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import torch |
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import torch.nn.functional as F |
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from einops import rearrange |
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from torch import nn |
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MIN_NUM_PATCHES = 16 |
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class Residual(nn.Module): |
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def __init__(self, fn): |
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super().__init__() |
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self.fn = fn |
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def forward(self, x, **kwargs): |
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return self.fn(x, **kwargs) + x |
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class PreNorm(nn.Module): |
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def __init__(self, dim, fn): |
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super().__init__() |
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self.norm = nn.LayerNorm(dim) |
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self.fn = fn |
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def forward(self, x, **kwargs): |
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return self.fn(self.norm(x), **kwargs) |
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class FeedForward(nn.Module): |
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def __init__(self, dim, hidden_dim, dropout = 0.): |
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super().__init__() |
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self.net = nn.Sequential( |
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nn.Linear(dim, hidden_dim), |
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nn.GELU(), |
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nn.Dropout(dropout), |
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nn.Linear(hidden_dim, dim), |
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nn.Dropout(dropout) |
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) |
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def forward(self, x): |
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return self.net(x) |
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class Attention(nn.Module): |
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def __init__(self, dim, heads = 8, dropout = 0.): |
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super().__init__() |
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self.heads = heads |
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self.scale = dim ** -0.5 |
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self.to_qkv = nn.Linear(dim, dim * 3, bias = False) |
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self.to_out = nn.Sequential( |
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nn.Linear(dim, dim), |
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nn.Dropout(dropout) |
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) |
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def forward(self, x, mask = None): |
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b, n, _, h = *x.shape, self.heads |
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qkv = self.to_qkv(x).chunk(3, dim = -1) |
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q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), qkv) |
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dots = torch.einsum('bhid,bhjd->bhij', q, k) * self.scale |
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if mask is not None: |
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mask = F.pad(mask.flatten(1), (1, 0), value = True) |
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assert mask.shape[-1] == dots.shape[-1], 'mask has incorrect dimensions' |
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mask = mask[:, None, :] * mask[:, :, None] |
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dots.masked_fill_(~mask, float('-inf')) |
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del mask |
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attn = dots.softmax(dim=-1) |
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out = torch.einsum('bhij,bhjd->bhid', attn, v) |
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out = rearrange(out, 'b h n d -> b n (h d)') |
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out = self.to_out(out) |
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return out |
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class Transformer(nn.Module): |
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def __init__(self, dim, depth, heads, mlp_dim, dropout): |
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super().__init__() |
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self.layers = nn.ModuleList([]) |
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for _ in range(depth): |
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self.layers.append(nn.ModuleList([ |
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Residual(PreNorm(dim, Attention(dim, heads = heads, dropout = dropout))), |
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Residual(PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout))) |
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])) |
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def forward(self, x, mask = None): |
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for attn, ff in self.layers: |
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x = attn(x, mask = mask) |
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x = ff(x) |
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return x |
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class ViT(nn.Module): |
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def __init__(self, *, image_size, patch_size, dim, depth, heads, mlp_dim, channels = 3, dropout = 0., emb_dropout = 0.): |
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super().__init__() |
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assert image_size % patch_size == 0, 'image dimensions must be divisible by the patch size' |
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num_patches = (image_size // patch_size) ** 2 |
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patch_dim = channels * patch_size ** 2 |
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assert num_patches > MIN_NUM_PATCHES, f'your number of patches ({num_patches}) is way too small for attention to be effective. try decreasing your patch size' |
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self.patch_size = patch_size |
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self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim)) |
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self.patch_to_embedding = nn.Linear(patch_dim, dim) |
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self.cls_token = nn.Parameter(torch.randn(1, 1, dim)) |
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self.dropout = nn.Dropout(emb_dropout) |
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self.transformer = Transformer(dim, depth, heads, mlp_dim, dropout) |
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self.to_cls_token = nn.Identity() |
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self.mlp_head = nn.Sequential( |
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nn.LayerNorm(dim), |
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nn.Linear(dim, mlp_dim), |
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nn.GELU(), |
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nn.Dropout(dropout) |
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) |
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def forward(self, img, mask = None): |
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p = self.patch_size |
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x = rearrange(img, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = p, p2 = p) |
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x = self.patch_to_embedding(x) |
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b, n, _ = x.shape |
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cls_tokens = self.cls_token.expand(b, -1, -1) |
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x = torch.cat((cls_tokens, x), dim=1) |
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x += self.pos_embedding[:, :(n + 1)] |
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x = self.dropout(x) |
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x = self.transformer(x, mask) |
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x = self.to_cls_token(x[:, 0]) |
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return self.mlp_head(x) |
