mirror of
https://github.com/lucidrains/vit-pytorch.git
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255 lines
7.3 KiB
Python
255 lines
7.3 KiB
Python
from __future__ import annotations
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# DetPool ViT - a vit that accepts an object mask and attends and pools only using that mask - table 1
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# Dantong Niu et al. - https://openreview.net/forum?id=NZDaMcpXZm
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import torch
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import torch.nn.functional as F
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from torch import nn
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from torch.nn import Module, ModuleList
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from einops import rearrange, repeat, pack, unpack
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from einops.layers.torch import Rearrange, Reduce
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# helpers
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def exists(val):
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return val is not None
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def pair(t):
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return t if isinstance(t, tuple) else (t, t)
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def masked_mean(t, mask, dim = 1, eps = 1e-5):
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if not exists(mask):
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return t.mean(dim = dim)
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mask = rearrange(mask.bool(), '... -> ... 1')
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t = t.masked_fill(~mask, 0.)
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return t.sum(dim = dim) / mask.sum(dim = dim).clamp(min = eps)
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# classes
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class FeedForward(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.LayerNorm(dim),
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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(Module):
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def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):
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super().__init__()
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inner_dim = dim_head * heads
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project_out = not (heads == 1 and dim_head == dim)
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self.heads = heads
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self.scale = dim_head ** -0.5
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self.norm = nn.LayerNorm(dim)
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self.attend = nn.Softmax(dim = -1)
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self.dropout = nn.Dropout(dropout)
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self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
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self.to_out = nn.Sequential(
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nn.Linear(inner_dim, dim),
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nn.Dropout(dropout)
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) if project_out else nn.Identity()
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def forward(self, x, mask = None):
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x = self.norm(x)
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qkv = self.to_qkv(x).chunk(3, dim = -1)
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q, k, v = (rearrange(t, 'b n (h d) -> b h n d', h = self.heads) for t in qkv)
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dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale
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if exists(mask):
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mask = rearrange(mask, 'b j -> b 1 1 j')
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mask_value = -torch.finfo(dots.dtype).max
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dots = dots.masked_fill(~mask, mask_value)
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attn = self.attend(dots)
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attn = self.dropout(attn)
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out = torch.matmul(attn, v)
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out = rearrange(out, 'b h n d -> b n (h d)')
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return self.to_out(out)
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class Transformer(Module):
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def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
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super().__init__()
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self.norm = nn.LayerNorm(dim)
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self.layers = ModuleList([])
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for _ in range(depth):
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self.layers.append(ModuleList([
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Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout),
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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) + x
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x = ff(x) + x
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return self.norm(x)
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class ViTDetPool(Module):
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def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, use_cls_token = True, channels = 3, dim_head = 64, dropout = 0., emb_dropout = 0., mask_generator: Module | None = None):
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super().__init__()
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image_height, image_width = pair(image_size)
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patch_height, patch_width = pair(patch_size)
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assert image_height % patch_height == 0 and image_width % patch_width == 0, 'Image dimensions must be divisible by the patch size.'
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num_patches = (image_height // patch_height) * (image_width // patch_width)
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patch_dim = channels * patch_height * patch_width
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self.patch_height = patch_height
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self.patch_width = patch_width
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self.downsample_mask = Reduce('b (h p1) (w p2) -> b (h w)', 'max', p1 = patch_height, p2 = patch_width)
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self.to_patch_embedding = nn.Sequential(
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Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_height, p2 = patch_width),
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nn.LayerNorm(patch_dim),
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nn.Linear(patch_dim, dim),
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nn.LayerNorm(dim),
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)
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# maybe cls
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self.use_cls_token = use_cls_token
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if use_cls_token:
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self.cls_token = nn.Parameter(torch.randn(dim) * 1e-2)
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self.pos_embedding = nn.Parameter(torch.randn(num_patches, dim) * 1e-2)
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self.dropout = nn.Dropout(emb_dropout)
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self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)
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self.to_latent = nn.Identity()
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self.mlp_head = nn.Linear(dim, num_classes) if num_classes > 0 else None
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self.mask_generator = mask_generator
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def forward(self, img, object_mask = None):
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if not exists(object_mask) and exists(self.mask_generator):
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with torch.no_grad():
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self.mask_generator.eval()
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object_mask = self.mask_generator(img)
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has_cls = self.use_cls_token
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batch, _, height, width = img.shape
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tokens = self.to_patch_embedding(img)
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seq = tokens.shape[1]
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tokens = tokens + self.pos_embedding[:seq]
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if has_cls:
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cls_token = repeat(self.cls_token, 'd -> b d', b = batch)
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tokens, packed_shape = pack((cls_token, tokens), 'b * d')
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tokens = self.dropout(tokens)
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# handle the attention mask, and for final pooling
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mask = None
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if exists(object_mask):
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assert object_mask.ndim in {3, 2}
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if object_mask.shape == (batch, height, width):
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mask = self.downsample_mask(object_mask)
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else:
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mask = object_mask
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mask = rearrange(mask, 'b ... -> b (...)')
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assert mask.shape == (batch, seq)
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if has_cls:
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mask = F.pad(mask, (1, 0), value = True)
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# attend with maybe mask
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tokens = self.transformer(tokens, mask = mask)
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if not exists(self.mlp_head):
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return tokens
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# splice out cls
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if has_cls:
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_, tokens = unpack(tokens, packed_shape, 'b * d')
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if exists(mask):
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mask = mask[..., 1:]
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# pooling with the mask
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pooled = masked_mean(tokens, mask, dim = 1)
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pooled = self.to_latent(pooled)
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return self.mlp_head(pooled)
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# quick test
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if __name__ == '__main__':
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vit = ViTDetPool(
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image_size = 256,
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patch_size = 32,
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num_classes = 1000,
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dim = 1024,
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depth = 6,
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heads = 16,
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mlp_dim = 2048,
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dropout = 0.1,
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emb_dropout = 0.1
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)
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img = torch.randn(1, 3, 256, 256)
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object_mask = torch.randint(0, 2, (1, 256, 256)).bool()
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preds = vit(img, object_mask = object_mask)
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assert preds.shape == (1, 1000)
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preds_no_mask = vit(img)
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assert preds_no_mask.shape == (1, 1000)
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# test with module included
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class MockMasker(Module):
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def forward(self, img):
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batch, _, height, width = img.shape
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return torch.ones(batch, height, width).bool()
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vit = ViTDetPool(
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image_size = 256,
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patch_size = 32,
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num_classes = 1000,
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dim = 1024,
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depth = 1,
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heads = 16,
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mlp_dim = 2048,
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mask_generator = MockMasker()
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)
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preds = vit(img)
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assert preds.shape == (1, 1000)
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