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| import torch.nn as nn | |
| from networks.encoders import build_encoder | |
| from networks.layers.transformer import LongShortTermTransformer | |
| from networks.decoders import build_decoder | |
| from networks.layers.position import PositionEmbeddingSine | |
| class AOT(nn.Module): | |
| def __init__(self, cfg, encoder='mobilenetv2', decoder='fpn'): | |
| super().__init__() | |
| self.cfg = cfg | |
| self.max_obj_num = cfg.MODEL_MAX_OBJ_NUM | |
| self.epsilon = cfg.MODEL_EPSILON | |
| self.encoder = build_encoder(encoder, | |
| frozen_bn=cfg.MODEL_FREEZE_BN, | |
| freeze_at=cfg.TRAIN_ENCODER_FREEZE_AT) | |
| self.encoder_projector = nn.Conv2d(cfg.MODEL_ENCODER_DIM[-1], | |
| cfg.MODEL_ENCODER_EMBEDDING_DIM, | |
| kernel_size=1) | |
| self.LSTT = LongShortTermTransformer( | |
| 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 + | |
| 1) if cfg.MODEL_DECODER_INTERMEDIATE_LSTT else cfg.MODEL_ENCODER_EMBEDDING_DIM | |
| 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) | |
| if cfg.MODEL_ALIGN_CORNERS: | |
| self.patch_wise_id_bank = nn.Conv2d( | |
| cfg.MODEL_MAX_OBJ_NUM + 1, | |
| cfg.MODEL_ENCODER_EMBEDDING_DIM, | |
| kernel_size=17, | |
| stride=16, | |
| padding=8) | |
| else: | |
| self.patch_wise_id_bank = nn.Conv2d( | |
| cfg.MODEL_MAX_OBJ_NUM + 1, | |
| cfg.MODEL_ENCODER_EMBEDDING_DIM, | |
| kernel_size=16, | |
| stride=16, | |
| padding=0) | |
| self.id_dropout = nn.Dropout(cfg.TRAIN_LSTT_ID_DROPOUT, True) | |
| self.pos_generator = PositionEmbeddingSine( | |
| cfg.MODEL_ENCODER_EMBEDDING_DIM // 2, normalize=True) | |
| self._init_weight() | |
| def get_pos_emb(self, x): | |
| pos_emb = self.pos_generator(x) | |
| return pos_emb | |
| def get_id_emb(self, x): | |
| id_emb = self.patch_wise_id_bank(x) | |
| id_emb = self.id_dropout(id_emb) | |
| return id_emb | |
| def encode_image(self, img): | |
| xs = self.encoder(img) | |
| xs[-1] = self.encoder_projector(xs[-1]) | |
| return xs | |
| 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, c).permute(2, 3, 0, 1)) | |
| pred_logit = self.decoder(decoder_inputs, shortcuts) | |
| return pred_logit | |
| def LSTT_forward(self, | |
| curr_embs, | |
| long_term_memories, | |
| short_term_memories, | |
| curr_id_emb=None, | |
| pos_emb=None, | |
| size_2d=(30, 30)): | |
| n, c, h, w = curr_embs[-1].size() | |
| curr_emb = curr_embs[-1].view(n, c, h * w).permute(2, 0, 1) | |
| lstt_embs, lstt_memories = self.LSTT(curr_emb, long_term_memories, | |
| short_term_memories, curr_id_emb, | |
| pos_emb, size_2d) | |
| lstt_curr_memories, lstt_long_memories, lstt_short_memories = zip( | |
| *lstt_memories) | |
| return lstt_embs, lstt_curr_memories, lstt_long_memories, lstt_short_memories | |
| def _init_weight(self): | |
| nn.init.xavier_uniform_(self.encoder_projector.weight) | |
| nn.init.orthogonal_( | |
| self.patch_wise_id_bank.weight.view( | |
| self.cfg.MODEL_ENCODER_EMBEDDING_DIM, -1).permute(0, 1), | |
| gain=17**-2 if self.cfg.MODEL_ALIGN_CORNERS else 16**-2) | |