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from functools import partial
import numpy as np
import torch
from torch import nn
# from torch.nn.init import trunc_normal_
def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
# Convert tensor to float32
tensor = tensor.float()
with torch.no_grad():
tensor.normal_(mean, std)
valid = (tensor > a) & (tensor < b)
ind = valid.max()
tensor = tensor[ind].uniform_(a, b)
return tensor.half()
def get_2d_sincos_pos_embed(embed_dim, image_size):
"""
image_size: image_size or (image_height, image_width)
return:
pos_embed: [image_height, image_width, embed_dim]
"""
if isinstance(image_size, int):
grid_h_size, grid_w_size = image_size, image_size
else:
grid_h_size, grid_w_size = image_size[0], image_size[1]
grid_h = np.arange(grid_h_size, dtype=np.float32)
grid_w = np.arange(grid_w_size, dtype=np.float32)
grid = np.meshgrid(grid_w, grid_h) # here w goes first
grid = np.stack(grid, axis=0)
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
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_new(embed_dim // 2, grid[0]) # (H, W, D/2)
emb_w = get_1d_sincos_pos_embed_from_grid_new(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_new(embed_dim, pos):
"""
embed_dim: output dimension for each position
pos: a list of positions to be encoded: size (H, W)
out: (H, W, 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,)
out = np.einsum('hw,d->hwd', pos, omega) # (H, W, D/2), outer product
emb_sin = np.sin(out) # (H, W, D/2)
emb_cos = np.cos(out) # (H, W, D/2)
emb = np.concatenate([emb_sin, emb_cos], axis=-1) # (H, W, D)
return emb
class Resampler(nn.Module):
"""
A 2D perceiver-resampler network with one cross attention layers by
given learnable queries and 2d sincos pos_emb
Outputs:
A tensor with the shape of (batch_size, num_queries, embed_dim)
"""
def __init__(
self,
num_queries,
embed_dim,
num_heads,
kv_dim=None,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
adaptive=False,
max_size=(70, 70),
):
super().__init__()
self.num_queries = num_queries
self.embed_dim = embed_dim
self.num_heads = num_heads
self.adaptive = adaptive
self.max_size = max_size
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.proj = nn.Parameter((embed_dim ** -0.5) * torch.randn(embed_dim, embed_dim))
self._set_2d_pos_cache(self.max_size)
self.apply(self._init_weights)
def _set_2d_pos_cache(self, max_size, device='cpu'):
pos_embed = torch.from_numpy(get_2d_sincos_pos_embed(self.embed_dim, max_size)).float().to(device)
self.register_buffer("pos_embed", pos_embed, persistent=False)
def _adjust_pos_cache(self, tgt_sizes, device):
max_h = torch.max(tgt_sizes[:, 0])
max_w = torch.max(tgt_sizes[:, 1])
if max_h > self.max_size[0] or max_w > self.max_size[1]:
self.max_size = [max(max_h, self.max_size[0]), max(max_w, self.max_size[1])]
self._set_2d_pos_cache(self.max_size, device)
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, tgt_sizes=None):
assert x.shape[0] == tgt_sizes.shape[0]
bs = x.shape[0]
device = x.device
dtype = x.dtype
patch_len = tgt_sizes[:, 0] * tgt_sizes[:, 1]
self._adjust_pos_cache(tgt_sizes, device=device)
max_patch_len = torch.max(patch_len)
key_padding_mask = torch.zeros((bs, max_patch_len), dtype=torch.bool, device=device)
pos_embed = []
for i in range(bs):
tgt_h, tgt_w = tgt_sizes[i]
pos_embed.append(self.pos_embed[:tgt_h, :tgt_w, :].reshape((tgt_h * tgt_w, -1)).to(dtype)) # patches * D
key_padding_mask[i, patch_len[i]:] = True
pos_embed = torch.nn.utils.rnn.pad_sequence(
pos_embed, batch_first=True, padding_value=0.0).permute(1, 0, 2) # BLD => L * B * D
x = self.kv_proj(x) # B * L * D
x = self.ln_kv(x).permute(1, 0, 2) # L * B * D
q = self.ln_q(self.query) # Q * D
out = self.attn(
self._repeat(q, bs), # Q * B * D
x + pos_embed, # L * B * D + L * B * D
x,
key_padding_mask=key_padding_mask)[0]
# out: Q * B * D
x = out.permute(1, 0, 2) # B * Q * D
x = self.ln_post(x)
x = x @ self.proj
return x
def _repeat(self, query, N: int):
return query.unsqueeze(1).repeat(1, N, 1) |