head = 64, dropout = 0.):
        super().__init__()
        inner_dim = dim_head *  heads
        self.heads = heads
        self.scale = dim ** -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):
        b, n, _, h = *x.shape, self.heads
        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 = h), qkv)

        dots = torch.einsum('bhid,bhjd->bhij', q, k) * self.scale
        mask_value = -torch.finfo(dots.dtype).max

        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

        attn = dots.softmax(dim=-1)

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


class MixedAttention(nn.Module):
    def __init__(self, dim, heads=8, dim_head=64, dropout=0.):
        super().__init__()
        inner_dim = dim_head * heads
        self.heads = heads
        self.scale = dim ** -0.5

        self.to_q = nn.Linear(dim, inner_dim, bias=False)
        self.to_k = nn.Linear(dim, inner_dim, bias=False)
        self.to_v = nn.Linear(dim, inner_dim, bias=False)
        self.to_out = nn.Sequential(
            nn.Linear(inner_dim, dim),
            nn.Dropout(dropout)
        )

    def forward(self, x, m, mask=None):

        b, n, _, h = *x.shape, self.heads
        q = self.to_q(x)
        k = self.to_k(m)
        v = self.to_v(m)
        q = rearrange(q, 'b n (h d) -> b h n d', h=h)
        k = rearrange(k, 'b n (h d) -> b h n d', h=h)
        v = rearrange(v, 'b n (h d) -> b h n d', h=h)

        dots = torch.einsum('bhid,bhjd->bhij', q, k) * self.scale
        mask_value = -torch.finfo(dots.dtype).max

        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

        attn = dots.softmax(dim=-1)

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


class TransformerEncoder(nn.Module):
    def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout):
        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_dim, dropout=dropout)))
            ]))

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


class TransformerDecoder(nn.Module):
    def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout):
        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))),
                Residual2(PreNorm2(dim, MixedAttention(dim, heads=heads, dim_head=dim_head, dropout=dropout))),
                Residual(PreNorm(dim, FeedForward(dim, mlp_dim, dropout=dropout)))
            ]))

    def with_pos_embed(self, tensor, pos=None):
        return tensor if pos is None else tensor + pos

    def forward(self, x, m, mask=None):
        for attn1, attn2, ff in self.layers:
            x = attn1(x, mask=mask)
            x = attn2(x, m, mask=mask)
            x = ff(x)
        return x