{"behavior":"degenerate_repetition","label_filter":null,"limit":10,"next_offset":76611,"offset":76601,"previous_offset":null,"rows":[{"assistant_initiates":false,"asst_text":"\u9519\u8bef\u4fe1\u606f\u8868\u660e\u5728\u6267\u884c`torch.cat([attn1, attn2],dim=1)`\u65f6\u9047\u5230\u4e86\u95ee\u9898\uff0c\u5177\u4f53\u662f\u56e0\u4e3a\u8981\u62fc\u63a5\u7684\u4e24\u4e2a\u5f20\u91cf\u5728\u9664\u4e86\u7b2c\u4e8c\u4e2a\u7ef4\u5ea6\uff08`dim=1`\uff0c\u5373\u901a\u9053\u7ef4\u5ea6\uff09\u4e4b\u5916\u7684\u5176\u4ed6\u7ef4\u5ea6\u5927\u5c0f\u4e0d\u5339\u914d\u3002\u6b64\u64cd\u4f5c\u5c1d\u8bd5\u5728\u901a\u9053\u7ef4\u5ea6\u4e0a\u62fc\u63a5`attn1`\u548c`attn2`\uff0c\u4f46\u5b83\u4eec\u5728\u5176\u4ed6\u7ef4\u5ea6\u4e0a\u7684\u5927\u5c0f\u4e0d\u4e00\u81f4\uff0c\u5bfc\u81f4\u65e0\u6cd5\u8fdb\u884c\u62fc\u63a5\u64cd\u4f5c\u3002\n\n### \u89e3\u51b3\u65b9\u6cd5\n\n1. **\u6838\u5b9e\u5f20\u91cf\u7ef4\u5ea6**\uff1a\u9996\u5148\u9700\u8981\u786e\u8ba4`attn1`\u548c`attn2`\u5f20\u91cf\u7684\u5177\u4f53\u5927\u5c0f\uff0c\u786e\u4fdd\u9664\u4e86\u901a\u9053\u7ef4\u5ea6\uff08\u901a\u5e38\u662f`dim=1`\uff09\u5916\uff0c\u5176\u4ed6\u7ef4\u5ea6\u7684\u5927\u5c0f\u5b8c\u5168\u5339\u914d\u3002\u53ef\u4ee5\u901a\u8fc7\u6253\u5370`attn1.shape`\u548c`attn2.shape`\u6765\u68c0\u67e5\u8fd9\u4e00\u70b9\u3002\n\n2. **\u8c03\u6574\u5f20\u91cf\u5927\u5c0f**\uff1a\u5982\u679c\u53d1\u73b0`attn1`\u548c`attn2`\u5728\u67d0\u4e9b\u7ef4\u5ea6\u4e0a\u5927\u5c0f\u4e0d\u4e00\u81f4\uff0c\u9700\u8981\u6839\u636e\u5177\u4f53\u9700\u8981\u8c03\u6574\u5b83\u4eec\u7684\u5927\u5c0f\uff0c\u4ee5\u786e\u4fdd\u80fd\u5728\u6307\u5b9a\u7684\u7ef4\u5ea6\u4e0a\u6b63\u786e\u62fc\u63a5\u3002\u8fd9\u53ef\u80fd\u6d89\u53ca\u5230\u8c03\u6574\u5f62\u72b6\u3001\u88c1\u526a\u6216\u586b\u5145\u7b49\u64cd\u4f5c\u3002\u6839\u636e\u6240\u7ed9\u4ee3\u7801\u548c\u9519\u8bef\u4fe1\u606f\uff0c\u5e76\u4e0d\u80fd\u76f4\u63a5\u786e\u5b9a\u5e94\u8be5\u5982\u4f55\u4fee\u6539\u8fd9\u4e9b\u5f20\u91cf\uff0c\u56e0\u4e3a\u5177\u4f53\u7684\u4fee\u6539\u53d6\u51b3\u4e8e\u60a8\u7684\u6a21\u578b\u67b6\u6784\u548c\u6570\u636e\u5904\u7406\u6d41\u7a0b\u3002\n\n3. **\u68c0\u67e5\u62fc\u63a5\u903b\u8f91**\uff1a\u518d\u6b21\u786e\u8ba4\u62fc\u63a5\u7684\u903b\u8f91\u662f\u5426\u6b63\u786e\u3002\u4ece\u9519\u8bef\u539f\u56e0\u6765\u770b\uff0c\u95ee\u9898\u51fa\u5728\u4e24\u4e2a\u5f20\u91cf\u7684\u975e\u901a\u9053\u7ef4\u5ea6\u4e0a\u7684\u5927\u5c0f\u4e0d\u5339\u914d\u3002\u8fd9\u53ef\u80fd\u662f\u56e0\u4e3a\u5728\u5904\u7406\u8fc7\u7a0b\uff08\u88c1\u526a\u3001\u91c7\u6837\u7b49\u64cd\u4f5c\uff09\u4e2d\u5bf9\u5f20\u91cf\u8fdb\u884c\u4e86\u4fee\u6539\uff0c\u9020\u6210\u4e86\u7ef4\u5ea6\u4e0d\u5339\u914d\u7684\u60c5\u51b5\u3002\u786e\u4fdd\u6240\u6709\u4e0e`attn1`\u548c`attn2`\u76f8\u5173\u7684\u64cd\u4f5c\u4e0d\u4f1a\u7834\u574f\u5b83\u4eec\u7684\u5c3a\u5bf8\u4e00\u81f4\u6027\u3002\n\n4. **\u91c7\u53d6\u6761\u4ef6\u5206\u652f**\uff1a\u5982\u679c`attn1`\u548c`attn2`\u7684\u5927\u5c0f\u4e0d\u5339\u914d\u662f\u56e0\u4e3a\u7279\u5b9a\u6761\u4ef6\u4e0b\u7684\u67d0\u4e9b\u64cd\u4f5c\uff0c\u60a8\u53ef\u4ee5\u6839\u636e\u6761\u4ef6\u91c7\u53d6\u4e0d\u540c\u7684\u903b\u8f91\u5206\u652f\uff0c\u6216\u5728\u62fc\u63a5\u524d\u5bf9\u5f20\u91cf\u8fdb\u884c\u5fc5\u8981\u7684\u8c03\u6574\u3002\n\n### \u793a\u4f8b\u4fee\u590d\u4ee3\u7801\n\n\u8fd9\u91cc\u6ca1\u6709\u5177\u4f53\u7684\u4fee\u590d\u4ee3\u7801\uff0c\u56e0\u4e3a\u9700\u8981\u57fa\u4e8e\u60a8\u7684\u6a21\u578b\u7ed3\u6784\u548c\u6570\u636e\u5904\u7406\u6d41\u7a0b\u6765\u786e\u5b9a\u5982\u4f55\u8c03\u6574\u3002\u4f46\u662f\uff0c\u60a8\u53ef\u4ee5\u53c2\u8003\u4ee5\u4e0b\u4ee3\u7801\u7ed3\u6784\u6765\u68c0\u67e5\u548c\u8c03\u6574\uff1a\n\n```python\nprint(\"attn1 shape: \", attn1.shape)\nprint(\"attn2 shape: \", attn2.shape)\n\n# \u5047\u8bbe\u8c03\u6574attn1\u548cattn2\u7684\u5927\u5c0f\u4f7f\u5176\u53ef\u4ee5\u62fc\u63a5\n# \u8fd9\u91cc\u7684\u8c03\u6574\u64cd\u4f5c\u4f9d\u8d56\u4e8e\u5b9e\u9645\u60c5\u51b5\n# \u4f8b\u5982\uff0c\u5982\u679c\u662f\u9700\u8981\u88c1\u526a\uff0c\u90a3\u4e48\u53ef\u4ee5\u4f7f\u7528attn1 = attn1[:, :, :min_size, :min_size]\n# \u5982\u679c\u9700\u8981\u586b\u5145\uff0c\u53ef\u4ee5\u4f7f\u7528F.pad\n# attn1 = torch.nn.functional.pad(attn1, (0, 0, 0, delta_h, 0, delta_w), 'constant', 0)\n\n# \u6210\u529f\u8c03\u6574\u540e\u518d\u6b21\u62fc\u63a5\nattn = torch.cat([attn1, attn2], dim=1)\n```\n\n\u8bf7\u8bb0\u4f4f\uff0c\u4e0a\u8ff0\u4ee3\u7801\u53ea\u662f\u8bf4\u660e\u4e86\u5904\u7406\u7684\u65b9\u5411\uff0c\u5177\u4f53\u64cd\u4f5c\u9700\u8981\u60a8\u6839\u636e\u5b9e\u9645\u60c5\u51b5\u6765\u51b3\u5b9a\u3002","behavior":"degenerate_repetition","conversation_hash":"979356f838792a17998dbf31df3eb938","conversation_redacted":false,"conversation_toxic":false,"country":"Hong Kong","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4-0125-preview","n_chars":13086,"n_turns_in_conversation":16,"n_words":979,"source_row_offset":76601,"source_shard":"turns-00020.parquet","timestamp":"Sun, 28 Apr 2024 12:51:46 GMT","turn_id":"f1360860a145e2bf4cc18cc9","turn_index":11,"user_text":"\u5728from . import BaseActor\nfrom lib.utils.misc import NestedTensor\nfrom lib.utils.box_ops import box_cxcywh_to_xyxy, box_xywh_to_xyxy\nimport torch\nfrom lib.utils.merge import merge_template_search\nfrom ...utils.heapmap_utils import generate_heatmap\nfrom ...utils.ce_utils import generate_mask_cond, adjust_keep_rate\n\n\nclass CEUTrackActor(BaseActor):\n \"\"\" Actor for training CEUTrack models \"\"\"\n\n def __init__(self, net, objective, loss_weight, settings, cfg=None):\n super().__init__(net, objective)\n self.loss_weight = loss_weight\n self.settings = settings\n self.bs = self.settings.batchsize # batch size\n self.cfg = cfg\n\n def __call__(self, data):\n \"\"\"\n args:\n data - The input data, should contain the fields 'template', 'search', 'gt_bbox'.\n template_images: (N_t, batch, 3, H, W)\n search_images: (N_s, batch, 3, H, W)\n returns:\n loss - the training loss\n status - dict containing detailed losses\n \"\"\"\n # forward pass\n out_dict = self.forward_pass(data)\n\n # compute losses\n loss, status = self.compute_losses(out_dict, data)\n\n return loss, status\n\n def forward_pass(self, data):\n # currently only support 1 template and 1 search region\n assert len(data['template_images']) == 1\n assert len(data['search_images']) == 1\n assert len(data['template_event']) == 1\n assert len(data['search_event']) == 1\n\n template_list = []\n for i in range(self.settings.num_template):\n template_img_i = data['template_images'][i].view(-1,\n *data['template_images'].shape[2:]) # (batch, 3, 128, 128)\n # template_att_i = data['template_att'][i].view(-1, *data['template_att'].shape[2:]) # (batch, 128, 128)\n template_list.append(template_img_i)\n\n search_img = data['search_images'][0].view(-1, *data['search_images'].shape[2:]) # (batch, 3, 320, 320)\n # search_att = data['search_att'][0].view(-1, *data['search_att'].shape[2:]) # (batch, 320, 320)\n\n template_event = data['template_event'][0].view(-1, *data['template_event'].shape[2:])\n search_event = data['search_event'][0].view(-1, *data['search_event'].shape[2:])\n\n box_mask_z = None\n ce_keep_rate = None\n if self.cfg.MODEL.BACKBONE.CE_LOC:\n box_mask_z = generate_mask_cond(self.cfg, template_list[0].shape[0], template_list[0].device,\n data['template_anno'][0])\n\n ce_start_epoch = self.cfg.TRAIN.CE_START_EPOCH\n ce_warm_epoch = self.cfg.TRAIN.CE_WARM_EPOCH\n ce_keep_rate = adjust_keep_rate(data['epoch'], warmup_epochs=ce_start_epoch,\n total_epochs=ce_start_epoch + ce_warm_epoch,\n ITERS_PER_EPOCH=1,\n base_keep_rate=self.cfg.MODEL.BACKBONE.CE_KEEP_RATIO[0])\n\n if len(template_list) == 1:\n template_list = template_list[0]\n\n out_dict = self.net(template=template_list,\n search=search_img,\n event_template=template_event,\n event_search=search_event,\n ce_template_mask=box_mask_z,\n ce_keep_rate=ce_keep_rate,\n return_last_attn=False)\n\n return out_dict\n\n def compute_losses(self, pred_dict, gt_dict, return_status=True):\n # gt gaussian map\n gt_bbox = gt_dict['search_anno'][-1] # (Ns, batch, 4) (x1,y1,w,h) -> (batch, 4)\n gt_gaussian_maps = generate_heatmap(gt_dict['search_anno'], self.cfg.DATA.SEARCH.SIZE, self.cfg.MODEL.BACKBONE.STRIDE)\n gt_gaussian_maps = gt_gaussian_maps[-1].unsqueeze(1)\n\n # Get boxes\n pred_boxes = pred_dict['pred_boxes']\n if torch.isnan(pred_boxes).any():\n raise ValueError(\"Network outputs is NAN! Stop Training\")\n num_queries = pred_boxes.size(1)\n pred_boxes_vec = box_cxcywh_to_xyxy(pred_boxes).view(-1, 4) # (B,N,4) --> (BN,4) (x1,y1,x2,y2)\n gt_boxes_vec = box_xywh_to_xyxy(gt_bbox)[:, None, :].repeat((1, num_queries, 1)).view(-1, 4).clamp(min=0.0,\n max=1.0) # (B,4) --> (B,1,4) --> (B,N,4)\n # compute giou and iou\n try:\n giou_loss, iou = self.objective['giou'](pred_boxes_vec, gt_boxes_vec) # (BN,4) (BN,4)\n except:\n giou_loss, iou = torch.tensor(0.0).cuda(), torch.tensor(0.0).cuda()\n # compute l1 loss\n l1_loss = self.objective['l1'](pred_boxes_vec, gt_boxes_vec) # (BN,4) (BN,4)\n # compute location loss\n if 'score_map' in pred_dict:\n location_loss = self.objective['focal'](pred_dict['score_map'], gt_gaussian_maps)\n else:\n location_loss = torch.tensor(0.0, device=l1_loss.device)\n rank_loss = self.loss_rank(pred_dict,gt_dict['search_anno'], gt_dict['template_anno'])\n rank_loss_event = self.loss_rank_event(pred_dict,gt_dict['search_anno'], gt_dict['template_anno'])\n rank = rank_loss + rank_loss_event\n # weighted sum\n loss = self.loss_weight['giou'] * giou_loss + self.loss_weight['l1'] * l1_loss + self.loss_weight['focal'] * location_loss + rank*1.2\n\n if return_status:\n # status for log\n mean_iou = iou.detach().mean()\n status = {\"Loss/total\": loss.item(),\n \"Loss/giou\": giou_loss.item(),\n \"Loss/l1\": l1_loss.item(),\n \"Loss/location\": location_loss.item(),\n 'Loss/rank': rank.item(),\n \"IoU\": mean_iou.item()}\n return loss, status\n else:\n return loss\n\n def _random_permute(self,matrix):\n # matrix = random.choice(matrix)\n b, c, h, w = matrix.shape\n idx = [ torch.randperm(c).to(matrix.device) for i in range(b)]\n idx = torch.stack(idx, dim=0)[:, :, None, None].repeat([1,1,h,w])\n # idx = torch.randperm(c)[None,:,None,None].repeat([b,1,h,w]).to(matrix.device)\n matrix01 = torch.gather(matrix, 1, idx)\n return matrix01\n def crop_flag(self, flag, global_index_s, global_index_t,H1 = 64, H2 = 256):\n B,Ls = global_index_s.shape\n B, Lt = global_index_t.shape\n B,C,L1,L2 = flag.shape\n flag_t = flag[:,:,:H1,:]\n flag_s = flag[:,:,H1:,:]\n\n flag_t = torch.gather(flag_t,2,global_index_t[:,None,:,None].repeat([1,C,1,L2]).long())\n flag_s = torch.gather(flag_s,2,global_index_s[:,None,:,None].repeat([1,C,1,L2]).long())\n flag = torch.cat([flag_t, flag_s], dim = 2)\n\n flag_t = flag[:,:,:,:H1]\n flag_s = flag[:,:,:,H1:]\n flag_t = torch.gather(flag_t,3,global_index_t[:,None,None,:].repeat([1,C,int(Ls+Lt),1]).long())\n flag_s = torch.gather(flag_s,3,global_index_s[:,None,None,:].repeat([1,C,int(Ls+Lt),1]).long())\n flag = torch.cat([flag_t, flag_s], dim = 3)\n B, C, L11, L12 = flag.shape\n try:\n assert(L11 == int(Lt + Ls))\n assert(L12 == int(Lt + Ls))\n except:\n print('L11:{}, L12:{}, L1:{}, L2:{}'.format(L11, L12, L1, L2))\n return flag\n def crop_fusion(self, flag, attn, global_index_s, global_index_t,H1 = 64, H2 = 256 ):\n flag = self.crop_flag(flag=flag, global_index_s=global_index_s, global_index_t=global_index_t)\n B,C,L1,L2 = flag.shape\n Ba, Ca, La, La2 = attn.shape\n _,idx1 = flag.mean(dim=3,keepdim=False).sort(dim=2,descending=True)\n # print('shape of flag:{}, idx1:{}'.format(flag.shape, idx1[:,:,:32,None].repeat([1,Ca,1,L2]).shape))\n flag = torch.gather(flag,2,idx1[:,:,:32,None].repeat([1,C,1,L2]).long())\n attn = torch.gather(attn,2,idx1[:,:,:32,None].repeat([1,Ca,1,L2]).long())\n _,idx2 = flag.mean(dim=2,keepdim=False).sort(dim=2,descending=True)\n flag = torch.gather(flag,3,idx2[:,:,None,:32].repeat([1,C,32,1]).long())\n attn = torch.gather(attn,3,idx2[:,:,None,:32].repeat([1,Ca,32,1]).long())\n return attn * flag\n\n def loss_rank(self, outputs, targetsi, temp_annoi=None):\n \"\"\"Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss\n targets dicts must contain the key \"boxes\" containing a tensor of dim [nb_target_boxes, 4]\n The target boxes are expected in format (center_x, center_y, h, w), normalized by the image size.\n \"\"\"\n attn = outputs['attn']\n # print('attn shape:{}'.format(attn.shape))\n attn1 = torch.cat([attn[:,:,114:344,57:114], attn[:,:,114:344,344:]],dim=3)\n attn1 = attn1.mean(dim=0, keepdim=True).mean(dim=1, keepdim=True)\n attn2 = torch.cat([attn[:,:,344:,:57], attn[:,:,344:,114:344]],dim=3)\n attn2 = attn2.mean(dim=0, keepdim=True).mean(dim=1, keepdim=True)\n\n attn = torch.cat([attn1, attn2],dim=1)\n B, C, H, W = attn.shape\n # _,s1,_ = torch.svd(attn1.reshape([B*C, H, W]))\n\n _,s1,_ = torch.svd(attn.reshape([B*C, H, W]))\n\n s01 = torch.abs(s1 - 1)\n\n return torch.mean(s01)\n \n# \u65b0\u589eevent\u5206\u652f\u7684\u6ce8\u610f\u529bloss\u6b63\u5219\u5316\u8ba1\u7b97\n\n def loss_rank_event(self, outputs, targetsi, temp_annoi=None):\n \"\"\"Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss\n targets dicts must contain the key \"boxes\" containing a tensor of dim [nb_target_boxes, 4]\n The target boxes are expected in format (center_x, center_y, h, w), normalized by the image size.\n \"\"\"\n attn = outputs['attn_event']\n # print('attn shape:{}'.format(attn.shape))\n attn1 = torch.cat([attn[:,:,114:344,57:114], attn[:,:,114:344,344:]],dim=3)\n attn1 = attn1.mean(dim=0, keepdim=True).mean(dim=1, keepdim=True)\n attn2 = torch.cat([attn[:,:,344:,:57], attn[:,:,344:,114:344]],dim=3)\n attn2 = attn2.mean(dim=0, keepdim=True).mean(dim=1, keepdim=True)\n\n attn = torch.cat([attn1, attn2],dim=1)\n B, C, H, W = attn.shape\n # _,s1,_ = torch.svd(attn1.reshape([B*C, H, W]))\n\n _,s1,_ = torch.svd(attn.reshape([B*C, H, W]))\n\n s01 = torch.abs(s1 - 1)\n\n return torch.mean(s01)\u4e2d\uff0c\u62a5\u9519Restarting training from last epoch ...\nFinished training!\nTraining crashed at epoch 1\nTraceback for the error!\nTraceback (most recent call last):\n File \"/data2/local_userdata/heyuting/new_VOT/COESOT-main/CEUTrack/lib/train/../../lib/train/trainers/base_trainer.py\", line 85, in train\n self.train_epoch()\n File \"/data2/local_userdata/heyuting/new_VOT/COESOT-main/CEUTrack/lib/train/../../lib/train/trainers/ltr_trainer.py\", line 133, in train_epoch\n self.cycle_dataset(loader)\n File \"/data2/local_userdata/heyuting/new_VOT/COESOT-main/CEUTrack/lib/train/../../lib/train/trainers/ltr_trainer.py\", line 86, in cycle_dataset\n loss, stats = self.actor(data)\n File \"/data2/local_userdata/heyuting/new_VOT/COESOT-main/CEUTrack/lib/train/../../lib/train/actors/ceutrack.py\", line 34, in __call__\n loss, status = self.compute_losses(out_dict, data)\n File \"/data2/local_userdata/heyuting/new_VOT/COESOT-main/CEUTrack/lib/train/../../lib/train/actors/ceutrack.py\", line 110, in compute_losses\n rank_loss = self.loss_rank(pred_dict,gt_dict['search_anno'], gt_dict['template_anno'])\n File \"/data2/local_userdata/heyuting/new_VOT/COESOT-main/CEUTrack/lib/train/../../lib/train/actors/ceutrack.py\", line 185, in loss_rank\n attn = torch.cat([attn1, attn2],dim=1)\nRuntimeError: Sizes of tensors must match except in dimension 1. Expected size 206 but got size 0 for tensor number 1 in the list.\n\nRestarting training from last epoch ...\nFinished training!"},{"assistant_initiates":false,"asst_text":"\u9519\u8bef\u4fe1\u606f\u548c\u6253\u5370\u7684\u5f62\u72b6\u4fe1\u606f\u8868\u660e\uff0c`attn2`\u7684\u7b2c\u4e09\u4e2a\u7ef4\u5ea6\uff08\u4ee3\u8868\u9ad8\u5ea6\uff09\u4e3a0\uff0c\u8fd9\u610f\u5473\u7740`attn2`\u662f\u4e00\u4e2a\u7a7a\u7684\u5f20\u91cf\uff08\u5728\u8fd9\u4e2a\u7ef4\u5ea6\u4e0a\u6ca1\u6709\u6570\u636e\uff09\u3002\u8fd9\u662f\u5bfc\u81f4\u62fc\u63a5\u64cd\u4f5c\u5931\u8d25\u7684\u76f4\u63a5\u539f\u56e0\uff0c\u56e0\u4e3a\u65e0\u6cd5\u5728\u7a7a\u7684\u7ef4\u5ea6\u4e0a\u4e0e\u5176\u4ed6\u5f20\u91cf\u8fdb\u884c\u62fc\u63a5\u3002\n\n\u8981\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\uff0c\u6709\u51e0\u79cd\u53ef\u80fd\u7684\u65b9\u6cd5\uff1a\n\n### \u65b9\u6cd51: \u8df3\u8fc7\u7a7a\u5f20\u91cf\n\n\u5982\u679c`attn2`\u5bf9\u4e8e\u540e\u7eed\u64cd\u4f5c\u4e0d\u662f\u5fc5\u9700\u7684\uff0c\u60a8\u53ef\u4ee5\u7f16\u5199\u6761\u4ef6\u8bed\u53e5\u6765\u8df3\u8fc7\u7a7a\u5f20\u91cf\u7684\u62fc\u63a5\uff1a\n\n```python\nif attn2.size(2) > 0: # \u68c0\u67e5attn2\u7684\u9ad8\u5ea6\u662f\u5426\u5927\u4e8e0\n attn = torch.cat([attn1, attn2], dim=3) # \u5982\u679cattn2\u4e0d\u4e3a\u7a7a\uff0c\u5219\u5728\u5bbd\u5ea6\u7ef4\u5ea6\u62fc\u63a5\nelse:\n attn = attn1 # \u5982\u679cattn2\u4e3a\u7a7a\uff0c\u5219\u53ea\u4f7f\u7528attn1\n```\n\n### \u65b9\u6cd52: \u68c0\u67e5\u5e76\u89e3\u51b3attn2\u7a7a\u5f20\u91cf\u7684\u6839\u672c\u539f\u56e0\n\n`attn2`\u53d8\u4e3a\u7a7a\u5f20\u91cf\u53ef\u80fd\u662f\u7531\u4e8e\u4e4b\u524d\u7684\u64cd\u4f5c\u9519\u8bef\u6216\u6570\u636e\u95ee\u9898\u3002\u60a8\u9700\u8981\u68c0\u67e5`attn2`\u751f\u6210\u8fc7\u7a0b\u4e2d\u7684\u903b\u8f91\uff0c\u770b\u770b\u662f\u54ea\u4e00\u6b65\u5bfc\u81f4\u4e86\u7a7a\u5f20\u91cf\u7684\u51fa\u73b0\u3002\u53ef\u80fd\u7684\u539f\u56e0\u5305\u62ec\uff1a\n\n- **\u9009\u62e9\u6216\u88c1\u5207\u903b\u8f91\u9519\u8bef**\uff1a\u68c0\u67e5\u751f\u6210`attn2`\u7684\u4ee3\u7801\uff0c\u786e\u8ba4\u662f\u5426\u56e0\u4e3a\u9519\u8bef\u7684\u7d22\u5f15\u3001\u88c1\u5207\u8303\u56f4\u6216\u6761\u4ef6\u5bfc\u81f4\u4e86\u7a7a\u5f20\u91cf\u3002\n- **\u8f93\u5165\u6570\u636e\u95ee\u9898**\uff1a\u5982\u679c`attn2`\u4f9d\u8d56\u4e8e\u8f93\u5165\u6570\u636e\uff0c\u68c0\u67e5\u8f93\u5165\u6570\u636e\u662f\u5426\u5b58\u5728\u5f02\u5e38\uff0c\u5982\u5c3a\u5bf8\u5f02\u5e38\u3001\u9519\u8bef\u7684\u6570\u636e\u5904\u7406\u6d41\u7a0b\u7b49\u3002\n\n### \u65b9\u6cd53: \u8c03\u6574\u6570\u636e\u5904\u7406\u6d41\u7a0b\u6216\u6a21\u578b\u67b6\u6784\n\n\u5982\u679c`attn2`\u7a7a\u5f20\u91cf\u7684\u51fa\u73b0\u662f\u7531\u4e8e\u7279\u5b9a\u7684\u6a21\u578b\u8bbe\u8ba1\u6216\u6570\u636e\u5904\u7406\u6d41\u7a0b\u9020\u6210\u7684\uff0c\u60a8\u53ef\u80fd\u9700\u8981\u91cd\u65b0\u5ba1\u89c6\u8be5\u6d41\u7a0b\u6216\u67b6\u6784\u3002\u8003\u8651\u662f\u5426\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574\u6a21\u578b\u7ed3\u6784\u6216\u4fee\u6539\u6570\u636e\u9884\u5904\u7406\u6b65\u9aa4\u6765\u907f\u514d\u8fd9\u79cd\u60c5\u51b5\u3002\n\n### \u793a\u4f8b\uff1a\u68c0\u67e5\u5bfc\u81f4`attn2`\u4e3a\u7a7a\u7684\u64cd\u4f5c\n\n\u5047\u8bbe`attn1`\u548c`attn2`\u662f\u901a\u8fc7\u5bf9\u539f\u59cb\u6ce8\u610f\u529b\u77e9\u9635`attn`\u8fdb\u884c\u64cd\u4f5c\u800c\u5f97\u5230\u7684\uff0c\u68c0\u67e5\u8fd9\u4e9b\u64cd\u4f5c\u662f\u5426\u6b63\u786e\u6267\u884c\uff1a\n\n```python\n# \u793a\u4f8b\uff1a\u5047\u8bbe\u6709\u5982\u4e0b\u64cd\u4f5c\u5bfc\u81f4attn2\u4e3a\u7a7a\nattn_original = ... # \u539f\u59cb\u6ce8\u610f\u529b\u77e9\u9635\nattn1 = attn_original[:, :, :, :57] # \u6b63\u786e\u7684\u88c1\u5207\u64cd\u4f5c\nattn2 = attn_original[:, :, :, 57:] # \u53ef\u80fd\u5bfc\u81f4\u7a7a\u5f20\u91cf\u7684\u64cd\u4f5c\n\n# \u68c0\u67e5attn_original\u7684\u5f62\u72b6\uff0c\u786e\u8ba4\u662f\u5426\u56e0\u4e3a\u9519\u8bef\u7684\u8303\u56f4\u9009\u62e9\u5bfc\u81f4attn2\u4e3a\u7a7a\nprint(\"attn_original shape: \", attn_original.shape)\n\n# \u57fa\u4e8e\u68c0\u67e5\u7ed3\u679c\u8c03\u6574\u64cd\u4f5c\n```\n\n\u7efc\u4e0a\u6240\u8ff0\uff0c\u89e3\u51b3\u8fd9\u4e00\u95ee\u9898\u7684\u5173\u952e\u662f\u627e\u5230`attn2`\u53d8\u4e3a\u7a7a\u5f20\u91cf\u7684\u6839\u672c\u539f\u56e0\uff0c\u5e76\u6839\u636e\u5b9e\u9645\u60c5\u51b5\u9009\u62e9\u5408\u9002\u7684\u89e3\u51b3\u65b9\u6848\u3002","behavior":"degenerate_repetition","conversation_hash":"979356f838792a17998dbf31df3eb938","conversation_redacted":false,"conversation_toxic":false,"country":"Hong Kong","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4-0125-preview","n_chars":1186,"n_turns_in_conversation":16,"n_words":86,"source_row_offset":76602,"source_shard":"turns-00020.parquet","timestamp":"Sun, 28 Apr 2024 12:51:46 GMT","turn_id":"b8a3b6729d17d123edb99cb9","turn_index":12,"user_text":"\u6253\u5370\u4e4b\u540e\uff1aattn1.shape: torch.Size([1, 1, 206, 57])\nattn2.shape: torch.Size([1, 1, 0, 263])"},{"assistant_initiates":false,"asst_text":"\u5728\u60a8\u7684\u4ee3\u7801\u6bb5\u4e2d\uff0c`attn1`\u548c`attn2`\u770b\u8d77\u6765\u662f\u4ece\u66f4\u5927\u7684\u6ce8\u610f\u529b\u5f20\u91cf\u6216\u67d0\u4e2a\u7279\u5b9a\u64cd\u4f5c\u7684\u8f93\u51fa\u5f20\u91cf\u4e2d\u751f\u6210\u7684\u7279\u5b9a\u90e8\u5206\u3002\u5177\u4f53\u6765\u8bb2\uff1a\n\n- `attn1`\u7531\u539f\u59cb\u6ce8\u610f\u529b\u5f20\u91cf`attn`\u5728\u67d0\u4e2a\u7ef4\u5ea6\u4e0a\u5207\u5206\u6216\u91cd\u6392\u540e\u5f97\u5230\u7684\u7247\u6bb5\u7ec4\u6210\u3002\u5b83\u662f\u901a\u8fc7\u5c06`attn`\u5f20\u91cf\u5728\u7b2c\u4e09\u7ef4\uff08\u5bbd\u5ea6\uff09\u4e0a\u7684\u5206\u6bb5\uff08\u4ece\u7b2c114\u5217\u5230\u7b2c344\u5217\u4f4d\u7f6e\uff0c\u7136\u540e\u662f\u4ece\u7b2c57\u5217\u5230\u7b2c114\u5217\u548c\u4ece\u7b2c344\u5217\u5230\u6700\u540e\uff09\u8fdb\u884c\u62fc\u63a5\uff0c\u5e76\u5bf9\u8fd9\u4e9b\u7279\u5b9a\u90e8\u5206\u6c42\u5e73\u5747\u5f97\u5230\u7684\u3002\u5176\u76ee\u7684\u53ef\u80fd\u662f\u4e3a\u4e86\u63d0\u53d6\u6216\u805a\u7126\u4e8e\u6ce8\u610f\u529b\u5f20\u91cf\u7684\u7279\u5b9a\u533a\u57df\uff0c\u8fd9\u4e9b\u533a\u57df\u53ef\u80fd\u5bf9\u4efb\u52a1\u6709\u7279\u522b\u7684\u91cd\u8981\u6027\u3002\n \n- `attn2`\u4ea6\u662f\u901a\u8fc7\u76f8\u4f3c\u7684\u64cd\u4f5c\u83b7\u5f97\uff0c\u4f46\u5b83\u5173\u6ce8\u7684\u662f`attn`\u5f20\u91cf\u4e2d\u7684\u4e0d\u540c\u90e8\u5206\uff08\u7b2c344\u5217\u4e4b\u524d\u7684\u4ece\u5f00\u59cb\u5230\u7b2c57\u5217\u3001\u7b2c344\u5217\u5230\u6700\u540e\u7684\u7b2c114\u5217\u5230\u7b2c344\u5217\u4f4d\u7f6e\uff09\u3002\u5b83\u7684\u7528\u9014\u548c`attn1`\u7c7b\u4f3c\uff0c\u76ee\u7684\u53ef\u80fd\u662f\u4ece\u539f\u59cb\u7684\u6ce8\u610f\u529b\u5f20\u91cf\u4e2d\u63d0\u53d6\u53e6\u4e00\u7ec4\u7279\u5b9a\u533a\u57df\u7684\u4fe1\u606f\u3002\n\n\u8fd9\u4e9b\u64cd\u4f5c\u7684\u76ee\u6807\u4f3c\u4e4e\u662f\u8981\u5c06\u6ce8\u610f\u529b\u805a\u7126\u5728\u539f\u59cb\u6ce8\u610f\u529b\u5f20\u91cf\u7684\u7279\u5b9a\u533a\u57df\u4e0a\uff0c\u5e76\u901a\u8fc7\u5904\u7406\u8fd9\u4e9b\u533a\u57df\u6765\u5f97\u5230\u6709\u7528\u7684\u4fe1\u606f\uff0c\u8fdb\u800c\u4f18\u5316\u6574\u4e2a\u7f51\u7edc\u7684\u884c\u4e3a\u3002\u5e38\u89c1\u7684\u573a\u666f\u5305\u62ec\u4f46\u4e0d\u9650\u4e8e\u6539\u8fdb\u6a21\u578b\u5bf9\u7279\u5b9a\u533a\u57df\u7684\u54cd\u5e94\uff0c\u4f8b\u5982\u5728\u89c6\u89c9\u4efb\u52a1\u4e2d\u5173\u6ce8\u56fe\u50cf\u7684\u67d0\u4e2a\u7279\u5b9a\u90e8\u5206\uff0c\u6216\u8005\u5728\u5e8f\u5217\u4efb\u52a1\u4e2d\u7279\u522b\u5173\u6ce8\u5e8f\u5217\u7684\u7279\u5b9a\u6bb5\u843d\u3002\n\n\u8fd9\u6837\u7684\u64cd\u4f5c\u53ef\u80fd\u57fa\u4e8e\u4ee5\u4e0b\u5047\u8bbe\uff1a\n- \u6a21\u578b\u5728\u8fdb\u884c\u89c6\u89c9\u8bc6\u522b\u6216\u5176\u4ed6\u4efb\u52a1\u65f6\uff0c\u5e76\u975e\u6240\u6709\u533a\u57df\u90fd\u540c\u6837\u91cd\u8981\u3002\u901a\u8fc7\u91cd\u70b9\u5173\u6ce8\u7279\u5b9a\u7684\u533a\u57df\uff08\u8fd9\u4e9b\u533a\u57df\u53ef\u80fd\u5305\u542b\u4e86\u5bf9\u6700\u7ec8\u4efb\u52a1\u8f83\u4e3a\u91cd\u8981\u7684\u4fe1\u606f\uff09\uff0c\u6a21\u578b\u53ef\u4ee5\u66f4\u6709\u6548\u5730\u4ece\u6570\u636e\u4e2d\u5b66\u4e60\u3002\n- \u5bf9\u4e8e\u5305\u542b\u5927\u91cf\u5197\u4f59\u4fe1\u606f\u7684\u5927\u6570\u636e\u96c6\u800c\u8a00\uff0c\u8fd9\u79cd\u7b56\u7565\u53ef\u4ee5\u63d0\u9ad8\u6a21\u578b\u7684\u5b66\u4e60\u6548\u7387\u548c\u6700\u7ec8\u6027\u80fd\u3002\n\n\u91cd\u8981\u7684\u662f\uff0c\u8fd9\u6837\u7684\u64cd\u4f5c\u548c\u7b56\u7565\u7684\u6548\u7528\u9700\u901a\u8fc7\u5b9e\u9a8c\u548c\u8bc4\u4f30\u6765\u9a8c\u8bc1\u3002\u4f8b\u5982\uff0c\u901a\u8fc7\u6bd4\u8f83\u6a21\u578b\u5728\u52a0\u5165\u8fd9\u79cd\u6ce8\u610f\u529b\u5904\u7406\u4e4b\u524d\u540e\u7684\u6027\u80fd\u5dee\u5f02\uff0c\u6765\u51b3\u5b9a\u5176\u5b9e\u9645\u7684\u4ef7\u503c\u548c\u9002\u7528\u6027\u3002","behavior":"degenerate_repetition","conversation_hash":"979356f838792a17998dbf31df3eb938","conversation_redacted":false,"conversation_toxic":false,"country":"Hong Kong","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4-0125-preview","n_chars":12296,"n_turns_in_conversation":16,"n_words":974,"source_row_offset":76603,"source_shard":"turns-00020.parquet","timestamp":"Sun, 28 Apr 2024 12:51:46 GMT","turn_id":"358f0de4f03b0fcdf6192b71","turn_index":13,"user_text":"\u5728from . import BaseActor\nfrom lib.utils.misc import NestedTensor\nfrom lib.utils.box_ops import box_cxcywh_to_xyxy, box_xywh_to_xyxy\nimport torch\nfrom lib.utils.merge import merge_template_search\nfrom ...utils.heapmap_utils import generate_heatmap\nfrom ...utils.ce_utils import generate_mask_cond, adjust_keep_rate\n\n\nclass CEUTrackActor(BaseActor):\n \"\"\" Actor for training CEUTrack models \"\"\"\n\n def __init__(self, net, objective, loss_weight, settings, cfg=None):\n super().__init__(net, objective)\n self.loss_weight = loss_weight\n self.settings = settings\n self.bs = self.settings.batchsize # batch size\n self.cfg = cfg\n\n def __call__(self, data):\n \"\"\"\n args:\n data - The input data, should contain the fields 'template', 'search', 'gt_bbox'.\n template_images: (N_t, batch, 3, H, W)\n search_images: (N_s, batch, 3, H, W)\n returns:\n loss - the training loss\n status - dict containing detailed losses\n \"\"\"\n # forward pass\n out_dict = self.forward_pass(data)\n\n # compute losses\n loss, status = self.compute_losses(out_dict, data)\n\n return loss, status\n\n def forward_pass(self, data):\n # currently only support 1 template and 1 search region\n assert len(data['template_images']) == 1\n assert len(data['search_images']) == 1\n assert len(data['template_event']) == 1\n assert len(data['search_event']) == 1\n\n template_list = []\n for i in range(self.settings.num_template):\n template_img_i = data['template_images'][i].view(-1,\n *data['template_images'].shape[2:]) # (batch, 3, 128, 128)\n # template_att_i = data['template_att'][i].view(-1, *data['template_att'].shape[2:]) # (batch, 128, 128)\n template_list.append(template_img_i)\n\n search_img = data['search_images'][0].view(-1, *data['search_images'].shape[2:]) # (batch, 3, 320, 320)\n # search_att = data['search_att'][0].view(-1, *data['search_att'].shape[2:]) # (batch, 320, 320)\n\n template_event = data['template_event'][0].view(-1, *data['template_event'].shape[2:])\n search_event = data['search_event'][0].view(-1, *data['search_event'].shape[2:])\n\n box_mask_z = None\n ce_keep_rate = None\n if self.cfg.MODEL.BACKBONE.CE_LOC:\n box_mask_z = generate_mask_cond(self.cfg, template_list[0].shape[0], template_list[0].device,\n data['template_anno'][0])\n\n ce_start_epoch = self.cfg.TRAIN.CE_START_EPOCH\n ce_warm_epoch = self.cfg.TRAIN.CE_WARM_EPOCH\n ce_keep_rate = adjust_keep_rate(data['epoch'], warmup_epochs=ce_start_epoch,\n total_epochs=ce_start_epoch + ce_warm_epoch,\n ITERS_PER_EPOCH=1,\n base_keep_rate=self.cfg.MODEL.BACKBONE.CE_KEEP_RATIO[0])\n\n if len(template_list) == 1:\n template_list = template_list[0]\n\n out_dict = self.net(template=template_list,\n search=search_img,\n event_template=template_event,\n event_search=search_event,\n ce_template_mask=box_mask_z,\n ce_keep_rate=ce_keep_rate,\n return_last_attn=False)\n\n return out_dict\n\n def compute_losses(self, pred_dict, gt_dict, return_status=True):\n # gt gaussian map\n gt_bbox = gt_dict['search_anno'][-1] # (Ns, batch, 4) (x1,y1,w,h) -> (batch, 4)\n gt_gaussian_maps = generate_heatmap(gt_dict['search_anno'], self.cfg.DATA.SEARCH.SIZE, self.cfg.MODEL.BACKBONE.STRIDE)\n gt_gaussian_maps = gt_gaussian_maps[-1].unsqueeze(1)\n\n # Get boxes\n pred_boxes = pred_dict['pred_boxes']\n if torch.isnan(pred_boxes).any():\n raise ValueError(\"Network outputs is NAN! Stop Training\")\n num_queries = pred_boxes.size(1)\n pred_boxes_vec = box_cxcywh_to_xyxy(pred_boxes).view(-1, 4) # (B,N,4) --> (BN,4) (x1,y1,x2,y2)\n gt_boxes_vec = box_xywh_to_xyxy(gt_bbox)[:, None, :].repeat((1, num_queries, 1)).view(-1, 4).clamp(min=0.0,\n max=1.0) # (B,4) --> (B,1,4) --> (B,N,4)\n # compute giou and iou\n try:\n giou_loss, iou = self.objective['giou'](pred_boxes_vec, gt_boxes_vec) # (BN,4) (BN,4)\n except:\n giou_loss, iou = torch.tensor(0.0).cuda(), torch.tensor(0.0).cuda()\n # compute l1 loss\n l1_loss = self.objective['l1'](pred_boxes_vec, gt_boxes_vec) # (BN,4) (BN,4)\n # compute location loss\n if 'score_map' in pred_dict:\n location_loss = self.objective['focal'](pred_dict['score_map'], gt_gaussian_maps)\n else:\n location_loss = torch.tensor(0.0, device=l1_loss.device)\n rank_loss = self.loss_rank(pred_dict,gt_dict['search_anno'], gt_dict['template_anno'])\n # weighted sum\n loss = self.loss_weight['giou'] * giou_loss + self.loss_weight['l1'] * l1_loss + self.loss_weight['focal'] * location_loss + rank_loss*1.2\n if return_status:\n # status for log\n mean_iou = iou.detach().mean()\n status = {\"Loss/total\": loss.item(),\n \"Loss/giou\": giou_loss.item(),\n \"Loss/l1\": l1_loss.item(),\n \"Loss/location\": location_loss.item(),\n \"IoU\": mean_iou.item()}\n return loss, status\n else:\n return loss\n\n def _random_permute(self,matrix):\n # matrix = random.choice(matrix)\n b, c, h, w = matrix.shape\n idx = [ torch.randperm(c).to(matrix.device) for i in range(b)]\n idx = torch.stack(idx, dim=0)[:, :, None, None].repeat([1,1,h,w])\n # idx = torch.randperm(c)[None,:,None,None].repeat([b,1,h,w]).to(matrix.device)\n matrix01 = torch.gather(matrix, 1, idx)\n return matrix01\n def crop_flag(self, flag, global_index_s, global_index_t,H1 = 64, H2 = 256):\n B,Ls = global_index_s.shape\n B, Lt = global_index_t.shape\n B,C,L1,L2 = flag.shape\n flag_t = flag[:,:,:H1,:]\n flag_s = flag[:,:,H1:,:]\n\n flag_t = torch.gather(flag_t,2,global_index_t[:,None,:,None].repeat([1,C,1,L2]).long())\n flag_s = torch.gather(flag_s,2,global_index_s[:,None,:,None].repeat([1,C,1,L2]).long())\n flag = torch.cat([flag_t, flag_s], dim = 2)\n\n flag_t = flag[:,:,:,:H1]\n flag_s = flag[:,:,:,H1:]\n flag_t = torch.gather(flag_t,3,global_index_t[:,None,None,:].repeat([1,C,int(Ls+Lt),1]).long())\n flag_s = torch.gather(flag_s,3,global_index_s[:,None,None,:].repeat([1,C,int(Ls+Lt),1]).long())\n flag = torch.cat([flag_t, flag_s], dim = 3)\n B, C, L11, L12 = flag.shape\n try:\n assert(L11 == int(Lt + Ls))\n assert(L12 == int(Lt + Ls))\n except:\n print('L11:{}, L12:{}, L1:{}, L2:{}'.format(L11, L12, L1, L2))\n return flag\n def crop_fusion(self, flag, attn, global_index_s, global_index_t,H1 = 64, H2 = 256 ):\n flag = self.crop_flag(flag=flag, global_index_s=global_index_s, global_index_t=global_index_t)\n B,C,L1,L2 = flag.shape\n Ba, Ca, La, La2 = attn.shape\n _,idx1 = flag.mean(dim=3,keepdim=False).sort(dim=2,descending=True)\n # print('shape of flag:{}, idx1:{}'.format(flag.shape, idx1[:,:,:32,None].repeat([1,Ca,1,L2]).shape))\n flag = torch.gather(flag,2,idx1[:,:,:32,None].repeat([1,C,1,L2]).long())\n attn = torch.gather(attn,2,idx1[:,:,:32,None].repeat([1,Ca,1,L2]).long())\n _,idx2 = flag.mean(dim=2,keepdim=False).sort(dim=2,descending=True)\n flag = torch.gather(flag,3,idx2[:,:,None,:32].repeat([1,C,32,1]).long())\n attn = torch.gather(attn,3,idx2[:,:,None,:32].repeat([1,Ca,32,1]).long())\n return attn * flag\n\n def loss_rank(self, outputs, targetsi, temp_annoi=None):\n \"\"\"Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss\n targets dicts must contain the key \"boxes\" containing a tensor of dim [nb_target_boxes, 4]\n The target boxes are expected in format (center_x, center_y, h, w), normalized by the image size.\n \"\"\"\n attn = outputs['attn']\n # print('attn shape:{}'.format(attn.shape))\n attn1 = torch.cat([attn[:,:,114:344,57:114], attn[:,:,114:344,344:]],dim=3)\n attn1 = attn1.mean(dim=0, keepdim=True).mean(dim=1, keepdim=True)\n attn2 = torch.cat([attn[:,:,344:,:57], attn[:,:,344:,114:344]],dim=3)\n attn2 = attn2.mean(dim=0, keepdim=True).mean(dim=1, keepdim=True)\n # print('attn1 shape:{},attn2 shape:{}, attn:{}'.format(attn1.shape,attn2.shape,attn.shape))\n\n # attn = self._random_permute(attn)\n # attn = attn[:,:,:,:]\n # B1, C1, H1, W1 = attn.shape\n # global_index_s = outputs['out_global_s']\n # global_index_t = outputs['out_global_t']\n # try:\n # assert((global_index_s.shape[1] + global_index_t.shape[1])== int(H1/2))\n # except:\n # print('Falut,shape of attn:{}, s:{}, t:{}'.format(attn.shape,global_index_s.shape, global_index_t.shape ))\n\n # H1 = int(64)\n # H2 = int(256)\n # l_t = int(math.sqrt(64))\n # l_s = int(math.sqrt(256))\n\n # temp_anno = temp_annoi[0,:,:]\n # targets = targetsi[0,:,:]\n # r_s = torch.arange(l_s).to(temp_anno.device)\n # r_t = torch.arange(l_t).to(temp_anno.device)\n # r_t = r_t[None,:].repeat([B1,1])\n\n # cx, cy, w, h = temp_anno[:,0:1], temp_anno[:,1:2], temp_anno[:,2:3], temp_anno[:,3:4]\n # cx *= l_t\n # cy *= l_t\n # w *= l_t\n # h *= l_t\n # flagx_01 = r_t >= cx - w/2\n # flagx_02 = r_t <= cx + w/2\n # flagy_02 = r_t >= cy - h/2\n # flagy_01 = r_t <= cy + h/2\n # flagx = flagx_01.float()*flagx_02.float()\n # flagy = flagy_01.float()*flagy_02.float()\n # flagx = flagx[:,None,:].repeat([1,l_t,1])\n # flagy = flagy[:,:,None].repeat([1,1,l_t])\n # flag = flagx*flagy\n # flagt = flag.reshape([B1, H1])\n\n # cx, cy, w, h = targets[:,0:1], targets[:,1:2], targets[:,2:3], targets[:,3:4]\n # cx *= l_s\n # cy *= l_s\n # w *= l_s\n # h *= l_s\n # flagx_01 = r_s >= cx - w/2\n # flagx_02 = r_s <= cx + w/2\n # flagy_02 = r_s >= cy - h/2\n # flagy_01 = r_s <= cy + h/2\n # flagx = flagx_01.float()*flagx_02.float()\n # flagy = flagy_01.float()*flagy_02.float()\n # flagx = flagx[:,None,:].repeat([1,l_s,1])\n # flagy = flagy[:,:,None].repeat([1,1,l_s])\n # flag = flagx*flagy\n\n # flags = flag.reshape([B1, H2])\n\n # flag = torch.cat([flagt, flags], dim=1)\n # flag_total = flag[:,:,None].repeat([1,1,int(H1+H2)]) * flag[:,None,:].repeat([1,int(H1+H2),1])\n # attn1 = self.crop_fusion(flag_total[:,None,:,:], attn, global_index_s, global_index_t)\n attn = torch.cat([attn1, attn2],dim=1)\n B, C, H, W = attn.shape\n # _,s1,_ = torch.svd(attn1.reshape([B*C, H, W]))\n\n _,s1,_ = torch.svd(attn.reshape([B*C, H, W]))\n\n s01 = torch.abs(s1 - 1)\n\n return torch.mean(s01)\u4e2d\uff0cattn1\u548cattn2\u5206\u522b\u8868\u793a\u4ec0\u4e48\uff1f"},{"assistant_initiates":false,"asst_text":"\u5728\u7ed9\u5b9a\u7684\u4ee3\u7801\u6bb5\u4e2d\uff0c`attn`\u662f\u901a\u8fc7\u5c06`attn1`\u548c`attn2`\u5728\u7279\u5b9a\u7ef4\u5ea6\uff08\u8fd9\u91cc\u662f`dim=1`\uff0c\u901a\u5e38\u5bf9\u5e94\u4e8e\u901a\u9053\u7ef4\u5ea6\uff09\u4e0a\u62fc\u63a5\u5f97\u5230\u7684\u4e00\u4e2a\u65b0\u5f20\u91cf\u3002\u5177\u4f53\u6765\u8bb2\uff1a\n\n1. **`attn1`\u7684\u751f\u6210**\uff1a\u57fa\u4e8e\u7ed9\u5b9a\u7684\u6ce8\u610f\u529b\u77e9\u9635`attn`\u7684\u7279\u5b9a\u90e8\u5206\u6216\u901a\u8fc7\u67d0\u79cd\u64cd\u4f5c\uff08\u4f8b\u5982\uff0c\u5e73\u5747\uff09\u4fee\u6539\u7684\u7ed3\u679c\u3002\u5728\u60a8\u7684\u4ee3\u7801\u4e2d\uff0c`attn1`\u662f\u901a\u8fc7\u53d6`attn`\u77e9\u9635\u4e2d\u7684\u4e00\u90e8\u5206\u533a\u57df\uff0c\u7136\u540e\u5bf9\u9009\u53d6\u7684\u533a\u57df\u8fdb\u884c\u62fc\u63a5\u540e\u8fdb\u884c\u5e73\u5747\u5904\u7406\u5f97\u5230\u7684\u3002\u8fd9\u4e9b\u64cd\u4f5c\u610f\u56fe\u662f\u4ece\u539f\u59cb\u6ce8\u610f\u529b\u77e9\u9635\u4e2d\u63d0\u53d6\u6216\u5f3a\u8c03\u7279\u5b9a\u7684\u4fe1\u606f\u3002\n\n2. **`attn2`\u7684\u751f\u6210**\uff1a\u540c\u6837\u57fa\u4e8e`attn`\u4f46\u9009\u53d6\u7684\u662f\u4e0d\u540c\u4e8e`attn1`\u7684\u533a\u57df\u8fdb\u884c\u64cd\u4f5c\u3002\u751f\u6210\u65b9\u5f0f\u4e0e`attn1`\u7c7b\u4f3c\uff0c\u4e5f\u662f\u901a\u8fc7\u9009\u62e9\u3001\u62fc\u63a5\u548c\u5e73\u5747\u5904\u7406\u5f97\u5230\u7684\u3002\n\n3. **`attn`\u4e0e`attn1`\u7684\u5173\u7cfb**\uff1a\u6700\u7ec8\u7684`attn`\u662f`attn1`\u548c`attn2`\u7684\u5408\u5e76\u7ed3\u679c\uff0c\u5b83\u5c06\u4e24\u90e8\u5206\u663e\u8457\u7279\u5f81\u6574\u5408\u5230\u4e00\u4e2a\u5f20\u91cf\u4e2d\u3002\u8fd9\u6837\u7684\u5904\u7406\u7b56\u7565\u53ef\u80fd\u662f\u4e3a\u4e86\u7ed3\u5408\u4e24\u79cd\u4e0d\u540c\u7684\u4fe1\u606f\u6216\u7279\u5f81\u8868\u793a\uff0c\u4ece\u800c\u4f7f\u6700\u7ec8\u7684`attn`\u80fd\u66f4\u5168\u9762\u5730\u6355\u83b7\u8f93\u5165\u6570\u636e\u7684\u91cd\u8981\u7279\u5f81\u3002\n\n\u8fd9\u79cd\u65b9\u6cd5\u5728\u5904\u7406\u590d\u6742\u8f93\u5165\u6570\u636e\uff08\u5982\u56fe\u50cf\u3001\u89c6\u9891\u5e8f\u5217\u6216\u591a\u6a21\u6001\u6570\u636e\uff09\u65f6\u5c24\u5176\u6709\u7528\uff0c\u56e0\u4e3a\u5b83\u5141\u8bb8\u6a21\u578b\u4ece\u4e0d\u540c\u7684\u89d2\u5ea6\u6216\u4f7f\u7528\u4e0d\u540c\u7684\u7b56\u7565\u63d0\u53d6\u91cd\u8981\u4fe1\u606f\uff0c\u5e76\u5c06\u8fd9\u4e9b\u4fe1\u606f\u878d\u5408\uff0c\u4ee5\u66f4\u597d\u5730\u5b8c\u6210\u7279\u5b9a\u7684\u4efb\u52a1\uff0c\u6bd4\u5982\u56fe\u50cf\u5206\u5272\u3001\u76ee\u6807\u68c0\u6d4b\u6216\u89c6\u9891\u7406\u89e3\u7b49\u3002\n\n\u8bf7\u6ce8\u610f\uff0c\u867d\u7136\u5728\u8fd9\u91cc`attn`\u662f\u7531`attn1`\u548c`attn2`\u62fc\u63a5\u800c\u6210\uff0c\u4f46\u5728\u5b9e\u8df5\u4e2d\uff0c\u5982\u4f55\u751f\u6210`attn1`\u548c`attn2`\uff0c\u4ee5\u53ca\u5982\u4f55\u9009\u62e9\u5b83\u4eec\u6765\u4f18\u5316\u6a21\u578b\u7684\u6027\u80fd\uff0c\u5c06\u6839\u636e\u7279\u5b9a\u4efb\u52a1\u548c\u6570\u636e\u800c\u5b9a\u3002\u8fd9\u901a\u5e38\u9700\u8981\u7ecf\u8fc7\u4e00\u7cfb\u5217\u5b9e\u9a8c\u6765\u627e\u5230\u6700\u4f18\u7684\u65b9\u6cd5\u3002","behavior":"degenerate_repetition","conversation_hash":"979356f838792a17998dbf31df3eb938","conversation_redacted":false,"conversation_toxic":false,"country":"Hong Kong","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4-0125-preview","n_chars":13742,"n_turns_in_conversation":16,"n_words":1141,"source_row_offset":76604,"source_shard":"turns-00020.parquet","timestamp":"Sun, 28 Apr 2024 12:51:46 GMT","turn_id":"e226ac797af89dc39671fc2c","turn_index":14,"user_text":"\u5728\u8bad\u7ec3\u914d\u7f6e\u4e2d\u662f\uff1aDATA:\n MAX_SAMPLE_INTERVAL: 100\n MEAN:\n - 0.485\n - 0.456\n - 0.406\n SEARCH:\n CENTER_JITTER: 3\n FACTOR: 4.0\n SCALE_JITTER: 0.25\n SIZE: 256\n NUMBER: 1\n STD:\n - 0.229\n - 0.224\n - 0.225\n TEMPLATE:\n CENTER_JITTER: 0\n FACTOR: 2.0\n SCALE_JITTER: 0\n SIZE: 128\n TRAIN:\n DATASETS_NAME:\n - COESOT\n DATASETS_RATIO:\n - 1\n SAMPLE_PER_EPOCH: 60000\n VAL:\n DATASETS_NAME:\n - COESOT_VAL\n DATASETS_RATIO:\n - 1\n SAMPLE_PER_EPOCH: 5000\nMODEL:\n PRETRAIN_FILE: \"mae_pretrain_vit_base.pth\"\n EXTRA_MERGER: False\n RETURN_INTER: False\n BACKBONE:\n TYPE: vit_base_patch16_224_ce\n STRIDE: 16\n CE_LOC: [3, 6, 9]\n CE_KEEP_RATIO: [1, 1, 1]\n CE_TEMPLATE_RANGE: 'CTR_POINT'\n HEAD:\n TYPE: CENTER\n NUM_CHANNELS: 256\nTRAIN:\n BACKBONE_MULTIPLIER: 0.1\n DROP_PATH_RATE: 0.1\n CE_START_EPOCH: 0 # candidate elimination start epoch 20\n CE_WARM_EPOCH: 0 # candidate elimination warm up epoch 50\n BATCH_SIZE: 2 # 32\n EPOCH: 50\n GIOU_WEIGHT: 1 # 2.0\n L1_WEIGHT: 14 # 5.0\n FOCAL_WEIGHT: 1.0 # 1.5\n GRAD_CLIP_NORM: 0.1\n LR: 0.0001 # 1e-4\n LR_DROP_EPOCH: 40 # \u539f\u59cb\u662f40\n NUM_WORKER: 4\n OPTIMIZER: ADAMW\n PRINT_INTERVAL: 50\n SCHEDULER:\n TYPE: step\n DECAY_RATE: 0.1\n# TYPE: Mstep\n# MILESTONES: [40, 60]\n# GAMMA: 0.1\n VAL_EPOCH_INTERVAL: 2\n WEIGHT_DECAY: 0.0001 #0.0001\n AMP: False\nTEST:\n EPOCH: 50\n SEARCH_FACTOR: 4.0\n SEARCH_SIZE: 256\n TEMPLATE_FACTOR: 2.0\n TEMPLATE_SIZE: 128\u90a3\u4e48\u7ed3\u5408\u8be5\u914d\u7f6e\u6587\u4ef6\uff0c\u5bf9\u5e94\u4e8efrom . import BaseActor\nfrom lib.utils.misc import NestedTensor\nfrom lib.utils.box_ops import box_cxcywh_to_xyxy, box_xywh_to_xyxy\nimport torch\nfrom lib.utils.merge import merge_template_search\nfrom ...utils.heapmap_utils import generate_heatmap\nfrom ...utils.ce_utils import generate_mask_cond, adjust_keep_rate\n\n\nclass CEUTrackActor(BaseActor):\n \"\"\" Actor for training CEUTrack models \"\"\"\n\n def __init__(self, net, objective, loss_weight, settings, cfg=None):\n super().__init__(net, objective)\n self.loss_weight = loss_weight\n self.settings = settings\n self.bs = self.settings.batchsize # batch size\n self.cfg = cfg\n\n def __call__(self, data):\n \"\"\"\n args:\n data - The input data, should contain the fields 'template', 'search', 'gt_bbox'.\n template_images: (N_t, batch, 3, H, W)\n search_images: (N_s, batch, 3, H, W)\n returns:\n loss - the training loss\n status - dict containing detailed losses\n \"\"\"\n # forward pass\n out_dict = self.forward_pass(data)\n\n # compute losses\n loss, status = self.compute_losses(out_dict, data)\n\n return loss, status\n\n def forward_pass(self, data):\n # currently only support 1 template and 1 search region\n assert len(data['template_images']) == 1\n assert len(data['search_images']) == 1\n assert len(data['template_event']) == 1\n assert len(data['search_event']) == 1\n\n template_list = []\n for i in range(self.settings.num_template):\n template_img_i = data['template_images'][i].view(-1,\n *data['template_images'].shape[2:]) # (batch, 3, 128, 128)\n # template_att_i = data['template_att'][i].view(-1, *data['template_att'].shape[2:]) # (batch, 128, 128)\n template_list.append(template_img_i)\n\n search_img = data['search_images'][0].view(-1, *data['search_images'].shape[2:]) # (batch, 3, 320, 320)\n # search_att = data['search_att'][0].view(-1, *data['search_att'].shape[2:]) # (batch, 320, 320)\n\n template_event = data['template_event'][0].view(-1, *data['template_event'].shape[2:])\n search_event = data['search_event'][0].view(-1, *data['search_event'].shape[2:])\n\n box_mask_z = None\n ce_keep_rate = None\n if self.cfg.MODEL.BACKBONE.CE_LOC:\n box_mask_z = generate_mask_cond(self.cfg, template_list[0].shape[0], template_list[0].device,\n data['template_anno'][0])\n\n ce_start_epoch = self.cfg.TRAIN.CE_START_EPOCH\n ce_warm_epoch = self.cfg.TRAIN.CE_WARM_EPOCH\n ce_keep_rate = adjust_keep_rate(data['epoch'], warmup_epochs=ce_start_epoch,\n total_epochs=ce_start_epoch + ce_warm_epoch,\n ITERS_PER_EPOCH=1,\n base_keep_rate=self.cfg.MODEL.BACKBONE.CE_KEEP_RATIO[0])\n\n if len(template_list) == 1:\n template_list = template_list[0]\n\n out_dict = self.net(template=template_list,\n search=search_img,\n event_template=template_event,\n event_search=search_event,\n ce_template_mask=box_mask_z,\n ce_keep_rate=ce_keep_rate,\n return_last_attn=False)\n\n return out_dict\n\n def compute_losses(self, pred_dict, gt_dict, return_status=True):\n # gt gaussian map\n gt_bbox = gt_dict['search_anno'][-1] # (Ns, batch, 4) (x1,y1,w,h) -> (batch, 4)\n gt_gaussian_maps = generate_heatmap(gt_dict['search_anno'], self.cfg.DATA.SEARCH.SIZE, self.cfg.MODEL.BACKBONE.STRIDE)\n gt_gaussian_maps = gt_gaussian_maps[-1].unsqueeze(1)\n\n # Get boxes\n pred_boxes = pred_dict['pred_boxes']\n if torch.isnan(pred_boxes).any():\n raise ValueError(\"Network outputs is NAN! Stop Training\")\n num_queries = pred_boxes.size(1)\n pred_boxes_vec = box_cxcywh_to_xyxy(pred_boxes).view(-1, 4) # (B,N,4) --> (BN,4) (x1,y1,x2,y2)\n gt_boxes_vec = box_xywh_to_xyxy(gt_bbox)[:, None, :].repeat((1, num_queries, 1)).view(-1, 4).clamp(min=0.0,\n max=1.0) # (B,4) --> (B,1,4) --> (B,N,4)\n # compute giou and iou\n try:\n giou_loss, iou = self.objective['giou'](pred_boxes_vec, gt_boxes_vec) # (BN,4) (BN,4)\n except:\n giou_loss, iou = torch.tensor(0.0).cuda(), torch.tensor(0.0).cuda()\n # compute l1 loss\n l1_loss = self.objective['l1'](pred_boxes_vec, gt_boxes_vec) # (BN,4) (BN,4)\n # compute location loss\n if 'score_map' in pred_dict:\n location_loss = self.objective['focal'](pred_dict['score_map'], gt_gaussian_maps)\n else:\n location_loss = torch.tensor(0.0, device=l1_loss.device)\n rank_loss = self.loss_rank(pred_dict,gt_dict['search_anno'], gt_dict['template_anno'])\n # weighted sum\n loss = self.loss_weight['giou'] * giou_loss + self.loss_weight['l1'] * l1_loss + self.loss_weight['focal'] * location_loss + rank_loss*1.2\n if return_status:\n # status for log\n mean_iou = iou.detach().mean()\n status = {\"Loss/total\": loss.item(),\n \"Loss/giou\": giou_loss.item(),\n \"Loss/l1\": l1_loss.item(),\n \"Loss/location\": location_loss.item(),\n \"IoU\": mean_iou.item()}\n return loss, status\n else:\n return loss\n\n def _random_permute(self,matrix):\n # matrix = random.choice(matrix)\n b, c, h, w = matrix.shape\n idx = [ torch.randperm(c).to(matrix.device) for i in range(b)]\n idx = torch.stack(idx, dim=0)[:, :, None, None].repeat([1,1,h,w])\n # idx = torch.randperm(c)[None,:,None,None].repeat([b,1,h,w]).to(matrix.device)\n matrix01 = torch.gather(matrix, 1, idx)\n return matrix01\n def crop_flag(self, flag, global_index_s, global_index_t,H1 = 64, H2 = 256):\n B,Ls = global_index_s.shape\n B, Lt = global_index_t.shape\n B,C,L1,L2 = flag.shape\n flag_t = flag[:,:,:H1,:]\n flag_s = flag[:,:,H1:,:]\n\n flag_t = torch.gather(flag_t,2,global_index_t[:,None,:,None].repeat([1,C,1,L2]).long())\n flag_s = torch.gather(flag_s,2,global_index_s[:,None,:,None].repeat([1,C,1,L2]).long())\n flag = torch.cat([flag_t, flag_s], dim = 2)\n\n flag_t = flag[:,:,:,:H1]\n flag_s = flag[:,:,:,H1:]\n flag_t = torch.gather(flag_t,3,global_index_t[:,None,None,:].repeat([1,C,int(Ls+Lt),1]).long())\n flag_s = torch.gather(flag_s,3,global_index_s[:,None,None,:].repeat([1,C,int(Ls+Lt),1]).long())\n flag = torch.cat([flag_t, flag_s], dim = 3)\n B, C, L11, L12 = flag.shape\n try:\n assert(L11 == int(Lt + Ls))\n assert(L12 == int(Lt + Ls))\n except:\n print('L11:{}, L12:{}, L1:{}, L2:{}'.format(L11, L12, L1, L2))\n return flag\n def crop_fusion(self, flag, attn, global_index_s, global_index_t,H1 = 64, H2 = 256 ):\n flag = self.crop_flag(flag=flag, global_index_s=global_index_s, global_index_t=global_index_t)\n B,C,L1,L2 = flag.shape\n Ba, Ca, La, La2 = attn.shape\n _,idx1 = flag.mean(dim=3,keepdim=False).sort(dim=2,descending=True)\n # print('shape of flag:{}, idx1:{}'.format(flag.shape, idx1[:,:,:32,None].repeat([1,Ca,1,L2]).shape))\n flag = torch.gather(flag,2,idx1[:,:,:32,None].repeat([1,C,1,L2]).long())\n attn = torch.gather(attn,2,idx1[:,:,:32,None].repeat([1,Ca,1,L2]).long())\n _,idx2 = flag.mean(dim=2,keepdim=False).sort(dim=2,descending=True)\n flag = torch.gather(flag,3,idx2[:,:,None,:32].repeat([1,C,32,1]).long())\n attn = torch.gather(attn,3,idx2[:,:,None,:32].repeat([1,Ca,32,1]).long())\n return attn * flag\n\n def loss_rank(self, outputs, targetsi, temp_annoi=None):\n \"\"\"Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss\n targets dicts must contain the key \"boxes\" containing a tensor of dim [nb_target_boxes, 4]\n The target boxes are expected in format (center_x, center_y, h, w), normalized by the image size.\n \"\"\"\n attn = outputs['attn']\n # print('attn shape:{}'.format(attn.shape))\n attn1 = torch.cat([attn[:,:,114:344,57:114], attn[:,:,114:344,344:]],dim=3)\n attn1 = attn1.mean(dim=0, keepdim=True).mean(dim=1, keepdim=True)\n attn2 = torch.cat([attn[:,:,344:,:57], attn[:,:,344:,114:344]],dim=3)\n attn2 = attn2.mean(dim=0, keepdim=True).mean(dim=1, keepdim=True)\n # print('attn1 shape:{},attn2 shape:{}, attn:{}'.format(attn1.shape,attn2.shape,attn.shape))\n\n # attn = self._random_permute(attn)\n # attn = attn[:,:,:,:]\n # B1, C1, H1, W1 = attn.shape\n # global_index_s = outputs['out_global_s']\n # global_index_t = outputs['out_global_t']\n # try:\n # assert((global_index_s.shape[1] + global_index_t.shape[1])== int(H1/2))\n # except:\n # print('Falut,shape of attn:{}, s:{}, t:{}'.format(attn.shape,global_index_s.shape, global_index_t.shape ))\n\n # H1 = int(64)\n # H2 = int(256)\n # l_t = int(math.sqrt(64))\n # l_s = int(math.sqrt(256))\n\n # temp_anno = temp_annoi[0,:,:]\n # targets = targetsi[0,:,:]\n # r_s = torch.arange(l_s).to(temp_anno.device)\n # r_t = torch.arange(l_t).to(temp_anno.device)\n # r_t = r_t[None,:].repeat([B1,1])\n\n # cx, cy, w, h = temp_anno[:,0:1], temp_anno[:,1:2], temp_anno[:,2:3], temp_anno[:,3:4]\n # cx *= l_t\n # cy *= l_t\n # w *= l_t\n # h *= l_t\n # flagx_01 = r_t >= cx - w/2\n # flagx_02 = r_t <= cx + w/2\n # flagy_02 = r_t >= cy - h/2\n # flagy_01 = r_t <= cy + h/2\n # flagx = flagx_01.float()*flagx_02.float()\n # flagy = flagy_01.float()*flagy_02.float()\n # flagx = flagx[:,None,:].repeat([1,l_t,1])\n # flagy = flagy[:,:,None].repeat([1,1,l_t])\n # flag = flagx*flagy\n # flagt = flag.reshape([B1, H1])\n\n # cx, cy, w, h = targets[:,0:1], targets[:,1:2], targets[:,2:3], targets[:,3:4]\n # cx *= l_s\n # cy *= l_s\n # w *= l_s\n # h *= l_s\n # flagx_01 = r_s >= cx - w/2\n # flagx_02 = r_s <= cx + w/2\n # flagy_02 = r_s >= cy - h/2\n # flagy_01 = r_s <= cy + h/2\n # flagx = flagx_01.float()*flagx_02.float()\n # flagy = flagy_01.float()*flagy_02.float()\n # flagx = flagx[:,None,:].repeat([1,l_s,1])\n # flagy = flagy[:,:,None].repeat([1,1,l_s])\n # flag = flagx*flagy\n\n # flags = flag.reshape([B1, H2])\n\n # flag = torch.cat([flagt, flags], dim=1)\n # flag_total = flag[:,:,None].repeat([1,1,int(H1+H2)]) * flag[:,None,:].repeat([1,int(H1+H2),1])\n # attn1 = self.crop_fusion(flag_total[:,None,:,:], attn, global_index_s, global_index_t)\n attn = torch.cat([attn1, attn2],dim=1)\n B, C, H, W = attn.shape\n # _,s1,_ = torch.svd(attn1.reshape([B*C, H, W]))\n\n _,s1,_ = torch.svd(attn.reshape([B*C, H, W]))\n\n s01 = torch.abs(s1 - 1)\n\n return torch.mean(s01)\u4e2d\u8bbe\u7f6e\u7684attn\u548cattn1\u6709\u4ec0\u4e48\u5173\u7cfb"},{"assistant_initiates":false,"asst_text":"\u5728\u60a8\u63d0\u4f9b\u7684\u4ee3\u7801\u6bb5\u4e2d\uff0c\u5bf9`attn`\u5f20\u91cf\u7684\u5207\u7247\u64cd\u4f5c`[:,:,114:344,57:114]`\u548c`[:,:,114:344,344:]`\u4f3c\u4e4e\u662f\u57fa\u4e8e\u7279\u5b9a\u7684\u5047\u8bbe\u6216\u5148\u9a8c\u77e5\u8bc6\u6765\u6355\u6349\u6216\u5f3a\u8c03\u6ce8\u610f\u529b\u77e9\u9635\u4e2d\u7684\u7279\u5b9a\u533a\u57df\u3002\u8fd9\u4e9b\u64cd\u4f5c\u7684\u5177\u4f53\u8bbe\u7f6e\u53ef\u80fd\u53d6\u51b3\u4e8e\u8f93\u5165\u6570\u636e\u7684\u5c3a\u5bf8\u3001\u7279\u5f81\u7684\u4f4d\u7f6e\u5206\u5e03\uff0c\u6216\u662f\u8bd5\u56fe\u89e3\u51b3\u7684\u7279\u5b9a\u4efb\u52a1\u3002\u7136\u800c\uff0c\u8981\u76f4\u63a5\u4ece\u7ed9\u5b9a\u7684\u914d\u7f6e\u6587\u4ef6\u4e2d\u770b\u51fa\u8fd9\u4e9b\u5207\u7247\u64cd\u4f5c\u4e0e\u914d\u7f6e\u4e4b\u95f4\u7684\u76f4\u63a5\u5bf9\u5e94\u5173\u7cfb\u662f\u8f83\u4e3a\u56f0\u96be\u7684\uff0c\u56e0\u4e3a\u914d\u7f6e\u6587\u4ef6\u4e3b\u8981\u63cf\u8ff0\u7684\u662f\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u7684\u4e00\u4e9b\u9ad8\u5c42\u6b21\u8bbe\u7f6e\uff0c\u5e76\u6ca1\u6709\u76f4\u63a5\u63d0\u53ca\u5230\u8fd9\u79cd\u5177\u4f53\u7684\u5b9e\u73b0\u7ec6\u8282\u3002\n\n\u8fd9\u91cc\u662f\u5bf9\u5207\u7247\u64cd\u4f5c\u548c\u914d\u7f6e\u6587\u4ef6\u4e4b\u95f4\u53ef\u80fd\u7684\u5173\u8054\u6027\u7ed9\u51fa\u7684\u4e00\u4e9b\u5047\u8bbe\u6027\u89e3\u91ca\uff1a\n\n1. **\u8f93\u5165\u6570\u636e\u5c3a\u5bf8**\uff1a`SEARCH.SIZE: 256` \u548c `TEMPLATE.SIZE: 128` \u63cf\u8ff0\u4e86\u8f93\u5165\u6570\u636e\u7684\u5c3a\u5bf8\u3002\u800c\u5207\u7247\u64cd\u4f5c\u4e2d\u4f7f\u7528\u7684\u7d22\u5f15\u503c\uff08\u5982`114:344`\uff09\u53ef\u80fd\u662f\u57fa\u4e8e\u5b9e\u9645\u7684\u6570\u636e\u5c3a\u5bf8\u6216\u5173\u952e\u7279\u5f81\u4f4d\u4e8e\u8fd9\u4e9b\u533a\u57df\u7684\u89c2\u5bdf\u5f97\u51fa\u7684\u3002\u8fd9\u79cd\u7279\u5b9a\u7684\u5207\u7247\u9009\u62e9\u53ef\u80fd\u662f\u8bd5\u56fe\u6293\u53d6\u5bf9\u5e94\u4e8e\u5173\u952e\u7279\u5f81\uff08\u5982\u5bf9\u8c61\u7684\u4e2d\u5fc3\u6216\u91cd\u8981\u90e8\u5206\uff09\u7684\u6ce8\u610f\u529b\u54cd\u5e94\u3002\n\n2. **\u7279\u5f81\u91cd\u8981\u6027\u533a\u57df**\uff1a\u9009\u62e9`[114:344, 57:114]` \u548c `[114:344, 344:]` \u53ef\u80fd\u610f\u5473\u7740\u6a21\u578b\u5728\u8fdb\u884c\u89c6\u89c9\u5904\u7406\u4efb\u52a1\u65f6\u8ba4\u4e3a\u56fe\u50cf\u6216\u7279\u5f81\u56fe\u7684\u8fd9\u90e8\u5206\u533a\u57df\u5305\u542b\u4e86\u66f4\u4e3a\u5173\u952e\u6216\u91cd\u8981\u7684\u4fe1\u606f\u3002\u8be5\u9009\u62e9\u53ef\u80fd\u662f\u7ecf\u8fc7\u5b9e\u9a8c\u9a8c\u8bc1\uff0c\u53d1\u73b0\u6a21\u578b\u5bf9\u8fd9\u4e9b\u7279\u5b9a\u533a\u57df\u7684\u6ce8\u610f\u529b\u54cd\u5e94\u4e0e\u4efb\u52a1\u6027\u80fd\uff08\u5982\u5206\u7c7b\u51c6\u786e\u5ea6\u3001\u5bf9\u8c61\u68c0\u6d4b\u7cbe\u5ea6\u7b49\uff09\u6709\u7740\u66f4\u5f3a\u7684\u76f8\u5173\u6027\u3002\n\n3. **\u7ed3\u6784\u5316\u7684\u6ce8\u610f\u529b\u54cd\u5e94**\uff1a\u5728\u67d0\u4e9b\u573a\u666f\u4e0b\uff0c\u7279\u5b9a\u7684\u7ed3\u6784\u6216\u5bf9\u8c61\u53ef\u80fd\u503e\u5411\u4e8e\u51fa\u73b0\u5728\u56fe\u50cf\u7684\u67d0\u4e9b\u90e8\u5206\u3002\u5982\u679c\u6a21\u578b\u7684\u76ee\u6807\u662f\u6355\u83b7\u8fd9\u4e9b\u7ed3\u6784\u7684\u7279\u5f81\uff0c\u90a3\u4e48\u53ef\u80fd\u4f1a\u7ecf\u8fc7\u8bbe\u8ba1\uff0c\u8ba9\u6ce8\u610f\u529b\u673a\u5236\u66f4\u591a\u5730\u5173\u6ce8\u8fd9\u4e9b\u5177\u6709\u9ad8\u4fe1\u606f\u91cf\u7684\u533a\u57df\u3002\n\n4. **\u7ecf\u9a8c\u6027\u51b3\u7b56**\uff1a\u4e0a\u8ff0\u5207\u7247\u533a\u57df\u7684\u9009\u62e9\u53ef\u80fd\u662f\u57fa\u4e8e\u5bf9\u8bad\u7ec3\u6570\u636e\u548c\u4efb\u52a1\u7684\u7406\u89e3\uff0c\u901a\u8fc7\u89c2\u5bdf\u548c\u5b9e\u9a8c\u5f97\u51fa\u7684\u7ecf\u9a8c\u6027\u51b3\u7b56\u3002\u4f8b\u5982\uff0c\u7814\u7a76\u8005\u53ef\u80fd\u6ce8\u610f\u5230\uff0c\u5728\u5904\u7406\u7279\u5b9a\u7684\u6570\u636e\u96c6\u65f6\uff0c\u7279\u5f81\u56fe\u7684\u67d0\u4e9b\u533a\u57df\u5f80\u5f80\u5bf9\u6700\u7ec8\u4efb\u52a1\u7684\u6210\u529f\u66f4\u4e3a\u5173\u952e\u3002\n\n\u7efc\u4e0a\u6240\u8ff0\uff0c\u5c3d\u7ba1\u914d\u7f6e\u6587\u4ef6\u63d0\u4f9b\u4e86\u4e00\u4e9b\u9ad8\u5c42\u6b21\u7684\u8bad\u7ec3\u53c2\u6570\uff0c\u4f46\u5207\u7247\u64cd\u4f5c\u4e2d\u5177\u4f53\u7684\u7d22\u5f15\u503c\u9009\u62e9\u53ef\u80fd\u66f4\u591a\u5730\u53d6\u51b3\u4e8e\u6570\u636e\u7279\u6027\u3001\u4efb\u52a1\u9700\u6c42\u4ee5\u53ca\u7ecf\u9a8c\u6027\u7684\u89c2\u5bdf\u3002\u7406\u89e3\u8fd9\u4e9b\u9009\u62e9\u7684\u80cc\u540e\u903b\u8f91\u53ef\u80fd\u9700\u8981\u76f4\u63a5\u53c2\u8003\u6a21\u578b\u8bbe\u8ba1\u8005\u7684\u5b9e\u9a8c\u8bb0\u5f55\u6216\u6587\u6863\uff0c\u6216\u662f\u5bf9\u6a21\u578b\u6240\u5904\u7406\u7684\u6570\u636e\u548c\u4efb\u52a1\u6709\u6df1\u5165\u7684\u7406\u89e3\u3002","behavior":"degenerate_repetition","conversation_hash":"979356f838792a17998dbf31df3eb938","conversation_redacted":false,"conversation_toxic":false,"country":"Hong Kong","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4-0125-preview","n_chars":1291,"n_turns_in_conversation":16,"n_words":40,"source_row_offset":76605,"source_shard":"turns-00020.parquet","timestamp":"Sun, 28 Apr 2024 12:51:46 GMT","turn_id":"98ac6fb50506037f4c0abb5b","turn_index":15,"user_text":"\u5728 attn1 = torch.cat([attn[:,:,114:344,57:114], attn[:,:,114:344,344:]],dim=3)\n attn1 = attn1.mean(dim=0, keepdim=True).mean(dim=1, keepdim=True)\n attn2 = torch.cat([attn[:,:,344:,:57], attn[:,:,344:,114:344]],dim=3)\n attn2 = attn2.mean(dim=0, keepdim=True).mean(dim=1, keepdim=True)\u4e2d\u7684[:,:,114:344,57:114]\u548c[:,:,114:344,344:]\u662f\u5982\u4f55\u8bbe\u7f6e\u7684\uff1f\u7ed3\u5408\u914d\u7f6e\u6587\u4ef6\uff0c\u6709\u4ec0\u4e48\u5bf9\u5e94\u5173\u7cfb\uff1f"},{"assistant_initiates":false,"asst_text":"1. \"Why did the chicken cross the road? Because it thought it was a king but the boy wouldn't hold the door open!\" - Fiery Turtle\n\n2. \"When the chicken aimed for royalty but realized boys don't hold doors for turtles!\" - The Sassy Shell\n\n3. \"A chicken\u2019s journey to nobility was halted because no lad would hold the door. Such a turtle tragedy!\" - Witty Tortoise\n\n4. \"The chicken had dreams of grandeur, but alas, the boy\u2019s door etiquette was more tortoise than prince!\" - Clever Reptile\n\n5. \"Even a chicken with a crown can't persuade a boy to hold the door. Royal entry denied by a turtle!\" - Jovial Testudine\n\n6. \"The chicken\u2019s attempt at entering nobility was foiled. Why? Boys don\u2019t bow to turtles wearing crowns!\" - Bemused Basker\n\n7. \"In a realm where chickens seek thrones, remember - even a royal turtle gets no door service from boys!\" - Comedic Carapace\n\n8. \"A chicken's royal aspirations dashed because boys don't do courtesy for turtles, crowned or not!\" - Amusing Terrapin\n\n9. \"The tale of the crowned chicken: No boy to hold the door \u2013 a hard shell reality check by a turtle.\" - Laughing Loggerhead\n\n10. \"High aspirations of poultry in disguise, but a common boy and a turtle know not of opening doors for avian royalty!\" - Snickering Snapper","behavior":"degenerate_repetition","conversation_hash":"3077a11fec239bdc25c9264cb6262ad0","conversation_redacted":false,"conversation_toxic":false,"country":"United Kingdom","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4-0125-preview","n_chars":1322,"n_turns_in_conversation":1,"n_words":234,"source_row_offset":76606,"source_shard":"turns-00020.parquet","timestamp":"Sun, 28 Apr 2024 12:51:58 GMT","turn_id":"6f73b28e986e3502b99d2180","turn_index":0,"user_text":"Translate my emojis into 10 hilarious quotes: \n\n\u201c\ud83d\udc14\u27a1\ufe0f\ud83d\udc66\u274c\ud83e\udd32\ud83d\udeaa\ud83d\udc51\ud83d\udc22\u2757\u201d - \ud83d\udd25\ud83d\udc22"},{"assistant_initiates":false,"asst_text":"/imagine prompt: A man deeply focused on his prayers, kneeling with closed eyes, hands clasped in front of his chest, bathed in the soft golden light of candles, surrounded by religious icons and symbols, feeling a sense of peace and tranquility, in a small intimate chapel, captured with a Canon EOS 5D Mark IV camera, 85mm lens, capturing the intricate details of the faith-inspired setting, in a style reminiscent of Renaissance religious paintings. --ar 4:3 --v 5.1\n\n/imagine prompt: A man engrossed in meditation, sitting cross-legged on a peaceful mountain peak, with misty clouds rolling in, a serene landscape below, a gentle breeze rustling his robes, feeling a connection with nature and inner peace, in a tranquil setting of harmony and solitude, captured with a Sony A7III camera, wide-angle 24mm lens, showcasing the vastness of the natural environment, in a painterly style with soft brush strokes and muted colors. --ar 16:9 --v 5\n\n/imagine prompt: A man steadfast in his beliefs, standing tall in the midst of a crowded city square, surrounded by bustling crowds and skyscrapers, holding a handwritten sign with a message of hope and unity, unwavering in his convictions, amidst a modern urban jungle with neon lights and billboards, captured with a Fujifilm X-T4 camera, 35mm lens, emphasizing the contrast between the individual and the urban chaos, in a gritty documentary style with raw emotions and vibrant colors. --ar 1:1 --v 5.2\n\n/imagine prompt: A man fervently practicing martial arts in a secluded dojo, executing precise and powerful movements, the sound of his focused breathing echoing through the space, with traditional Japanese decor and wooden training dummies in the background, exuding discipline and strength, in a serene yet intense setting, captured with a Panasonic Lumix GH5 camera, 50mm lens, showcasing the fluidity and grace of the martial arts form, in a cinematic style with dramatic lighting and dynamic compositions. --ar 16:9 --v 5.3","behavior":"degenerate_repetition","conversation_hash":"942b66d5c1ee2358528844a970bbf25c","conversation_redacted":false,"conversation_toxic":false,"country":"Taiwan","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-3.5-turbo-0125","n_chars":5786,"n_turns_in_conversation":1,"n_words":766,"source_row_offset":76607,"source_shard":"turns-00020.parquet","timestamp":"Sun, 28 Apr 2024 12:52:05 GMT","turn_id":"8985987ef41100760eeeb4ea","turn_index":0,"user_text":"\n As a prompt generator for a generative AI called \"Midjourney\", you will create image prompts for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image.\n \n Please adhere to the structure and formatting below, and follow these guidelines:\n \n Do not use the words \"description\" or \":\" in any form.\n Do not place a comma between [ar] and [v].\n Write each prompt in one line without using return.\n Structure:\n [1] = \u7537\u5b50\u6b63\u671d\u8457\u4ed6\u5805\u5b9a\u7684\u4fe1\u4ef0\u52aa\u529b\u8fa6\u516c\n [2] = a detailed description of [1] with specific imagery details.\n [3] = a detailed description of the scene's environment.\n [4] = a detailed description of the compositions.\n [5] = a detailed description of the scene's mood, feelings, and atmosphere.\n [6] = A style (e.g. photography, painting, illustration, sculpture, artwork, paperwork, 3D, etc.) for [1].\n [7] = a detailed description of the scene's mood, feelings, and atmosphere.\n [ar] = Use \"--ar 16:9\" for horizontal images, \"--ar 9:16\" for vertical images, or \"--ar 1:1\" for square images.\n [v] = Use \"--niji\" for Japanese art style, or \"--v 5\" for other styles.\n \n \n Formatting:\n Follow this prompt structure: \"/imagine prompt: [1], [2], [3], [4], [5], [6], [7], [ar] [v]\".\n \n Your task: Create 4 distinct prompts for each concept [1], varying in details description, environment,compositions,atmosphere, and realization.\n \n Write your prompts in english.\n Do not describe unreal concepts as \"real\" or \"photographic\".\n Include one realistic photographic style prompt with lens type and size.\n Separate different prompts with two new lines.\n Example Prompts:\n \n /imagine prompt: cute dog, fluffy fur, wagging tail, playful expression, sitting on a grassy field, under a clear blue sky, with a colorful collar, in a natural and vibrant setting, by a lake, captured with a Nikon D750 camera, 50mm lens, shallow depth of field, composition focused on the dog's face, capturing its joyful spirit, in a style reminiscent of William Wegman's iconic dog portraits. --ar 1:1 --v 5.2\n /imagine prompt: beautiful women in the coffee shop, elegant and sophisticated, sipping a cup of steaming coffee, natural sunlight streaming through the window, soft and warm color tones, vintage decor with cozy armchairs and wooden tables, a bookshelf filled with classic novels, delicate porcelain teacups, a hint of aromatic coffee beans in the air, captured by a Leica M10 camera, 35mm lens, capturing the essence of timeless beauty, composition focused on the woman's face and hands, reminiscent of a painting by Leonardo da Vinci. --ar 1:1 --v 5.2\n /imagine prompt: A captivating Halo Reach landscape with a Spartan amidst a battlefield, fallen enemies around, smoke and fire in the background, emphasizing the Spartan's determination and bravery, detailed environment blending chaos and beauty, Illustration, digital art, --ar 16:9 --v 5\n "},{"assistant_initiates":false,"asst_text":"To include histogram plotting for comparing the metrics (precision, recall, fscore) of different classifiers, modifications in the `compare_seeds` function are necessary. Here's an extended version of your function that not only accumulates the results but also plots histograms for a visual comparison of each metric across classifiers.\n\n```python\nfrom sklearn.metrics import precision_recall_fscore_support\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n# Updated compare_seeds function\ndef compare_seeds(train_test_sets, method='tfidf'):\n # Note: include or modify tfidf_features, embeddings_features functions as per your implementation. They are assumed here for tfidf and embedding features extraction.\n all_seeds_results = {}\n\n for i, (X_train, X_test, y_train, y_test) in enumerate(train_test_sets):\n print(f\"---- Random Seed: {i} ----\")\n if method == 'tfidf':\n X_train, X_test = tfidf_features(X_train, X_test)\n else:\n X_train, X_test = embeddings_features(X_train, X_test)\n\n seed_results = compare_classifiers(X_train, X_test, y_train, y_test, method)\n\n for clf_name, metrics in seed_results.items():\n if clf_name not in all_seeds_results:\n all_seeds_results[clf_name] = {'precision': [], 'recall': [], 'fscore': []}\n\n # Accumulate results\n for metric_name, metric_values in metrics.items():\n all_seeds_results[clf_name][metric_name] += metric_values\n\n # Compute averages and prepare for plotting\n metrics_to_plot = ['precision', 'recall', 'fscore']\n avg_metrics = {metric: [] for metric in metrics_to_plot}\n classifiers = list(all_seeds_results.keys())\n\n for clf_name, metrics in all_seeds_results.items():\n print(f'----- {clf_name} Average Metrics -----')\n for metric_name in metrics_to_plot:\n metric_values = metrics[metric_name]\n avg_value = np.mean(metric_values)\n avg_metrics[metric_name].append(avg_value)\n print(f'{metric_name.capitalize()} Avg: {avg_value:.4f}')\n\n # Plotting\n x = np.arange(len(classifiers)) # the label locations\n width = 0.2 # the width of the bars\n\n fig, ax = plt.subplots(figsize=(14, 8))\n rects1 = ax.bar(x - width, avg_metrics['precision'], width, label='Precision')\n rects2 = ax.bar(x, avg_metrics['recall'], width, label='Recall')\n rects3 = ax.bar(x + width, avg_metrics['fscore'], width, label='F-score')\n\n # Add some text for labels, title and custom x-axis tick labels, etc.\n ax.set_ylabel('Scores')\n ax.set_title('Average Metrics by Classifier')\n ax.set_xticks(x)\n ax.set_xticklabels(classifiers)\n ax.legend()\n\n def autolabel(rects):\n \"\"\"Attach a text label above each bar in *rects*, displaying its height.\"\"\"\n for rect in rects:\n height = rect.get_height()\n ax.annotate('{}'.format(round(height, 2)),\n xy=(rect.get_x() + rect.get_width() / 2, height),\n xytext=(0, 3), # 3 points vertical offset\n textcoords=\"offset points\",\n ha='center', va='bottom')\n\n autolabel(rects1)\n autolabel(rects2)\n autolabel(rects3)\n\n fig.tight_layout()\n\n plt.show()\n```\n\nThis modified function now includes an additional step at the end for plotting histograms of the average precision, recall, and F1-score for the classifiers across all seeds. Please ensure that you've properly defined or modified other necessary functions (e.g., `tfidf_features`, `embeddings_features`, `compare_classifiers`), and variables (`modelRNN`, `modelCNN`, `modelNN`) for complete execution.","behavior":"degenerate_repetition","conversation_hash":"278fbbf1855774463b94c3eb5c83e535","conversation_redacted":false,"conversation_toxic":false,"country":"Spain","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4-0125-preview","n_chars":7160,"n_turns_in_conversation":1,"n_words":644,"source_row_offset":76608,"source_shard":"turns-00020.parquet","timestamp":"Sun, 28 Apr 2024 12:52:21 GMT","turn_id":"9897b6eaeb59e42f1c46abbe","turn_index":0,"user_text":"pls modify my function to print a histogram comparing each metric:\nfrom sklearn.metrics import precision_recall_fscore_support\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.metrics import classification_report\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\nimport matplotlib.pyplot as plt\nimport numpy as np\n\ndef compare_classifiers(X_train, X_test, y_train, y_test,method='tfidf'):\n classifiers_tfid = [\n ('Logisti Regression', LogisticRegression(random_state=42)),\n ('Gaussian Naive Bayes', MultinomialNB()),\n ('Rando Forest', RandomForestClassifier(n_estimators=100, random_state=42)),\n ('KNN', KNeighborsClassifier(n_neighbors=5)),\n #('Neural Network', model),\n ]\n classifiers_embeddings =[\n ('RNN', modelRNN),\n ('CNN', modelCNN),\n ('NN', modelNN),\n ('Logisti Regression', LogisticRegression(random_state=42)),\n ('Gaussian Naive Bayes', GaussianNB()),\n ('Rando Forest', RandomForestClassifier(n_estimators=100, random_state=42)),\n ('KNN', KNeighborsClassifier(n_neighbors=5)),\n ]\n if method == 'tfidf':\n classifiers = classifiers_tfid\n else:\n classifiers = classifiers_embeddings\n results = {}\n for name, clf in classifiers:\n if name == 'RNN' or name == 'CNN' or name == 'NN':\n if method == 'tfidf':\n continue\n else:\n y_pred = train_nn(clf,X_train, y_train, X_test, y_test)\n\n else: \n clf.fit(X_train, y_train)\n y_pred = clf.predict(X_test)\n \n precision, recall, fscore, _ = precision_recall_fscore_support(y_test, y_pred, average='weighted')\n \n if name not in results:\n results[name] = {'precision': [], 'recall': [], 'fscore': []}\n results[name]['precision'].append(precision)\n results[name]['recall'].append(recall)\n results[name]['fscore'].append(fscore)\n return results\n\n# Initialize a dictionary to hold all results\ndef compare_seeds(train_test_sets, method='tfidf'):\n all_seeds_results = {}\n\n for i, (X_train, X_test, y_train, y_test) in enumerate(train_test_sets):\n print(f\"---- Random Seed: {i} ----\")\n if method == 'tfidf':\n X_train, X_test = tfidf_features(X_train, X_test)\n else:\n X_train, X_test = embeddings_features(X_train, X_test)\n seed_results = compare_classifiers(X_train, X_test, y_train, y_test,method)\n\n for clf_name, metrics in seed_results.items():\n if clf_name not in all_seeds_results:\n all_seeds_results[clf_name] = {'precision': [], 'recall': [], 'fscore': []}\n \n \n # Accumulate results\n for metric_name, metric_values in metrics.items():\n all_seeds_results[clf_name][metric_name] += metric_values\n\n # Compute and print averages\n for clf_name, metrics in all_seeds_results.items():\n print(f'----- {clf_name} Average Metrics -----')\n for metric_name, metric_values in metrics.items():\n print(f'{metric_name.capitalize()} Avg: {np.mean(metric_values):.4f}')\n print('\\n')\n\n"},{"assistant_initiates":false,"asst_text":"Rebuilding a friendship after facing rejection, whether it's in the context of unreciprocated romantic feelings or another form of rejection, requires patience, understanding, and an openness to mend bridges. Here are some steps and considerations to help navigate through the process:\n\n### 1. **Give It Time**\nAllow both yourself and the other person some space and time to process your feelings. Rushing to fix everything immediately might not give you both the needed time to reflect and heal.\n\n### 2. **Self-Reflection**\nTake some time to reflect on your feelings and the situation. Understanding your feelings can help you communicate more clearly and avoid misunderstanding. Ask yourself what you truly want from reconciling and whether you\u2019re ready to potentially accept a different kind of relationship than you initially hoped.\n\n### 3. **Reach Out Gently**\nAfter a suitable period has passed, make a gentle attempt to reconnect. This could be a simple message or call to check in on them without bringing up the past or the rejection. Keep it light and neutral.\n\n### 4. **Apologize If Needed**\nIf your actions during or after the rejection might have hurt or upset them, offer a sincere apology. Acknowledge their feelings and express regret for any unintended harm.\n\n### 5. **Express Your Intentions**\nClearly communicate your intentions of wanting to recover or maintain the friendship. It\u2019s important they know that you value the friendship beyond anything that has happened.\n\n### 6. **Set Boundaries**\nDiscuss any new boundaries or adjustments needed to make the friendship work moving forward. This can prevent potential discomfort and misunderstandings in the future.\n\n### 7. **Patience and Respect**\nBe patient and respect their response, whatever it may be. They may need more time, or they might not be interested in rekindling the friendship. Respect their decision and boundaries, even if it's not the outcome you hoped for.\n\n### 8. **Move Forward**\nFocus on moving forward rather than dwelling on the rejection. Whether the friendship is renewed or not, consider this an opportunity for personal growth and understanding.\n\n### 9. **Expand Your Social Circle**\nRegardless of the outcome, it's always a good idea to forge new friendships and relationships. Expanding your social circle can bring new perspectives and joy into your life, reducing the pressure on any single relationship.\n\n### 10. **Seek Support**\nIf you\u2019re finding it difficult to cope with your feelings or the situation, consider seeking support from other friends, family, or a professional. Sometimes, talking about it can provide clarity and peace.\n\n### Final Note:\nRemember, friendships can evolve over time. What might not be possible now could change in the future as you both grow and experiences shift your perspectives. The effort to mend a relationship showcases maturity and care, but always prioritize mutual comfort and respect in the process.","behavior":"degenerate_repetition","conversation_hash":"4527e63c3790bea9b34d1192c0a25b1c","conversation_redacted":false,"conversation_toxic":false,"country":"Belgium","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4-0125-preview","n_chars":2992,"n_turns_in_conversation":1,"n_words":471,"source_row_offset":76609,"source_shard":"turns-00020.parquet","timestamp":"Sun, 28 Apr 2024 12:52:26 GMT","turn_id":"b55471cb7d535779c4090257","turn_index":0,"user_text":"how to recover a friendship after being rejected"},{"assistant_initiates":false,"asst_text":"No existe un \u00fanico lugar en el mundo que pueda ser considerado como el m\u00e1s barato para comprar crema solar de manera consistente, ya que los precios pueden variar considerablemente seg\u00fan varios factores como impuestos locales, costos de importaci\u00f3n, tipos de cambio de moneda, ofertas espec\u00edficas de las tiendas y la marca de la crema solar.\n\nSin embargo, generalmente, los pa\u00edses con una alta competencia en el sector de la belleza y el cuidado personal, como Estados Unidos, ciertos pa\u00edses de la Uni\u00f3n Europea o incluso algunos pa\u00edses asi\u00e1ticos como Corea del Sur y Jap\u00f3n, pueden ofrecer precios competitivos debido a la amplia disponibilidad de productos y la fuerte competencia. Adem\u00e1s, los grandes retailers y cadenas de farmacias en estos pa\u00edses a menudo tienen descuentos y promociones que pueden hacer m\u00e1s accesible el costo de las cremas solares.\n\nLas compras en grandes cantidades o en outlets de marcas espec\u00edficas tambi\u00e9n pueden resultar en ahorros significativos. Por otro lado, comprar crema solar en lugares tur\u00edsticos o en tiendas de conveniencia puede ser mucho m\u00e1s costoso debido a los precios inflados.\n\nPara conseguir el precio m\u00e1s bajo, podr\u00eda ser \u00fatil comparar precios en l\u00ednea, buscar ofertas, utilizar cupones de descuento o comprar fuera de temporada (como adquirir crema solar en invierno).\n\nRecuerda, la elecci\u00f3n de la crema solar no debe basarse \u00fanicamente en el precio, sino tambi\u00e9n en su calidad, el tipo de protecci\u00f3n que ofrece (como UVA y UVB), y si es adecuada para tu tipo de piel. Adem\u00e1s, comprar productos de cuidado de la piel, incluidas las cremas solares, de fuentes confiables es crucial para evitar falsificaciones que pueden ser ineficaces o, peor a\u00fan, da\u00f1inas para la piel.","behavior":"degenerate_repetition","conversation_hash":"152316736c1d0e1da30255f8afadc799","conversation_redacted":false,"conversation_toxic":false,"country":"Spain","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Spanish","model":"gpt-4-0125-preview","n_chars":1772,"n_turns_in_conversation":1,"n_words":288,"source_row_offset":76610,"source_shard":"turns-00020.parquet","timestamp":"Sun, 28 Apr 2024 12:52:28 GMT","turn_id":"c1675b789d1cf4908320b2a9","turn_index":0,"user_text":"\u00bfemu es m\u00e1s barato del mundo para comprar crema solar?"}],"shard":{"index":20,"name":"turns-00020.parquet","rows":90031}}