import math import torch import torch.nn as nn # FFN def FeedForward(dim, mult=4): inner_dim = int(dim * mult) return nn.Sequential( nn.LayerNorm(dim), nn.Linear(dim, inner_dim, bias=False), nn.GELU(), nn.Linear(inner_dim, dim, bias=False), ) def reshape_tensor(x, heads): bs, length, width = x.shape # (bs, length, width) --> (bs, length, n_heads, dim_per_head) x = x.view(bs, length, heads, -1) # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head) x = x.transpose(1, 2) # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head) x = x.reshape(bs, heads, length, -1) return x class PerceiverAttention(nn.Module): def __init__(self, *, dim, dim_head=64, heads=8): super().__init__() self.scale = dim_head ** -0.5 self.dim_head = dim_head self.heads = heads inner_dim = dim_head * heads self.norm1 = nn.LayerNorm(dim) self.norm2 = nn.LayerNorm(dim) self.to_q = nn.Linear(dim, inner_dim, bias=False) self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False) self.to_out = nn.Linear(inner_dim, dim, bias=False) def forward(self, x, latents): """ Args: x (torch.Tensor): image features shape (b, n1, D) latent (torch.Tensor): latent features shape (b, n2, D) """ x = self.norm1(x) latents = self.norm2(latents) b, l, _ = latents.shape q = self.to_q(latents) kv_input = torch.cat((x, latents), dim=-2) k, v = self.to_kv(kv_input).chunk(2, dim=-1) q = reshape_tensor(q, self.heads) k = reshape_tensor(k, self.heads) v = reshape_tensor(v, self.heads) # attention scale = 1 / math.sqrt(math.sqrt(self.dim_head)) weight = (q * scale) @ (k * scale).transpose( -2, -1 ) # More stable with f16 than dividing afterwards weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) out = weight @ v out = out.permute(0, 2, 1, 3).reshape(b, l, -1) return self.to_out(out) class ImageProjModel(torch.nn.Module): """Projection Model""" def __init__( self, cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4, ): super().__init__() self.cross_attention_dim = cross_attention_dim self.clip_extra_context_tokens = clip_extra_context_tokens # from 1024 -> 4 * 1024 self.proj = torch.nn.Linear( clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim ) self.norm = torch.nn.LayerNorm(cross_attention_dim) def forward(self, image_embeds): embeds = image_embeds clip_extra_context_tokens = self.proj(embeds).reshape( -1, self.clip_extra_context_tokens, self.cross_attention_dim ) clip_extra_context_tokens = self.norm(clip_extra_context_tokens) return clip_extra_context_tokens class Resampler(nn.Module): def __init__( self, dim=1024, depth=8, dim_head=64, heads=16, num_queries=8, embedding_dim=768, output_dim=1024, ff_mult=4, ): super().__init__() self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim ** 0.5) self.proj_in = nn.Linear(embedding_dim, dim) self.proj_out = nn.Linear(dim, output_dim) self.norm_out = nn.LayerNorm(output_dim) self.layers = nn.ModuleList([]) for _ in range(depth): self.layers.append( nn.ModuleList( [ PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads), FeedForward(dim=dim, mult=ff_mult), ] ) ) def forward(self, x): latents = self.latents.repeat(x.size(0), 1, 1) x = self.proj_in(x) for attn, ff in self.layers: latents = attn(x, latents) + latents latents = ff(latents) + latents latents = self.proj_out(latents) return self.norm_out(latents)