| import torch.nn as nn | |
| from networks.layers.transformer import DualBranchGPM | |
| from networks.models.aot import AOT | |
| from networks.decoders import build_decoder | |
| class DeAOT(AOT): | |
| def __init__(self, cfg, encoder='mobilenetv2', decoder='fpn'): | |
| super().__init__(cfg, encoder, decoder) | |
| self.LSTT = DualBranchGPM( | |
| cfg.MODEL_LSTT_NUM, | |
| cfg.MODEL_ENCODER_EMBEDDING_DIM, | |
| cfg.MODEL_SELF_HEADS, | |
| cfg.MODEL_ATT_HEADS, | |
| emb_dropout=cfg.TRAIN_LSTT_EMB_DROPOUT, | |
| droppath=cfg.TRAIN_LSTT_DROPPATH, | |
| lt_dropout=cfg.TRAIN_LSTT_LT_DROPOUT, | |
| st_dropout=cfg.TRAIN_LSTT_ST_DROPOUT, | |
| droppath_lst=cfg.TRAIN_LSTT_DROPPATH_LST, | |
| droppath_scaling=cfg.TRAIN_LSTT_DROPPATH_SCALING, | |
| intermediate_norm=cfg.MODEL_DECODER_INTERMEDIATE_LSTT, | |
| return_intermediate=True) | |
| decoder_indim = cfg.MODEL_ENCODER_EMBEDDING_DIM * \ | |
| (cfg.MODEL_LSTT_NUM * 2 + | |
| 1) if cfg.MODEL_DECODER_INTERMEDIATE_LSTT else cfg.MODEL_ENCODER_EMBEDDING_DIM * 2 | |
| self.decoder = build_decoder( | |
| decoder, | |
| in_dim=decoder_indim, | |
| out_dim=cfg.MODEL_MAX_OBJ_NUM + 1, | |
| decode_intermediate_input=cfg.MODEL_DECODER_INTERMEDIATE_LSTT, | |
| hidden_dim=cfg.MODEL_ENCODER_EMBEDDING_DIM, | |
| shortcut_dims=cfg.MODEL_ENCODER_DIM, | |
| align_corners=cfg.MODEL_ALIGN_CORNERS) | |
| self.id_norm = nn.LayerNorm(cfg.MODEL_ENCODER_EMBEDDING_DIM) | |
| self._init_weight() | |
| def decode_id_logits(self, lstt_emb, shortcuts): | |
| n, c, h, w = shortcuts[-1].size() | |
| decoder_inputs = [shortcuts[-1]] | |
| for emb in lstt_emb: | |
| decoder_inputs.append(emb.view(h, w, n, -1).permute(2, 3, 0, 1)) | |
| pred_logit = self.decoder(decoder_inputs, shortcuts) | |
| return pred_logit | |
| def get_id_emb(self, x): | |
| id_emb = self.patch_wise_id_bank(x) | |
| id_emb = self.id_norm(id_emb.permute(2, 3, 0, 1)).permute(2, 3, 0, 1) | |
| id_emb = self.id_dropout(id_emb) | |
| return id_emb | |