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| # modified from https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py | |
| # and https://github.com/lucidrains/imagen-pytorch/blob/main/imagen_pytorch/imagen_pytorch.py | |
| import math | |
| import torch | |
| import torch.nn as nn | |
| from einops import rearrange | |
| from einops.layers.torch import Rearrange | |
| # 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 ResamplerFaceID(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, | |
| max_seq_len: int = 257, # CLIP tokens + CLS token | |
| apply_pos_emb: bool = False, | |
| num_latents_mean_pooled: int = 0, # number of latents derived from mean pooled representation of the sequence | |
| ): | |
| super().__init__() | |
| self.pos_emb = nn.Embedding(max_seq_len, embedding_dim) if apply_pos_emb else None | |
| 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.to_latents_from_mean_pooled_seq = ( | |
| nn.Sequential( | |
| nn.LayerNorm(dim), | |
| nn.Linear(dim, dim * num_latents_mean_pooled), | |
| Rearrange("b (n d) -> b n d", n=num_latents_mean_pooled), | |
| ) | |
| if num_latents_mean_pooled > 0 | |
| else None | |
| ) | |
| 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): | |
| if self.pos_emb is not None: | |
| n, device = x.shape[1], x.device | |
| pos_emb = self.pos_emb(torch.arange(n, device=device)) | |
| x = x + pos_emb | |
| #(x:2,1,512) | |
| #(latents:2,16,768) | |
| # x= x.unsqueeze(1) | |
| # x= x.repeat(x.size(0), latents.size(1), 1) | |
| latents = self.latents.repeat(x.size(0), 1, 1) | |
| x = self.proj_in(x) | |
| if self.to_latents_from_mean_pooled_seq: | |
| meanpooled_seq = masked_mean(x, dim=1, mask=torch.ones(x.shape[:2], device=x.device, dtype=torch.bool)) | |
| meanpooled_latents = self.to_latents_from_mean_pooled_seq(meanpooled_seq) | |
| latents = torch.cat((meanpooled_latents, latents), dim=-2) | |
| 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) | |
| 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, | |
| max_seq_len: int = 257, # CLIP tokens + CLS token | |
| apply_pos_emb: bool = False, | |
| num_latents_mean_pooled: int = 0, # number of latents derived from mean pooled representation of the sequence | |
| ): | |
| super().__init__() | |
| self.pos_emb = nn.Embedding(max_seq_len, embedding_dim) if apply_pos_emb else None | |
| 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.to_latents_from_mean_pooled_seq = ( | |
| nn.Sequential( | |
| nn.LayerNorm(dim), | |
| nn.Linear(dim, dim * num_latents_mean_pooled), | |
| Rearrange("b (n d) -> b n d", n=num_latents_mean_pooled), | |
| ) | |
| if num_latents_mean_pooled > 0 | |
| else None | |
| ) | |
| 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): | |
| if self.pos_emb is not None: | |
| n, device = x.shape[1], x.device | |
| pos_emb = self.pos_emb(torch.arange(n, device=device)) | |
| x = x + pos_emb | |
| latents = self.latents.repeat(x.size(0), 1, 1) | |
| x = self.proj_in(x) | |
| if self.to_latents_from_mean_pooled_seq: | |
| meanpooled_seq = masked_mean(x, dim=1, mask=torch.ones(x.shape[:2], device=x.device, dtype=torch.bool)) | |
| meanpooled_latents = self.to_latents_from_mean_pooled_seq(meanpooled_seq) | |
| latents = torch.cat((meanpooled_latents, latents), dim=-2) | |
| 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) | |
| def masked_mean(t, *, dim, mask=None): | |
| if mask is None: | |
| return t.mean(dim=dim) | |
| denom = mask.sum(dim=dim, keepdim=True) | |
| mask = rearrange(mask, "b n -> b n 1") | |
| masked_t = t.masked_fill(~mask, 0.0) | |
| return masked_t.sum(dim=dim) / denom.clamp(min=1e-5) | |