update track loss for video finetune

projects/GLEE/glee/models/glee_model.py

@@ -618,3 +618,92 @@ def get_template(self, imgs, pad_masks, prompt_mode='scribble'):
 
         return ref_feats, ref_masks
 
+
+
+
+    def get_tracking_contrastive_lossv3(self, video_outputs, video_targets, task):  # IDOL track loss
+        if task in self.no_mask_tasks:
+            indices_all = self.matcher(video_outputs, video_targets, 'task', cost=["cls", "box"])
+        else:
+            indices_all = self.matcher(video_outputs, video_targets, 'task' )
+
+        video_len = self.video_info['len']
+        track_loss = 0
+        num_inst = 0
+
+        batch_similarity = []
+        batch_label = []
+        for i in range(self.video_info['bz']): # 每个batch 切片操作
+            indices = indices_all[i*video_len:(i+1)*video_len]
+            bz_embedding = video_outputs['pred_track_embed'][i*video_len:(i+1)*video_len]
+            bz_target = video_targets[i*video_len:(i+1)*video_len]
+            zero = torch.tensor(0).to(bz_embedding.device)
+            one = torch.tensor(1).to(bz_embedding.device)
+            video_contras = {}
+            memory = {}
+            for f,(findice,fembed,ftarget) in enumerate(zip(indices,bz_embedding,bz_target)):
+                vf_embed_k = fembed[findice[0]]
+                if len(vf_embed_k.shape) ==1:
+                    vf_embed_k.unsqueeze(0)
+                vf_gt_id_k = ftarget['inst_id'][findice[1]]
+
+
+                # neg sample
+                sampled_index = set(random.sample(range(300),20)) 
+                neg_index = sampled_index - set(findice[0].tolist())
+                neg_index = list(neg_index)
+                vf_embed_neg = fembed[neg_index]
+                vf_embed = torch.cat([vf_embed_k,vf_embed_neg],dim=0)
+                vf_gt_id = torch.cat([vf_gt_id_k,zero.repeat(len(neg_index))-2],dim=0) 
+
+                video_contras[f] = (vf_embed,vf_gt_id)
+
+                if f > 0:
+                    num_inst = num_inst + len(ftarget['inst_id'])
+                    similarity_matric =  torch.einsum("ac,bc->ab", video_contras[f-1][0], vf_embed_k)  #[num_1, num_gt]
+
+                    v0_gt_id_m = video_contras[f-1][1].unsqueeze(-1).repeat(1,len(vf_gt_id_k))
+                    v1_gt_id_m = vf_gt_id_k.unsqueeze(0).repeat(len(video_contras[f-1][1]),1)
+                    similarity_label = (v0_gt_id_m == v1_gt_id_m).float()  # can be treat as one hot label 
+                    # use focal loss instand of contrastive
+                    # aux  cosine
+                    # aux_contrastive_embed=nn.functional.normalize(video_contras[f-1][0].float(),dim=1)
+                    # key_embed_i=nn.functional.normalize(vf_embed_k.float(),dim=1)    
+                    # cosine = torch.einsum('nc,kc->nk',[aux_contrastive_embed,key_embed_i])
+
+                    # batch_similarity_aux.append(cosine.flatten() )
+                    batch_similarity.append(similarity_matric.flatten() )
+                    batch_label.append(similarity_label.flatten() )
+        if len(batch_similarity)==0 or torch.cat(batch_similarity).shape[0] == 0:
+            track_loss = (video_outputs['pred_track_embed']*0).sum()
+        else:
+            contras_loss = 0
+            aux_loss = 0
+            for pred, label in zip(batch_similarity, batch_label):
+                if len(pred) == 0:
+                    continue
+                pred = pred.unsqueeze(0)
+                label = label.unsqueeze(0)
+                # aux_pred = aux_pred.unsqueeze(0)
+
+                pos_inds = (label == 1)
+                neg_inds = (label == 0)
+                pred_pos = pred * pos_inds.float()
+                pred_neg = pred * neg_inds.float()
+                # use -inf to mask out unwanted elements.
+                pred_pos[neg_inds] = pred_pos[neg_inds] + float('inf')
+                pred_neg[pos_inds] = pred_neg[pos_inds] + float('-inf')
+                _pos_expand = torch.repeat_interleave(pred_pos, pred.shape[1], dim=1)
+                _neg_expand = pred_neg.repeat(1, pred.shape[1])
+                # [bz,N], N is all pos and negative samples on reference frame, label indicate it's pos or negative
+                x = torch.nn.functional.pad((_neg_expand - _pos_expand), (0, 1), "constant", 0) 
+                contras_loss += torch.logsumexp(x, dim=1)
+
+
+            # track_loss = (contras_loss + 1.5*aux_loss)
+            track_loss = contras_loss/max(num_inst,1)
+
+        track_loss = track_loss #  /(self.video_info['bz'])
+        return track_loss
+
+