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from typing import Dict, Optional
from omegaconf import DictConfig
import torch
import torch.nn as nn
from matanyone.model.group_modules import GConv2d
from matanyone.utils.tensor_utils import aggregate
from matanyone.model.transformer.positional_encoding import PositionalEncoding
from matanyone.model.transformer.transformer_layers import CrossAttention, SelfAttention, FFN, PixelFFN
class QueryTransformerBlock(nn.Module):
def __init__(self, model_cfg: DictConfig):
super().__init__()
this_cfg = model_cfg.object_transformer
self.embed_dim = this_cfg.embed_dim
self.num_heads = this_cfg.num_heads
self.num_queries = this_cfg.num_queries
self.ff_dim = this_cfg.ff_dim
self.read_from_pixel = CrossAttention(self.embed_dim,
self.num_heads,
add_pe_to_qkv=this_cfg.read_from_pixel.add_pe_to_qkv)
self.self_attn = SelfAttention(self.embed_dim,
self.num_heads,
add_pe_to_qkv=this_cfg.query_self_attention.add_pe_to_qkv)
self.ffn = FFN(self.embed_dim, self.ff_dim)
self.read_from_query = CrossAttention(self.embed_dim,
self.num_heads,
add_pe_to_qkv=this_cfg.read_from_query.add_pe_to_qkv,
norm=this_cfg.read_from_query.output_norm)
self.pixel_ffn = PixelFFN(self.embed_dim)
def forward(
self,
x: torch.Tensor,
pixel: torch.Tensor,
query_pe: torch.Tensor,
pixel_pe: torch.Tensor,
attn_mask: torch.Tensor,
need_weights: bool = False) -> (torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor):
# x: (bs*num_objects)*num_queries*embed_dim
# pixel: bs*num_objects*C*H*W
# query_pe: (bs*num_objects)*num_queries*embed_dim
# pixel_pe: (bs*num_objects)*(H*W)*C
# attn_mask: (bs*num_objects*num_heads)*num_queries*(H*W)
# bs*num_objects*C*H*W -> (bs*num_objects)*(H*W)*C
pixel_flat = pixel.flatten(3, 4).flatten(0, 1).transpose(1, 2).contiguous()
x, q_weights = self.read_from_pixel(x,
pixel_flat,
query_pe,
pixel_pe,
attn_mask=attn_mask,
need_weights=need_weights)
x = self.self_attn(x, query_pe)
x = self.ffn(x)
pixel_flat, p_weights = self.read_from_query(pixel_flat,
x,
pixel_pe,
query_pe,
need_weights=need_weights)
pixel = self.pixel_ffn(pixel, pixel_flat)
if need_weights:
bs, num_objects, _, h, w = pixel.shape
q_weights = q_weights.view(bs, num_objects, self.num_heads, self.num_queries, h, w)
p_weights = p_weights.transpose(2, 3).view(bs, num_objects, self.num_heads,
self.num_queries, h, w)
return x, pixel, q_weights, p_weights
class QueryTransformer(nn.Module):
def __init__(self, model_cfg: DictConfig):
super().__init__()
this_cfg = model_cfg.object_transformer
self.value_dim = model_cfg.value_dim
self.embed_dim = this_cfg.embed_dim
self.num_heads = this_cfg.num_heads
self.num_queries = this_cfg.num_queries
# query initialization and embedding
self.query_init = nn.Embedding(self.num_queries, self.embed_dim)
self.query_emb = nn.Embedding(self.num_queries, self.embed_dim)
# projection from object summaries to query initialization and embedding
self.summary_to_query_init = nn.Linear(self.embed_dim, self.embed_dim)
self.summary_to_query_emb = nn.Linear(self.embed_dim, self.embed_dim)
self.pixel_pe_scale = model_cfg.pixel_pe_scale
self.pixel_pe_temperature = model_cfg.pixel_pe_temperature
self.pixel_init_proj = GConv2d(self.embed_dim, self.embed_dim, kernel_size=1)
self.pixel_emb_proj = GConv2d(self.embed_dim, self.embed_dim, kernel_size=1)
self.spatial_pe = PositionalEncoding(self.embed_dim,
scale=self.pixel_pe_scale,
temperature=self.pixel_pe_temperature,
channel_last=False,
transpose_output=True)
# transformer blocks
self.num_blocks = this_cfg.num_blocks
self.blocks = nn.ModuleList(
QueryTransformerBlock(model_cfg) for _ in range(self.num_blocks))
self.mask_pred = nn.ModuleList(
nn.Sequential(nn.ReLU(), GConv2d(self.embed_dim, 1, kernel_size=1))
for _ in range(self.num_blocks + 1))
self.act = nn.ReLU(inplace=True)
def forward(self,
pixel: torch.Tensor,
obj_summaries: torch.Tensor,
selector: Optional[torch.Tensor] = None,
need_weights: bool = False,
seg_pass=False) -> (torch.Tensor, Dict[str, torch.Tensor]):
# pixel: B*num_objects*embed_dim*H*W
# obj_summaries: B*num_objects*T*num_queries*embed_dim
T = obj_summaries.shape[2]
bs, num_objects, _, H, W = pixel.shape
# normalize object values
# the last channel is the cumulative area of the object
obj_summaries = obj_summaries.view(bs * num_objects, T, self.num_queries,
self.embed_dim + 1)
# sum over time
# during inference, T=1 as we already did streaming average in memory_manager
obj_sums = obj_summaries[:, :, :, :-1].sum(dim=1)
obj_area = obj_summaries[:, :, :, -1:].sum(dim=1)
obj_values = obj_sums / (obj_area + 1e-4)
obj_init = self.summary_to_query_init(obj_values)
obj_emb = self.summary_to_query_emb(obj_values)
# positional embeddings for object queries
query = self.query_init.weight.unsqueeze(0).expand(bs * num_objects, -1, -1) + obj_init
query_emb = self.query_emb.weight.unsqueeze(0).expand(bs * num_objects, -1, -1) + obj_emb
# positional embeddings for pixel features
pixel_init = self.pixel_init_proj(pixel)
pixel_emb = self.pixel_emb_proj(pixel)
pixel_pe = self.spatial_pe(pixel.flatten(0, 1))
pixel_emb = pixel_emb.flatten(3, 4).flatten(0, 1).transpose(1, 2).contiguous()
pixel_pe = pixel_pe.flatten(1, 2) + pixel_emb
pixel = pixel_init
# run the transformer
aux_features = {'logits': []}
# first aux output
aux_logits = self.mask_pred[0](pixel).squeeze(2)
attn_mask = self._get_aux_mask(aux_logits, selector, seg_pass=seg_pass)
aux_features['logits'].append(aux_logits)
for i in range(self.num_blocks):
query, pixel, q_weights, p_weights = self.blocks[i](query,
pixel,
query_emb,
pixel_pe,
attn_mask,
need_weights=need_weights)
if self.training or i <= self.num_blocks - 1 or need_weights:
aux_logits = self.mask_pred[i + 1](pixel).squeeze(2)
attn_mask = self._get_aux_mask(aux_logits, selector, seg_pass=seg_pass)
aux_features['logits'].append(aux_logits)
aux_features['q_weights'] = q_weights # last layer only
aux_features['p_weights'] = p_weights # last layer only
if self.training:
# no need to save all heads
aux_features['attn_mask'] = attn_mask.view(bs, num_objects, self.num_heads,
self.num_queries, H, W)[:, :, 0]
return pixel, aux_features
def _get_aux_mask(self, logits: torch.Tensor, selector: torch.Tensor, seg_pass=False) -> torch.Tensor:
# logits: batch_size*num_objects*H*W
# selector: batch_size*num_objects*1*1
# returns a mask of shape (batch_size*num_objects*num_heads)*num_queries*(H*W)
# where True means the attention is blocked
if selector is None:
prob = logits.sigmoid()
else:
prob = logits.sigmoid() * selector
logits = aggregate(prob, dim=1)
is_foreground = (logits[:, 1:] >= logits.max(dim=1, keepdim=True)[0])
foreground_mask = is_foreground.bool().flatten(start_dim=2)
inv_foreground_mask = ~foreground_mask
inv_background_mask = foreground_mask
aux_foreground_mask = inv_foreground_mask.unsqueeze(2).unsqueeze(2).repeat(
1, 1, self.num_heads, self.num_queries // 2, 1).flatten(start_dim=0, end_dim=2)
aux_background_mask = inv_background_mask.unsqueeze(2).unsqueeze(2).repeat(
1, 1, self.num_heads, self.num_queries // 2, 1).flatten(start_dim=0, end_dim=2)
aux_mask = torch.cat([aux_foreground_mask, aux_background_mask], dim=1)
aux_mask[torch.where(aux_mask.sum(-1) == aux_mask.shape[-1])] = False
return aux_mask |