pytorch实现transformer模块_pytorch swin transformer

import torch
import torch.nn.functional as F
import pdb
from torch import nn, einsum
import torch.nn.functional as F
from einops import rearrange, repeat
from einops.layers.torch import Rearrange
import numpy as np
from functools import partial
from tqdm import tqdm

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=8, dim_head=64, dropout=0.1):
        super().__init__()
        inner_dim = dim_head*heads
        self.inner_dim = inner_dim
        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):
        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 = einsum('b h i d, b h j d -> b h i j', 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 = rearrange(mask, 'b i -> b () i ()') * \
                rearrange(mask, 'b j -> b () () j')
            dots.masked_fill_(~mask, mask_value)
            del mask

        attn = dots.softmax(dim=-1)

        out = 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_dim, dropout=0.1):
        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
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