mirror of
https://github.com/lucidrains/vit-pytorch.git
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4218556acd
* Create mp3.py * Implementation: Position Prediction as an Effective Pretraining Strategy * Added description for Masked Position Prediction * MP3 image added
138 lines
4.6 KiB
Python
138 lines
4.6 KiB
Python
import torch
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from torch import nn, einsum
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import torch.nn.functional as F
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from einops import rearrange, repeat
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from einops.layers.torch import Rearrange
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# helpers
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def pair(t):
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return t if isinstance(t, tuple) else (t, t)
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# pre-layernorm
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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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# cross attention
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class CrossAttention(nn.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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self.heads = heads
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self.scale = dim_head ** -0.5
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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_q = nn.Linear(dim, inner_dim, bias = False)
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self.to_kv = nn.Linear(dim, inner_dim * 2, 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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)
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def forward(self, x, context):
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b, n, _, h = *x.shape, self.heads
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qkv = (self.to_q(x), *self.to_kv(context).chunk(2, 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 = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
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attn = self.attend(dots)
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attn = self.dropout(attn)
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out = einsum('b h i j, b h j d -> b h i d', 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(nn.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.layers = nn.ModuleList([])
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for _ in range(depth):
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self.layers.append(nn.ModuleList([
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PreNorm(dim, CrossAttention(dim, heads = heads, dim_head = dim_head, dropout = dropout)),
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PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout))
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]))
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def forward(self, x, context):
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for attn, ff in self.layers:
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x = attn(x, context=context) + x
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x = ff(x) + x
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return x
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# Masked Position Prediction Pre-Training
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class MP3(nn.Module):
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def __init__(self, *, image_size, patch_size, masking_ratio, dim, depth, heads, mlp_dim, channels = 3, dim_head = 64, dropout = 0.):
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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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assert masking_ratio > 0 and masking_ratio < 1, 'masking ratio must be kept between 0 and 1'
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self.masking_ratio = masking_ratio
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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.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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self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)
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self.mlp_head = nn.Sequential(
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nn.LayerNorm(dim),
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nn.Linear(dim, num_patches)
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)
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self.out = nn.Softmax(dim = -1)
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def forward(self, img):
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device = img.device
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tokens = self.to_patch_embedding(img)
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batch, num_patches, *_ = tokens.shape
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# Masking
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num_masked = int(self.masking_ratio * num_patches)
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rand_indices = torch.rand(batch, num_patches, device = device).argsort(dim = -1)
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masked_indices, unmasked_indices = rand_indices[:, :num_masked], rand_indices[:, num_masked:]
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batch_range = torch.arange(batch, device = device)[:, None]
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tokens_unmasked = tokens[batch_range, unmasked_indices]
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x = rearrange(self.mlp_head(self.transformer(tokens, tokens_unmasked)), 'b n d -> (b n) d')
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x = self.out(x)
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# Define labels
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labels = repeat(torch.arange(num_patches, device = device), 'n -> b n', b = batch).flatten()
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loss = F.cross_entropy(x, labels)
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return loss
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