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)