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from enum import IntEnum
from functools import partial
import einops
import numpy as np
import torch
from contextlib import nullcontext
import torch
import torch.nn as nn
from transformers import CLIPTextModelWithProjection
import copy
from transformers import T5ForConditionalGeneration
from transformers.modeling_outputs import BaseModelOutput
import torch.nn.functional as F
def get_mlp_head(input_size, hidden_size, output_size, dropout=0):
return nn.Sequential(*[
nn.Linear(input_size, hidden_size),
nn.ReLU(),
nn.LayerNorm(hidden_size, eps=1e-12),
nn.Dropout(dropout),
nn.Linear(hidden_size, output_size)
])
def layer_repeat(module, N, share_layer=False):
if share_layer:
return nn.ModuleList([module] * N)
else:
return nn.ModuleList([copy.deepcopy(module) for _ in range(N - 1)] + [module])
class CLIPLanguageEncoder(nn.Module):
def __init__(self, weights="openai/clip-vit-large-patch14", output_dim=768, freeze_backbone=True, use_projection=False, projection_type='mlp', num_projection_layers=1, dropout=0.1):
super().__init__()
self.context = torch.no_grad if freeze_backbone else nullcontext
self.model = CLIPTextModelWithProjection.from_pretrained(weights)
self.use_projection = use_projection
self.projection_type = projection_type
if use_projection:
if projection_type == 'mlp':
self.projection = get_mlp_head(self.model.config.hidden_size, output_dim, output_dim, dropout=dropout)
else:
raise NotImplementedError
#self.attention = nn.MultiheadAttention(embed_dim=768, num_heads=12, batch_first=True)
def forward(self, txt_ids, txt_masks):
with self.context():
txt = self.model(txt_ids, txt_masks).last_hidden_state
txt = self.model.text_projection(txt)
txt = torch.nn.functional.normalize(txt, p=2, dim=2)
#txt = self.attention(txt, txt, txt, key_padding_mask=txt_masks.logical_not())[0]
if self.use_projection:
if self.projection_type == 'mlp':
txt = self.projection(txt)
elif self.projection_type == 'attention':
for attention_layer in self.projection:
txt = attention_layer(txt, tgt_key_padding_mask = txt_masks.logical_not())
else:
raise NotImplementedError
return txt
def _init_weights_bert(module, std=0.02):
"""
Huggingface transformer weight initialization,
most commonly for bert initialization
"""
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
def break_up_pc(pc):
"""
Split the pointcloud into xyz positions and features tensors.
This method is taken from VoteNet codebase (https://github.com/facebookresearch/votenet)
@param pc: pointcloud [N, 3 + C]
:return: the xyz tensor and the feature tensor
"""
xyz = pc[..., 0:3].contiguous()
features = (
pc[..., 3:].transpose(1, 2).contiguous()
if pc.size(-1) > 3 else None
)
return xyz, features
class ObjectEncoder(nn.Module):
def __init__(self, backbone='none', input_feat_size=768, hidden_size=768, freeze_backbone=False, use_projection=False,
tgt_cls_num=607, pretrained=None, dropout=0.1, use_cls_head=True):
super().__init__()
self.freeze_backbone = freeze_backbone
self.context = torch.no_grad if freeze_backbone else nullcontext
# if backbone == 'pointnet++':
# self.backbone = PointNetPP(
# sa_n_points=[32, 16, None],
# sa_n_samples=[32, 32, None],
# sa_radii=[0.2, 0.4, None],
# sa_mlps=[[3, 64, 64, 128], [128, 128, 128, 256], [256, 256, 512, 768]],
# )
if use_cls_head:
self.cls_head = get_mlp_head(input_feat_size, input_feat_size // 2, tgt_cls_num, dropout=0.3)
self.use_projection = use_projection
if use_projection:
self.input_feat_proj = nn.Sequential(nn.Linear(input_feat_size, hidden_size), nn.LayerNorm(hidden_size))
else:
assert input_feat_size == hidden_size, "input_feat_size should be equal to hidden_size!"
if dropout > 0:
self.dropout = nn.Dropout(dropout)
# load weights
self.apply(_init_weights_bert)
if pretrained:
print("load pretrained weights from {}".format(pretrained))
pre_state_dict = torch.load(pretrained)
state_dict = {}
for k, v in pre_state_dict.items():
if k[0] in ['0', '2', '4']: # key mapping for voxel
k = 'cls_head.' + k
k = k.replace('vision_encoder.vis_cls_head.', 'cls_head.') # key mapping for mv
k = k.replace('point_cls_head.', 'cls_head.') # key mapping for pc
k = k.replace('point_feature_extractor.', 'backbone.')
state_dict[k] = v
warning = self.load_state_dict(state_dict, strict=False)
print(warning)
def freeze_bn(self, m):
for layer in m.modules():
if isinstance(layer, nn.BatchNorm2d):
layer.eval()
def forward(self, obj_feats, **kwargs):
if self.freeze_backbone and hasattr(self, 'backbone'):
self.freeze_bn(self.backbone)
batch_size, num_objs = obj_feats.shape[:2]
with self.context():
if hasattr(self, 'backbone'):
obj_feats = self.backbone(einops.rearrange(obj_feats, 'b o p d -> (b o) p d'))
obj_feats = einops.rearrange(obj_feats, '(b o) d -> b o d', b=batch_size)
obj_embeds = self.input_feat_proj(obj_feats) if self.use_projection else obj_feats
if hasattr(self, 'dropout'):
obj_embeds = self.dropout(obj_embeds)
if hasattr(self, 'cls_head'):
obj_cls_logits = self.cls_head(obj_feats)
return obj_embeds, obj_cls_logits
else:
return obj_embeds
class SelfAttentionLayer(nn.Module):
def __init__(
self,
d_model,
nhead,
dropout=0.0,
activation="relu",
normalize_before=False,
batch_first=False,
):
super().__init__()
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout, batch_first=batch_first)
self.norm = nn.LayerNorm(d_model)
self.dropout = nn.Dropout(dropout)
self.activation = get_activation_fn(activation)
self.normalize_before = normalize_before
self._reset_parameters()
def _reset_parameters(self):
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
def with_pos_embed(self, tensor, pos):
return tensor if pos is None else tensor + pos
def forward_post(
self, tgt, attn_mask=None, tgt_key_padding_mask=None, query_pos=None
):
q = k = self.with_pos_embed(tgt, query_pos)
tgt2 = self.self_attn(
q,
k,
value=tgt,
attn_mask=attn_mask,
key_padding_mask=tgt_key_padding_mask,
)[0]
tgt = tgt + self.dropout(tgt2)
tgt = self.norm(tgt)
return tgt
def forward_pre(
self, tgt, attn_mask=None, tgt_key_padding_mask=None, query_pos=None
):
tgt2 = self.norm(tgt)
q = k = self.with_pos_embed(tgt2, query_pos)
tgt2 = self.self_attn(
q,
k,
value=tgt2,
attn_mask=attn_mask,
key_padding_mask=tgt_key_padding_mask,
)[0]
tgt = tgt + self.dropout(tgt2)
return tgt
def forward(
self, tgt, attn_mask=None, tgt_key_padding_mask=None, query_pos=None
):
if self.normalize_before:
return self.forward_pre(
tgt, attn_mask, tgt_key_padding_mask, query_pos
)
return self.forward_post(
tgt, attn_mask, tgt_key_padding_mask, query_pos
)
class CrossAttentionLayer(nn.Module):
def __init__(
self,
d_model,
nhead,
dropout=0.0,
activation="relu",
normalize_before=False,
batch_first=False,
):
super().__init__()
self.multihead_attn = nn.MultiheadAttention(
d_model, nhead, dropout=dropout, batch_first=batch_first, add_zero_attn=True
)
self.norm = nn.LayerNorm(d_model)
self.dropout = nn.Dropout(dropout)
self.activation = get_activation_fn(activation)
self.normalize_before = normalize_before
self._reset_parameters()
def _reset_parameters(self):
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
def with_pos_embed(self, tensor, pos):
return tensor if pos is None else tensor + pos
def forward_post(
self,
tgt,
memory,
attn_mask=None,
memory_key_padding_mask=None,
pos=None,
query_pos=None,
):
tgt2 = self.multihead_attn(
query=self.with_pos_embed(tgt, query_pos),
key=self.with_pos_embed(memory, pos),
value=memory,
attn_mask=attn_mask,
key_padding_mask=memory_key_padding_mask,
)[0]
tgt = tgt + self.dropout(tgt2)
tgt = self.norm(tgt)
return tgt
def forward_pre(
self,
tgt,
memory,
attn_mask=None,
memory_key_padding_mask=None,
pos=None,
query_pos=None,
):
tgt2 = self.norm(tgt)
tgt2 = self.multihead_attn(
query=self.with_pos_embed(tgt2, query_pos),
key=self.with_pos_embed(memory, pos),
value=memory,
attn_mask=attn_mask,
key_padding_mask=memory_key_padding_mask,
)[0]
tgt = tgt + self.dropout(tgt2)
return tgt
def forward(
self,
tgt,
memory,
attn_mask=None,
memory_key_padding_mask=None,
pos=None,
query_pos=None,
):
if self.normalize_before:
return self.forward_pre(
tgt,
memory,
attn_mask,
memory_key_padding_mask,
pos,
query_pos,
)
return self.forward_post(
tgt, memory, attn_mask, memory_key_padding_mask, pos, query_pos
)
class FFNLayer(nn.Module):
def __init__(
self,
d_model,
dim_feedforward=2048,
dropout=0.0,
activation="relu",
normalize_before=False,
):
super().__init__()
# Implementation of Feedforward model
self.linear1 = nn.Linear(d_model, dim_feedforward)
self.dropout = nn.Dropout(dropout)
self.linear2 = nn.Linear(dim_feedforward, d_model)
self.norm = nn.LayerNorm(d_model)
self.activation = get_activation_fn(activation)
self.normalize_before = normalize_before
self._reset_parameters()
def _reset_parameters(self):
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
def with_pos_embed(self, tensor, pos):
return tensor if pos is None else tensor + pos
def forward_post(self, tgt):
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
tgt = tgt + self.dropout(tgt2)
tgt = self.norm(tgt)
return tgt
def forward_pre(self, tgt):
tgt2 = self.norm(tgt)
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
tgt = tgt + self.dropout(tgt2)
return tgt
def forward(self, tgt):
if self.normalize_before:
return self.forward_pre(tgt)
return self.forward_post(tgt)
def get_activation_fn(activation_type):
if activation_type not in ["relu", "gelu", "glu"]:
raise RuntimeError(f"activation function currently support relu/gelu, not {activation_type}")
return getattr(F, activation_type)
class MultiHeadAttentionSpatial(nn.Module):
def __init__(
self, d_model, n_head, dropout=0.1, spatial_multihead=True, spatial_dim=5,
spatial_attn_fusion='mul',
):
super().__init__()
assert d_model % n_head == 0, 'd_model: %d, n_head: %d' % (d_model, n_head)
self.n_head = n_head
self.d_model = d_model
self.d_per_head = d_model // n_head
self.spatial_multihead = spatial_multihead
self.spatial_dim = spatial_dim
self.spatial_attn_fusion = spatial_attn_fusion
self.w_qs = nn.Linear(d_model, d_model)
self.w_ks = nn.Linear(d_model, d_model)
self.w_vs = nn.Linear(d_model, d_model)
self.fc = nn.Linear(d_model, d_model)
self.spatial_n_head = n_head if spatial_multihead else 1
if self.spatial_attn_fusion in ['mul', 'bias', 'add']:
self.pairwise_loc_fc = nn.Linear(spatial_dim, self.spatial_n_head)
elif self.spatial_attn_fusion == 'ctx':
self.pairwise_loc_fc = nn.Linear(spatial_dim, d_model)
elif self.spatial_attn_fusion == 'cond':
self.lang_cond_fc = nn.Linear(d_model, self.spatial_n_head * (spatial_dim + 1))
else:
raise NotImplementedError('unsupported spatial_attn_fusion %s' % (self.spatial_attn_fusion))
def forward(self, q, k, v, pairwise_locs, key_padding_mask=None, txt_embeds=None):
residual = q
q = einops.rearrange(self.w_qs(q), 'b l (head k) -> head b l k', head=self.n_head)
k = einops.rearrange(self.w_ks(k), 'b t (head k) -> head b t k', head=self.n_head)
v = einops.rearrange(self.w_vs(v), 'b t (head v) -> head b t v', head=self.n_head)
attn = torch.einsum('hblk,hbtk->hblt', q, k) / np.sqrt(q.shape[-1])
if self.spatial_attn_fusion in ['mul', 'bias', 'add']:
loc_attn = self.pairwise_loc_fc(pairwise_locs)
loc_attn = einops.rearrange(loc_attn, 'b l t h -> h b l t')
if self.spatial_attn_fusion == 'mul':
loc_attn = F.relu(loc_attn)
if not self.spatial_multihead:
loc_attn = einops.repeat(loc_attn, 'h b l t -> (h nh) b l t', nh=self.n_head)
elif self.spatial_attn_fusion == 'ctx':
loc_attn = self.pairwise_loc_fc(pairwise_locs)
loc_attn = einops.rearrange(loc_attn, 'b l t (h k) -> h b l t k', h=self.n_head)
loc_attn = torch.einsum('hblk,hbltk->hblt', q, loc_attn) / np.sqrt(q.shape[-1])
elif self.spatial_attn_fusion == 'cond':
spatial_weights = self.lang_cond_fc(residual)
spatial_weights = einops.rearrange(spatial_weights, 'b l (h d) -> h b l d', h=self.spatial_n_head,
d=self.spatial_dim + 1)
if self.spatial_n_head == 1:
spatial_weights = einops.repeat(spatial_weights, '1 b l d -> h b l d', h=self.n_head)
spatial_bias = spatial_weights[..., :1]
spatial_weights = spatial_weights[..., 1:]
loc_attn = torch.einsum('hbld,bltd->hblt', spatial_weights, pairwise_locs) + spatial_bias
loc_attn = torch.sigmoid(loc_attn)
if key_padding_mask is not None:
mask = einops.repeat(key_padding_mask, 'b t -> h b l t', h=self.n_head, l=q.size(2))
attn = attn.masked_fill(mask, -np.inf)
if self.spatial_attn_fusion in ['mul', 'cond']:
loc_attn = loc_attn.masked_fill(mask, 0)
else:
loc_attn = loc_attn.masked_fill(mask, -np.inf)
if self.spatial_attn_fusion == 'add':
fused_attn = (torch.softmax(attn, 3) + torch.softmax(loc_attn, 3)) / 2
else:
if self.spatial_attn_fusion in ['mul', 'cond']:
fused_attn = torch.log(torch.clamp(loc_attn, min=1e-6)) + attn
else:
fused_attn = loc_attn + attn
fused_attn = torch.softmax(fused_attn, 3)
assert torch.sum(torch.isnan(fused_attn) == 0), print(fused_attn)
output = torch.einsum('hblt,hbtv->hblv', fused_attn, v)
output = einops.rearrange(output, 'head b l v -> b l (head v)')
output = self.fc(output)
return output, fused_attn
class SpatialSelfAttentionLayer(nn.Module):
def __init__(
self,
d_model,
nhead,
dropout=0.0,
activation="relu",
normalize_before=False,
batch_first=False,
spatial_multihead=True, spatial_dim=5, spatial_attn_fusion='mul'
):
super().__init__()
self.self_attn = MultiHeadAttentionSpatial(
d_model, nhead, dropout=dropout,
spatial_multihead=spatial_multihead,
spatial_dim=spatial_dim,
spatial_attn_fusion=spatial_attn_fusion,
)
self.norm = nn.LayerNorm(d_model)
self.dropout = nn.Dropout(dropout)
self.activation = get_activation_fn(activation)
self.normalize_before = normalize_before
self._reset_parameters()
def _reset_parameters(self):
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
def with_pos_embed(self, tensor, pos):
return tensor if pos is None else tensor + pos
def forward_post(
self, tgt, attn_mask=None, tgt_key_padding_mask=None, query_pos=None,
pairwise_locs=None
):
q = k = self.with_pos_embed(tgt, query_pos)
tgt2 = self.self_attn(
q,
k,
tgt,
key_padding_mask=tgt_key_padding_mask,
pairwise_locs=pairwise_locs,
)[0]
tgt = tgt + self.dropout(tgt2)
tgt = self.norm(tgt)
return tgt
def forward_pre(
self, tgt, attn_mask=None, tgt_key_padding_mask=None, query_pos=None,
pairwise_locs=None
):
tgt2 = self.norm(tgt)
q = k = self.with_pos_embed(tgt2, query_pos)
tgt2 = self.self_attn(
q,
k,
tgt,
key_padding_mask=tgt_key_padding_mask,
pairwise_locs=pairwise_locs,
)[0]
tgt = tgt + self.dropout(tgt2)
return tgt
def forward(
self, tgt, attn_mask=None, tgt_key_padding_mask=None, query_pos=None,
pairwise_locs=None
):
if self.normalize_before:
return self.forward_pre(
tgt, attn_mask, tgt_key_padding_mask, query_pos,
pairwise_locs
)
return self.forward_post(
tgt, attn_mask, tgt_key_padding_mask, query_pos,
pairwise_locs
)
class QueryEncoderLayer(nn.Module):
def __init__(self, d_model, nhead, memories, dim_feedforward=2048, dropout=0.1, activation="relu", prenorm=False, spatial_selfattn=False, structure='mixed', memory_dropout=0, drop_memories_test=[]):
super().__init__()
if spatial_selfattn:
self.self_attn = SpatialSelfAttentionLayer(d_model, nhead, dropout=dropout, activation=activation, normalize_before=prenorm, batch_first=True)
else:
self.self_attn = SelfAttentionLayer(d_model, nhead, dropout=dropout, activation=activation, normalize_before=prenorm, batch_first=True)
cross_attn_layer = CrossAttentionLayer(d_model, nhead, dropout=dropout, activation=activation, normalize_before=prenorm, batch_first=True)
self.cross_attn_list = layer_repeat(cross_attn_layer, len(memories))
self.memory2ca = {memory:ca for memory, ca in zip(memories, self.cross_attn_list)}
self.ffn = FFNLayer(d_model, dim_feedforward, dropout=dropout, activation=activation, normalize_before=prenorm)
self.structure = structure
self.memories = memories
self.memory_dropout = memory_dropout
self.drop_memories_test = drop_memories_test
if structure == 'gate':
self.gate_proj = nn.Linear(d_model, d_model)
def forward(self, query, input_dict, pairwise_locs=None):
_, query_masks, query_pos = input_dict['query']
def sequential_ca(query, memories):
for memory in memories:
cross_attn = self.memory2ca[memory]
feat, mask, pos = input_dict[memory]
if mask.ndim == 2:
memory_key_padding_mask = mask
attn_mask = None
else:
memory_key_padding_mask = None
attn_mask = mask
query = cross_attn(tgt=query, memory=feat, attn_mask=attn_mask, memory_key_padding_mask = memory_key_padding_mask, query_pos = query_pos, pos = pos)
return query
def parallel_ca(query, memories):
assert 'prompt' not in memories
query_list = []
for memory in memories:
cross_attn = self.memory2ca[memory]
feat, mask, pos = input_dict[memory]
if mask.ndim == 2:
memory_key_padding_mask = mask
attn_mask = None
else:
memory_key_padding_mask = None
attn_mask = mask
update = cross_attn(tgt=query, memory=feat, attn_mask=attn_mask, memory_key_padding_mask = memory_key_padding_mask, query_pos = query_pos, pos = pos)
query_list.append(update)
# training time memory dropout
if self.training and self.memory_dropout > 0.0:
dropout_mask = torch.rand(query.shape[0], len(memories), device=query.device) > self.memory_dropout
num_remained_memories = dropout_mask.sum(dim=1)
dropout_mask = torch.logical_or(dropout_mask, num_remained_memories.unsqueeze(-1) == 0)
num_remained_memories = dropout_mask.sum(dim=1)
query_tensor = torch.stack(query_list, dim=1)
query = (query_tensor * dropout_mask.unsqueeze(-1).unsqueeze(-1)).sum(dim=1) / num_remained_memories.unsqueeze(-1).unsqueeze(-1).float()
else:
query = torch.stack(query_list, dim=1).mean(dim=1)
return query
memories = self.memories if self.training else [m for m in self.memories if m not in self.drop_memories_test]
if self.structure == 'sequential':
query = sequential_ca(query, memories)
elif self.structure == 'parallel':
query = parallel_ca(query, memories)
elif self.structure == 'mixed':
# [mv,pc,vx] + prompt
query = parallel_ca(query, [m for m in memories if m != 'prompt'])
query = sequential_ca(query, ['prompt'])
elif self.structure == 'gate':
prompt = sequential_ca(query, ['prompt'])
gate = torch.sigmoid(self.gate_proj(prompt))
update = parallel_ca(query, [m for m in self.memories if m != 'prompt'])
query = (1. - gate) * query + gate * update
else:
raise NotImplementedError(f"Unknow structure type: {self.structure}")
if isinstance(self.self_attn, SpatialSelfAttentionLayer):
query = self.self_attn(query, tgt_key_padding_mask = query_masks, query_pos = query_pos,
pairwise_locs = pairwise_locs)
else:
query = self.self_attn(query, tgt_key_padding_mask = query_masks, query_pos = query_pos)
query = self.ffn(query)
return query
class QueryMaskEncoder(nn.Module):
def __init__(self, memories=[], memory_dropout=0.0, hidden_size=768, num_attention_heads=12, num_layers=4,
share_layer=False, spatial_selfattn=False, structure='sequential', drop_memories_test=[], use_self_mask=False, num_blocks=1):
super().__init__()
self.spatial_selfattn = spatial_selfattn
query_encoder_layer = QueryEncoderLayer(hidden_size, num_attention_heads, memories, spatial_selfattn=spatial_selfattn, structure=structure, memory_dropout=memory_dropout, drop_memories_test=drop_memories_test)
self.unified_encoder = layer_repeat(query_encoder_layer, num_layers, share_layer)
self.apply(_init_weights_bert)
self.memory_dropout = memory_dropout
self.scene_meomories = [x for x in memories if x != 'prompt']
self.drop_memories_test = drop_memories_test
self.use_self_mask = use_self_mask
self.num_heads = num_attention_heads
self.num_blocks = num_blocks
def forward(self, input_dict, pairwise_locs, mask_head=None):
predictions_class, predictions_mask = [], []
query = input_dict['query'][0]
voxel_feat = input_dict['voxel'][0] if 'voxel' in input_dict.keys() else None
for block_counter in range(self.num_blocks):
for i, layer in enumerate(self.unified_encoder):
if mask_head is not None:
output_class, outputs_mask, attn_mask = mask_head(query)
predictions_class.append(output_class)
predictions_mask.append(outputs_mask)
if self.use_self_mask:
attn_mask[attn_mask.all(-1)] = False # prevent query to attend to no point
attn_mask = attn_mask.repeat_interleave(self.num_heads, 0)
for memory in input_dict.keys():
if memory in ['query', 'prompt']:
continue
input_dict[memory][1] = attn_mask
if isinstance(voxel_feat, list):
input_dict['voxel'][0] = voxel_feat[i] # select voxel features from multi-scale
query = layer(query, input_dict, pairwise_locs)
return query, predictions_class, predictions_mask
class PromptType(IntEnum):
TXT = 1
IMAGE = 2
LOC = 3
class GroundHead(nn.Module):
def __init__(self, input_size=768, hidden_size=768, dropout=0.3):
super().__init__()
self.og3d_head = get_mlp_head(
input_size, hidden_size,
1, dropout=dropout
)
def forward(self, obj_embeds, obj_masks=None, **kwargs):
og3d_logits = self.og3d_head(obj_embeds).squeeze(2)
if obj_masks is not None:
og3d_logits = og3d_logits.masked_fill_(obj_masks.logical_not(), -float('inf'))
return og3d_logits
class T5(nn.Module):
def __init__(self, variant='t5-small', input_size=768, use_projection=True, **kwargs):
super().__init__()
self.model = T5ForConditionalGeneration.from_pretrained(variant)
self.model.config.update(kwargs)
hidden_size = self.model.config.d_model
self.use_projection = use_projection
if use_projection:
self.input_proj = nn.Sequential(nn.Linear(input_size, hidden_size), nn.LayerNorm(hidden_size))
else:
assert input_size == hidden_size, "input_feat_size should be equal to hidden_size!"
def forward(self, query_embeds, attention_masks, labels=None):
if self.use_projection:
query_embeds = self.input_proj(query_embeds)
if labels is not None:
outputs = self.model(encoder_outputs=[query_embeds], attention_mask=attention_masks, labels=labels)
outputs = outputs.logits
else:
outputs = self.model.generate(encoder_outputs=BaseModelOutput(last_hidden_state=query_embeds), attention_mask=attention_masks, do_sample=False)
outputs = outputs[:, 1:] # remove the decoder start token for T5 generation output.
return outputs
def calc_pairwise_locs(obj_centers, obj_whls, eps=1e-10, pairwise_rel_type='center', spatial_dist_norm=True,
spatial_dim=5):
if pairwise_rel_type == 'mlp':
obj_locs = torch.cat([obj_centers, obj_whls], 2)
pairwise_locs = torch.cat(
[einops.repeat(obj_locs, 'b l d -> b l x d', x=obj_locs.size(1)),
einops.repeat(obj_locs, 'b l d -> b x l d', x=obj_locs.size(1))],
dim=3
)
return pairwise_locs
pairwise_locs = einops.repeat(obj_centers, 'b l d -> b l 1 d') \
- einops.repeat(obj_centers, 'b l d -> b 1 l d')
pairwise_dists = torch.sqrt(torch.sum(pairwise_locs ** 2, 3) + eps) # (b, l, l)
if spatial_dist_norm:
max_dists = torch.max(pairwise_dists.view(pairwise_dists.size(0), -1), dim=1)[0]
norm_pairwise_dists = pairwise_dists / einops.repeat(max_dists, 'b -> b 1 1')
else:
norm_pairwise_dists = pairwise_dists
if spatial_dim == 1:
return norm_pairwise_dists.unsqueeze(3)
pairwise_dists_2d = torch.sqrt(torch.sum(pairwise_locs[..., :2] ** 2, 3) + eps)
if pairwise_rel_type == 'center':
pairwise_locs = torch.stack(
[norm_pairwise_dists, pairwise_locs[..., 2] / pairwise_dists,
pairwise_dists_2d / pairwise_dists, pairwise_locs[..., 1] / pairwise_dists_2d,
pairwise_locs[..., 0] / pairwise_dists_2d],
dim=3
)
elif pairwise_rel_type == 'vertical_bottom':
bottom_centers = torch.clone(obj_centers)
bottom_centers[:, :, 2] -= obj_whls[:, :, 2]
bottom_pairwise_locs = einops.repeat(bottom_centers, 'b l d -> b l 1 d') \
- einops.repeat(bottom_centers, 'b l d -> b 1 l d')
bottom_pairwise_dists = torch.sqrt(torch.sum(bottom_pairwise_locs ** 2, 3) + eps) # (b, l, l)
bottom_pairwise_dists_2d = torch.sqrt(torch.sum(bottom_pairwise_locs[..., :2] ** 2, 3) + eps)
pairwise_locs = torch.stack(
[norm_pairwise_dists,
bottom_pairwise_locs[..., 2] / bottom_pairwise_dists,
bottom_pairwise_dists_2d / bottom_pairwise_dists,
pairwise_locs[..., 1] / pairwise_dists_2d,
pairwise_locs[..., 0] / pairwise_dists_2d],
dim=3
)
if spatial_dim == 4:
pairwise_locs = pairwise_locs[..., 1:]
return pairwise_locs
class Query3DUnified(torch.nn.Module):
def __init__(self):
super().__init__()
# record parameters
self.memories = ['mv', 'pc', 'voxel', 'prompt']
self.heads = ['ground', 'generation']
self.use_offline_voxel_fts = True
self.use_offline_attn_mask = False
self.inputs = self.memories[:]
self.pairwise_rel_type = 'center'
self.spatial_dim = 5
self.num_heads = 12
self.skip_query_encoder_mask_pred = True
# build prompt type
self.prompt_types = ['txt', 'loc']
# build feature encoder
self.txt_encoder = CLIPLanguageEncoder(use_projection=True, projection_type='mlp', num_projection_layers=1)
self.mv_encoder = ObjectEncoder(input_feat_size=768, hidden_size=768, use_projection=True, dropout=0.1, use_cls_head=False)
self.voxel_encoder = ObjectEncoder(input_feat_size=128,hidden_size=768, use_projection=True, dropout=0.1, use_cls_head=False)
self.pc_encoder = ObjectEncoder(input_feat_size=768, hidden_size=768, dropout=0.1,use_cls_head=False)
# build location encoder
dim_loc = 6
hidden_size = 768
self.dim_loc = dim_loc
self.hidden_size = hidden_size
self.coord_encoder = nn.Sequential(
nn.Linear(3, hidden_size),
nn.LayerNorm(hidden_size),
)
self.box_encoder = nn.Sequential(
nn.Linear(3, hidden_size),
nn.LayerNorm(hidden_size),
)
# build unified encoder
self.unified_encoder = QueryMaskEncoder(hidden_size=768, num_attention_heads=12, num_layers=4, spatial_selfattn=True, memories=self.memories, drop_memories_test=[], memory_dropout=0.6, structure='mixed', use_self_mask=False, num_blocks=1)
# build task head
self.ground_head = GroundHead(hidden_size=384, input_size=768, dropout=0.3)
self.generation_head = T5(variant='t5-small', input_size=768, use_projection=True, max_new_tokens=50)
def prompt_encoder(self, data_dict):
prompt = data_dict['prompt']
prompt_pad_masks = data_dict['prompt_pad_masks']
prompt_type = data_dict['prompt_type']
prompt_feat = torch.zeros(prompt.shape + (self.hidden_size,), device=prompt.device)
for type in self.prompt_types:
# get idx
idx = prompt_type == getattr(PromptType, type.upper())
if idx.sum() == 0:
continue
input = prompt[idx]
mask = prompt_pad_masks[idx]
# encode
if type == 'txt':
encoder = self.txt_encoder
feat = encoder(input.long(), mask)
elif type == 'loc':
loc_prompts = input[:, :self.dim_loc]
if self.dim_loc > 3:
feat = self.coord_encoder(loc_prompts[:, :3]).unsqueeze(1) + self.box_encoder(loc_prompts[:, 3:6]).unsqueeze(1)
mask[:, 1:] = False
else:
raise NotImplementedError(f'{type} is not implemented')
# put back to orignal prompt
prompt_feat[idx] = feat
prompt_pad_masks[idx] = mask
return prompt_feat, prompt_pad_masks.logical_not()
def forward(self, data_dict):
input_dict = {}
# build query
mask = data_dict['query_pad_masks'].logical_not()
query_locs = data_dict['query_locs'][:, :, :self.dim_loc]
if self.dim_loc > 3:
query_pos = self.coord_encoder(query_locs[:, :, :3]) + self.box_encoder(query_locs[:, :, 3:6])
feat = torch.zeros_like(query_pos)
pos = query_pos
input_dict['query'] = (feat, mask, pos)
# encode fts including point, voxel, image, and prompt
# the semantics of the attention mask in pytorch (True as masked) is the opposite as Huggingface Transformers (False as masked)
fts_locs = data_dict['seg_center']
if self.dim_loc > 3:
fts_pos = self.coord_encoder(fts_locs[:, :, :3]) + self.box_encoder(fts_locs[:, :, 3:6])
if self.dim_loc > 3:
fts_pos += self.box_encoder(fts_locs[:, :, 3:6])
for input in self.inputs:
feat, mask, pos = None, None, None
if input == 'prompt':
feat, mask = self.prompt_encoder(data_dict)
elif input == 'mv':
feat = self.mv_encoder(obj_feats = data_dict['mv_seg_fts'])
mask = data_dict['mv_seg_pad_masks'].logical_not()
pos = fts_pos
elif input == 'pc':
feat = self.pc_encoder(obj_feats = data_dict['pc_seg_fts'])
mask = data_dict['pc_seg_pad_masks'].logical_not()
pos = fts_pos
elif input == 'voxel':
feat = self.voxel_encoder(data_dict['voxel_seg_fts'])
mask = data_dict['voxel_seg_pad_masks'].logical_not()
pos = fts_pos
else:
raise NotImplementedError(f"Unknow input type: {input}")
input_dict[input] = [feat, mask, pos]
# build offline attention mask for guided mask training
if self.use_offline_attn_mask:
offline_attn_masks = data_dict['offline_attn_mask']
else:
offline_attn_masks = None
mask_head_partial = None
# generate features for spatial attention
if self.unified_encoder.spatial_selfattn:
pairwise_locs = calc_pairwise_locs(query_locs[:, :, :3], None,
pairwise_rel_type=self.pairwise_rel_type, spatial_dist_norm=True,
spatial_dim=self.spatial_dim)
else:
pairwise_locs = None
# unified encoding
query, predictions_class, predictions_mask = self.unified_encoder(input_dict, pairwise_locs, mask_head_partial)
# task head
for head in self.heads:
if head == 'ground':
inputs = [query, data_dict['query_pad_masks']]
logits = getattr(self, head + '_head')(*inputs)
data_dict[head + '_logits'] = logits
data_dict['og3d_logits'] = logits
elif head == 'generation':
inputs = [query, data_dict['query_pad_masks']] + [None]
logits = getattr(self, head + '_head')(*inputs)
data_dict[head + '_logits'] = logits
else:
raise NotImplementedError(f"Unknow head type: {head}")
return data_dict |