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from modules.layers.transformers import (TransformerDecoderLayer,
TransformerEncoderLayer,
TransformerSpatialDecoderLayer)
class UnifiedSpatialCrossEncoderV2(nn.Module):
"""
spatial_dim: spatial feature dim, used to modify attention
dim_loc:
"""
def __init__(self, cfg, hidden_size=768, 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)
# loc layer
loc_layer = nn.Sequential(
nn.Linear(dim_loc, hidden_size),
nn.LayerNorm(hidden_size),
)
self.loc_layers = layer_repeat(loc_layer, 1)
# token embedding
self.token_type_embeddings = nn.Embedding(2, hidden_size)
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
):
txt_len = txt_embeds.shape[1]
obj_len = obj_embeds.shape[1]
for i, unified_layer in enumerate(self.unified_encoder):
# add embeddings for points
query_pos = self.loc_layers[0](obj_locs)
pc_token_type_ids = torch.ones((obj_embeds.shape[0:2])).long().cuda()
pc_type_embeds = self.token_type_embeddings(pc_token_type_ids)
obj_embeds = obj_embeds + query_pos + pc_type_embeds
# add embeddings for languages
lang_token_type_ids = torch.zeros((txt_embeds.shape[0:2])).long().cuda()
lang_type_embeds = self.token_type_embeddings(lang_token_type_ids)
txt_embeds = txt_embeds + lang_type_embeds
# fuse embeddings
joint_embeds = torch.cat((txt_embeds, obj_embeds), dim=1)
joint_masks = torch.cat((txt_masks, obj_masks), dim=1)
# transformer
joint_embeds, self_attn_matrices = unified_layer(joint_embeds,
tgt_key_padding_mask=joint_masks.logical_not())
# split
txt_embeds, obj_embeds = torch.split(joint_embeds, [txt_len, obj_len], dim=1)
return txt_embeds, obj_embeds |