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import os
import sys
import torch
import torch.nn as nn
from torch.nn import functional as F
from timm.models.layers import trunc_normal_
from functools import partial
import math
import numpy as np
def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
"""
grid_size: int of the grid height and width
return:
pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
"""
grid_h = np.arange(grid_size, dtype=np.float32)
grid_w = np.arange(grid_size, dtype=np.float32)
grid = np.meshgrid(grid_w, grid_h) # here w goes first
grid = np.stack(grid, axis=0)
grid = grid.reshape([2, 1, grid_size, grid_size])
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
if cls_token:
pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
return pos_embed
def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
assert embed_dim % 2 == 0
# use half of dimensions to encode grid_h
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
return emb
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
"""
embed_dim: output dimension for each position
pos: a list of positions to be encoded: size (M,)
out: (M, D)
"""
assert embed_dim % 2 == 0
omega = np.arange(embed_dim // 2, dtype=np.float32)
omega /= embed_dim / 2.
omega = 1. / 10000**omega # (D/2,)
pos = pos.reshape(-1) # (M,)
out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product
emb_sin = np.sin(out) # (M, D/2)
emb_cos = np.cos(out) # (M, D/2)
emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
return emb
def interpolate_pos_embed(model, checkpoint_model):
if 'pos_embed' in checkpoint_model:
pos_embed_checkpoint = checkpoint_model['pos_embed']
embedding_size = pos_embed_checkpoint.shape[-1]
num_patches = model.patch_embed.num_patches
num_extra_tokens = model.pos_embed.shape[-2] - num_patches
# height (== width) for the checkpoint position embedding
orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)
# height (== width) for the new position embedding
new_size = int(num_patches ** 0.5)
# class_token and dist_token are kept unchanged
if orig_size != new_size:
print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size))
extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
# only the position tokens are interpolated
pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)
pos_tokens = torch.nn.functional.interpolate(
pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)
pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
checkpoint_model['pos_embed'] = new_pos_embed
def get_abs_pos(abs_pos, tgt_size):
# abs_pos: L, C
# tgt_size: M
# return: M, C
src_size = int(math.sqrt(abs_pos.size(0)))
tgt_size = int(math.sqrt(tgt_size))
dtype = abs_pos.dtype
if src_size != tgt_size:
return F.interpolate(
abs_pos.float().reshape(1, src_size, src_size, -1).permute(0, 3, 1, 2),
size=(tgt_size, tgt_size),
mode="bicubic",
align_corners=False,
).permute(0, 2, 3, 1).flatten(0, 2).to(dtype=dtype)
else:
return abs_pos
class Resampler(nn.Module):
"""
A 2D perceiver-resampler network with one cross attention layers by
(grid_size**2) learnable queries and 2d sincos pos_emb
Outputs:
A tensor with the shape of (grid_size**2, embed_dim)
"""
def __init__(
self,
grid_size,
embed_dim,
num_heads,
kv_dim=None,
norm_layer=partial(nn.LayerNorm, eps=1e-6)
):
super().__init__()
self.num_queries = grid_size ** 2
self.embed_dim = embed_dim
self.num_heads = num_heads
self.pos_embed = nn.Parameter(
torch.from_numpy(get_2d_sincos_pos_embed(embed_dim, grid_size)).float()
).requires_grad_(False)
self.query = nn.Parameter(torch.zeros(self.num_queries, embed_dim))
trunc_normal_(self.query, std=.02)
if kv_dim is not None and kv_dim != embed_dim:
self.kv_proj = nn.Linear(kv_dim, embed_dim, bias=False)
else:
self.kv_proj = nn.Identity()
self.attn = nn.MultiheadAttention(embed_dim, num_heads)
self.ln_q = norm_layer(embed_dim)
self.ln_kv = norm_layer(embed_dim)
self.ln_post = norm_layer(embed_dim)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def forward(self, x, attn_mask=None):
pos_embed = get_abs_pos(self.pos_embed, x.size(1))
x = self.kv_proj(x)
x = self.ln_kv(x).permute(1, 0, 2)
k = x.clone()
k[1:] = x[1:] + pos_embed.unsqueeze(1)
N = x.shape[1]
q = self.ln_q(self.query)
out = self.attn(
self._repeat(q, N) + self.pos_embed.unsqueeze(1),
k,
x,
attn_mask=attn_mask)[0]
out = self.ln_post(out.permute(1, 0, 2))
return out
def _repeat(self, query, N: int):
return query.unsqueeze(1).repeat(1, N, 1)
def create_resampler(num_query_token=32, vision_width=1408,):
attn_pool = Resampler(
grid_size=int(math.sqrt(num_query_token)),
embed_dim=4096,
num_heads=4096 // 128,
kv_dim=vision_width,
)
return attn_pool
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