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import torch
import torch.nn as nn
from modules.build import GROUNDING_REGISTRY
from modules.layers.transformers import (TransformerDecoderLayer,
TransformerEncoderLayer,
TransformerSpatialDecoderLayer)
from modules.utils import layer_repeat, calc_pairwise_locs
from modules.weights import _init_weights_bert
@GROUNDING_REGISTRY.register()
class EntitySpatialCrossEncoder(nn.Module):
"""
spatial_dim: spatial feature dim, used to modify attention
dim_loc:
"""
def __init__(self, cfg, hidden_size=768, num_attention_heads=12, spatial_dim=5, num_layers=4, dim_loc=6,
pairwise_rel_type='center'):
super().__init__()
decoder_layer = TransformerSpatialDecoderLayer(hidden_size, num_attention_heads, dim_feedforward=2048,
dropout=0.1, activation='gelu',
spatial_dim=spatial_dim, spatial_multihead=True,
spatial_attn_fusion='cond')
self.layers = layer_repeat(decoder_layer, num_layers)
loc_layer = nn.Sequential(
nn.Linear(dim_loc, hidden_size),
nn.LayerNorm(hidden_size),
)
self.loc_layers = layer_repeat(loc_layer, 1)
self.pairwise_rel_type = pairwise_rel_type
self.spatial_dim = spatial_dim
self.spatial_dist_norm = True
self.apply(_init_weights_bert)
def forward(
self, txt_embeds, txt_masks, obj_embeds, obj_locs, obj_masks,
output_attentions=False, output_hidden_states=False, **kwargs
):
pairwise_locs = calc_pairwise_locs(
obj_locs[:, :, :3], obj_locs[:, :, 3:],
pairwise_rel_type=self.pairwise_rel_type
)
out_embeds = obj_embeds
for i, layer in enumerate(self.layers):
query_pos = self.loc_layers[0](obj_locs)
out_embeds = out_embeds + query_pos
out_embeds, self_attn_matrices, cross_attn_matrices = layer(
out_embeds, txt_embeds, pairwise_locs,
tgt_key_padding_mask=obj_masks.logical_not(),
memory_key_padding_mask=txt_masks.logical_not(),
)
return txt_embeds, out_embeds
@GROUNDING_REGISTRY.register()
class UnifiedSpatialCrossEncoderV1(nn.Module):
"""
spatial_dim: spatial feature dim, used to modify attention
dim_loc:
"""
def __init__(self, cfg, hidden_size=768, num_attention_heads=12, spatial_dim=5, num_layers=4, dim_loc=6,
pairwise_rel_type='center'):
super().__init__()
pc_encoder_layer = TransformerSpatialDecoderLayer(hidden_size, num_attention_heads, dim_feedforward=2048,
dropout=0.1, activation='gelu',
spatial_dim=spatial_dim, spatial_multihead=True,
spatial_attn_fusion='cond')
lang_encoder_layer = TransformerDecoderLayer(hidden_size, num_attention_heads)
self.pc_encoder = layer_repeat(pc_encoder_layer, num_layers)
self.lang_encoder = layer_repeat(lang_encoder_layer, num_layers)
loc_layer = nn.Sequential(
nn.Linear(dim_loc, hidden_size),
nn.LayerNorm(hidden_size),
)
self.loc_layers = layer_repeat(loc_layer, 1)
self.pairwise_rel_type = pairwise_rel_type
self.spatial_dim = spatial_dim
self.spatial_dist_norm = True
self.apply(_init_weights_bert)
def forward(
self, txt_embeds, txt_masks, obj_embeds, obj_locs, obj_masks,
output_attentions=False, output_hidden_states=False, **kwargs
):
pairwise_locs = calc_pairwise_locs(
obj_locs[:, :, :3], obj_locs[:, :, 3:],
pairwise_rel_type=self.pairwise_rel_type
)
for i, (pc_layer, lang_layer) in enumerate(zip(self.pc_encoder, self.lang_encoder)):
query_pos = self.loc_layers[0](obj_locs)
obj_embeds = obj_embeds + query_pos
obj_embeds_out, self_attn_matrices, cross_attn_matrices = pc_layer(
obj_embeds, txt_embeds, pairwise_locs,
tgt_key_padding_mask=obj_masks.logical_not(),
memory_key_padding_mask=txt_masks.logical_not(),
)
txt_embeds_out, self_attn_matrices, cross_attn_matrices = lang_layer(
txt_embeds, obj_embeds,
tgt_key_padding_mask=txt_masks.logical_not(),
memory_key_padding_mask=obj_masks.logical_not(),
)
obj_embeds = obj_embeds_out
txt_embeds = txt_embeds_out
return txt_embeds, obj_embeds
@GROUNDING_REGISTRY.register()
class UnifiedSpatialCrossEncoderV2(nn.Module):
"""
spatial_dim: spatial feature dim, used to modify attention
dim_loc:
"""
def __init__(self, cfg, hidden_size=512, dim_feedforward=2048, num_attention_heads=12, num_layers=4, dim_loc=6):
super().__init__()
# unfied encoder
unified_encoder_layer = TransformerEncoderLayer(hidden_size, num_attention_heads, dim_feedforward=dim_feedforward)
self.unified_encoder = layer_repeat(unified_encoder_layer, num_layers)
# token embedding
self.token_type_embeddings = nn.Embedding(2,1024)
self.pm_linear = nn.Linear(768, 1024)
self.apply(_init_weights_bert)
def forward(
self, txt_embeds, txt_masks, obj_embeds,
output_attentions=False, output_hidden_states=False, **kwargs
):
txt_len = txt_embeds.shape[1]
obj_len = obj_embeds.shape[1]
obj_embeds = self.pm_linear(obj_embeds)
# dummy mask for objects (all valid)
obj_masks = torch.ones((obj_embeds.shape[0], obj_len), dtype=torch.bool, device=txt_embeds.device)
for i, unified_layer in enumerate(self.unified_encoder):
# ----- Object embeddings -----
# Only add token type embedding (no spatial loc)
pc_token_type_ids = torch.ones_like(obj_masks, dtype=torch.long)
pc_type_embeds = self.token_type_embeddings(pc_token_type_ids)
obj_embeds = obj_embeds + pc_type_embeds
# ----- Text embeddings -----
lang_token_type_ids = torch.zeros_like(txt_masks, dtype=torch.long)
lang_type_embeds = self.token_type_embeddings(lang_token_type_ids)
txt_embeds = txt_embeds + lang_type_embeds
# ----- Fuse modalities -----
joint_embeds = torch.cat((txt_embeds, obj_embeds), dim=1)
joint_masks = torch.cat((txt_masks, obj_masks), dim=1)
# ----- Transformer layer -----
joint_embeds, self_attn_matrices = unified_layer(
joint_embeds,
tgt_key_padding_mask=joint_masks
)
# ----- Split back -----
txt_embeds, obj_embeds = torch.split(joint_embeds, [txt_len, obj_len], dim=1)
return txt_embeds, obj_embeds
if __name__ == '__main__':
x = UnifiedSpatialCrossEncoderV2().cuda()
txt_embeds = torch.zeros((3, 10, 768)).cuda()
txt_masks = torch.ones((3, 10)).cuda()
obj_embeds = torch.zeros((3, 10, 768)).cuda()
obj_locs = torch.ones((3, 10, 6)).cuda()
obj_masks = torch.ones((3, 10)).cuda()
x(txt_embeds, txt_masks, obj_embeds, obj_locs, obj_masks)
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