stock-vit/m6_vit_1d.py

import torch
from torch import nn

from einops import rearrange, repeat, pack, unpack
from einops.layers.torch import Rearrange

# classes


class FeedForward(nn.Module):
    def __init__(self, dim, hidden_dim, dropout=0.0):
        super().__init__()
        self.net = nn.Sequential(
            nn.LayerNorm(dim),
            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=0.0):
        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.norm = nn.LayerNorm(dim)
        self.attend = nn.Softmax(dim=-1)
        self.dropout = nn.Dropout(dropout)

        self.to_qkv = nn.Linear(dim, inner_dim * 3, 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):
        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)
        attn = self.dropout(attn)

        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, dim, depth, heads, dim_head, mlp_dim, dropout=0.0):
        super().__init__()
        self.layers = nn.ModuleList([])
        for _ in range(depth):
            self.layers.append(
                nn.ModuleList(
                    [
                        Attention(dim, heads=heads, dim_head=dim_head, dropout=dropout),
                        FeedForward(dim, mlp_dim, dropout=dropout),
                    ]
                )
            )

    def forward(self, x):
        for attn, ff in self.layers:
            x = attn(x) + x
            x = ff(x) + x
        return x


class ViT1D(nn.Module):
    def __init__(
        self,
        *,
        seq_len,
        patch_size,
        num_classes,
        dim,
        depth,
        heads,
        mlp_dim,
        channels=3,
        dim_head=64,
        dropout=0.0,
        emb_dropout=0.0
    ):
        super().__init__()
        assert (seq_len % patch_size) == 0

        num_patches = seq_len // patch_size
        patch_dim = channels * patch_size

        self.to_patch_embedding = nn.Sequential(
            Rearrange("b c (n p) -> b n (p c)", p=patch_size),
            nn.LayerNorm(patch_dim),
            nn.Linear(patch_dim, dim),
            nn.LayerNorm(dim),
        )

        self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
        self.cls_token = nn.Parameter(torch.randn(dim))
        self.dropout = nn.Dropout(emb_dropout)

        self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)

        self.mlp_head = nn.Sequential(nn.LayerNorm(dim), nn.Linear(dim, num_classes))

    def forward(self, series):
        x = self.to_patch_embedding(series)
        b, n, _ = x.shape

        cls_tokens = repeat(self.cls_token, "d -> b d", b=b)

        x, ps = pack([cls_tokens, x], "b * d")

        x += self.pos_embedding[:, : (n + 1)]
        x = self.dropout(x)

        x = self.transformer(x)

        cls_tokens, _ = unpack(x, ps, "b * d")

        return self.mlp_head(cls_tokens)


if __name__ == "__main__":

    v = ViT1D(
        seq_len=256,
        patch_size=16,  # 这里也就是特征的数量。
        num_classes=1000,
        dim=1024,
        depth=6,
        heads=8,
        mlp_dim=2048,
        dropout=0.1,
        emb_dropout=0.1,
    )

    time_series = torch.randn(4, 3, 256)
    logits = v(time_series)  # (4, 1000)