{"behavior":"degenerate_repetition","label_filter":null,"limit":10,"next_offset":82416,"offset":82406,"previous_offset":null,"rows":[{"assistant_initiates":false,"asst_text":"\u8fd9\u6bb5\u4ee3\u7801\u5b9e\u73b0\u4e86\u4e00\u4e2a\u540d\u4e3a `CEUTrack` \u7684\u591a\u6a21\u6001\u8ffd\u8e2a\u6a21\u578b\uff0c\u5b83\u57fa\u4e8eVision Transformer\uff08ViT\uff09\u67b6\u6784\uff0c\u5e76\u4e14\u4e3a\u5904\u7406\u591a\u6a21\u6001\u6570\u636e\uff08\u6bd4\u5982\u6807\u51c6\u56fe\u50cf\u6570\u636e\u548c\u4e8b\u4ef6\u76f8\u673a\u6570\u636e\uff09\u63d0\u4f9b\u4e86\u652f\u6301\u3002\u4ee5\u4e0b\u662f\u8fd9\u6bb5\u4ee3\u7801\u7684\u8be6\u7ec6\u6d41\u7a0b\u89e3\u6790\uff1a\n\n### \u6a21\u578b\u67b6\u6784\n\n1. **\u6a21\u578b\u521d\u59cb\u5316**\uff1a`CEUTrack` \u7c7b\u5728\u521d\u59cb\u5316\u65f6\u63a5\u6536Transformer\u6a21\u578b\uff08\u5982ViT\uff09\u4f5c\u4e3a\u5176\u4e3b\u5e72\u7f51\u7edc\uff08backbone\uff09\uff0c\u540c\u65f6\u63a5\u53d7\u4e00\u4e2a\u7528\u4e8e\u9884\u6d4b\u8fb9\u754c\u6846\u7684\u5934\u90e8\u7f51\u7edc\uff08box_head\uff09\u3002\u6b64\u5916\uff0c\u8fd8\u53ef\u4ee5\u6307\u5b9a\u662f\u5426\u4f7f\u7528\u8f85\u52a9\u635f\u5931\uff08aux_loss\uff09\u548c\u5934\u90e8\u7f51\u7edc\u7684\u7c7b\u578b\uff08head_type\uff0c\u6bd4\u5982\u201cCORNER\u201d\u6216\u201cCENTER\u201d\uff09\u3002\n\n2. **\u524d\u5411\u4f20\u64ad**\uff1a\u6a21\u578b\u901a\u8fc7 `forward` \u65b9\u6cd5\u652f\u6301\u540c\u65f6\u5904\u7406\u4e24\u5bf9\u8f93\u5165\u56fe\u50cf\uff1a\u6807\u51c6RGB\u56fe\u50cf\uff08`x_template_img` \u548c `x_search_img`\uff09\u4e0e\u4e8b\u4ef6\u76f8\u673a\u56fe\u50cf\uff08`event_template_img` \u548c `event_search_img`\uff09\u3002\u8fd9\u610f\u5473\u7740\u6a21\u578b\u662f\u4e3a\u4e86\u80fd\u540c\u65f6\u8003\u8651\u6765\u81ea\u4e0d\u540c\u4f20\u611f\u5668\u7684\u4fe1\u606f\uff0c\u5e76\u5904\u7406\u8fd9\u4e9b\u4e0d\u540c\u7c7b\u578b\u7684\u6570\u636e\u3002\n\n    - \u9996\u5148\uff0c\u8fd9\u4e24\u5bf9\u56fe\u50cf\u5206\u522b\u901a\u8fc7\u76f8\u540c\u7684Transformer\u4e3b\u5e72\u7f51\u7edc\uff08backbone\uff09\u8fdb\u884c\u7279\u5f81\u63d0\u53d6\uff0c\u751f\u6210\u76f8\u5e94\u7684\u7279\u5f81 `x` \u548c `event_x`\uff0c\u4ee5\u53ca\u5404\u81ea\u7684\u6ce8\u610f\u529b\u6743\u91cd `attn` \u548c `event_attn`\u3002\n    \n    - \u7136\u540e\uff0c\u4f7f\u7528\u5355\u72ec\u5b9a\u4e49\u7684 `forward_head` \u65b9\u6cd5\uff0c\u57fa\u4e8e\u63d0\u53d6\u7684\u7279\u5f81\u548c\u6ce8\u610f\u529b\u6743\u91cd\u6765\u751f\u6210\u6700\u7ec8\u7684\u8f93\u51fa\u3002\u8fd9\u4e2a\u8fc7\u7a0b\u5206\u522b\u5bf9\u4e24\u79cd\u6a21\u6001\u7684\u6570\u636e\u6267\u884c\uff0c\u5206\u522b\u5f97\u5230RGB\u56fe\u50cf\u548c\u4e8b\u4ef6\u76f8\u673a\u56fe\u50cf\u7684\u9884\u6d4b\u8f93\u51fa\u3002\n\n### \u6a21\u578b\u7ec6\u8282\n\n- `forward_head` \u65b9\u6cd5\u8d1f\u8d23\u5c06\u7279\u5f81\u6620\u5c04\u5230\u5177\u4f53\u7684\u9884\u6d4b\u76ee\u6807\u4e0a\uff08\u4f8b\u5982\u8fb9\u754c\u6846\u7684\u9884\u6d4b\uff09\u3002\u6839\u636e\u5934\u90e8\u7684\u7c7b\u578b\uff08\u4f8b\u5982\uff0cCORNER\u6216CENTER\uff09\uff0c\u8fd9\u4e2a\u8fc7\u7a0b\u53ef\u80fd\u5305\u62ec\u5c06\u7279\u5f81\u5f20\u91cf\u91cd\u65b0\u6392\u5217\u4e3a\u4e00\u4e2a\u6216\u591a\u4e2a\u8f93\u51fa\u56fe\uff0c\u7136\u540e\u901a\u8fc7box head\u8fdb\u884c\u8fdb\u4e00\u6b65\u7684\u5904\u7406\u6765\u9884\u6d4b\u8fb9\u754c\u6846\u3002\n\n- `build_ceutrack` \u51fd\u6570\u8d1f\u8d23\u6784\u5efa\u5e76\u914d\u7f6e `CEUTrack` \u6a21\u578b\u5b9e\u4f8b\u3002\u5b83\u652f\u6301\u52a0\u8f7d\u9884\u8bad\u7ec3\u7684ViT\u6a21\u578b\u4f5c\u4e3a\u4e3b\u5e72\u7f51\u7edc\uff0c\u5e76\u6839\u636e\u914d\u7f6e\u53c2\u6570\u521d\u59cb\u5316box\u5934\u90e8\u7f51\u7edc\u3002\n\n### \u5173\u952e\u6982\u5ff5\n\n- **\u591a\u6a21\u6001\u5904\u7406**\uff1a\u8fd9\u4e2a\u5b9e\u73b0\u5f3a\u8c03\u4e86\u5bf9\u6765\u81ea\u4e0d\u540c\u4f20\u611f\u5668\u6e90\u7684\u6570\u636e\u8fdb\u884c\u540c\u6b65\u5904\u7406\u7684\u80fd\u529b\u3002\u8fd9\u5bf9\u4e8e\u9700\u8981\u7efc\u5408\u591a\u79cd\u7c7b\u578b\u4fe1\u606f\u7684\u4efb\u52a1\u975e\u5e38\u91cd\u8981\uff0c\u6bd4\u5982\u5728\u771f\u5b9e\u73af\u5883\u4e2d\u7684\u5bf9\u8c61\u8ffd\u8e2a\uff0c\u5176\u4e2dRGB\u56fe\u50cf\u548c\u4e8b\u4ef6\u76f8\u673a\u6570\u636e\u53ef\u4ee5\u63d0\u4f9b\u4e92\u8865\u7684\u4fe1\u606f\u3002\n\n- **\u6a21\u5757\u5316\u8bbe\u8ba1**\uff1a`CEUTrack` \u5c06\u7279\u5f81\u63d0\u53d6\u548c\u9884\u6d4b\u4efb\u52a1\u89e3\u8026\u6210\u72ec\u7acb\u6a21\u5757\uff08\u5206\u522b\u662f\u4e3b\u5e72\u7f51\u7edc\u548c\u5934\u90e8\u7f51\u7edc\uff09\uff0c\u8fd9\u63d0\u9ad8\u4e86\u6a21\u578b\u7684\u7075\u6d3b\u6027\u548c\u6269\u5c55\u6027\u3002\n\n- **\u8fc1\u79fb\u5b66\u4e60**\uff1a\u901a\u8fc7\u5728 `build_ceutrack` \u51fd\u6570\u4e2d\u6307\u5b9a\u4e0d\u540c\u7684\u9884\u8bad\u7ec3\u6a21\u578b\u548c\u53c2\u6570\uff0c\u6613\u4e8e\u5728\u4e0d\u540c\u6761\u4ef6\u548c\u4efb\u52a1\u4e0a\u9002\u7528\u8be5\u6a21\u578b\u67b6\u6784\u3002\n\n\u603b\u4f53\u800c\u8a00\uff0c`CEUTrack` \u4f5c\u4e3a\u4e00\u4e2a\u6a21\u578b\u5b9e\u4f8b\u5c55\u793a\u4e86\u73b0\u4ee3\u6df1\u5ea6\u5b66\u4e60\u4e2d\u5e38\u7528\u7684\u4e00\u4e9b\u8bbe\u8ba1\u6a21\u5f0f\uff0c\u5305\u62ec\u5bf9\u591a\u6a21\u6001\u8f93\u5165\u7684\u5904\u7406\u3001\u57fa\u4e8eTransformer\u7684\u7279\u5f81\u63d0\u53d6\uff0c\u4ee5\u53ca\u6a21\u5757\u5316\u8bbe\u8ba1\u6765\u9002\u914d\u4e0d\u540c\u7684\u9884\u6d4b\u4efb\u52a1\u3002","behavior":"degenerate_repetition","conversation_hash":"14db8724701ffbfd25fef4b777a282ee","conversation_redacted":false,"conversation_toxic":false,"country":"United States","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":8374,"n_turns_in_conversation":12,"n_words":566,"source_row_offset":82406,"source_shard":"turns-00019.parquet","timestamp":"Fri, 12 Apr 2024 09:11:20 GMT","turn_id":"d6bb245cbdfac6ccdf75083b","turn_index":7,"user_text":"\u603b\u7ed3\u6d41\u7a0b\uff1aimport math\nimport os\nfrom typing import List\nimport torch\nfrom torch import nn\nfrom torch.nn.modules.transformer import _get_clones\nfrom lib.models.layers.head import build_box_head\nfrom lib.models.ceutrack.vit import vit_base_patch16_224\nfrom lib.models.ceutrack.vit_ce import vit_large_patch16_224_ce, vit_base_patch16_224_ce\nfrom lib.utils.box_ops import box_xyxy_to_cxcywh\n\nclass CEUTrack(nn.Module):\n    \"\"\" This is the base class for hdetrack \"\"\"\n\n    def __init__(self, transformer, box_head, aux_loss=False, head_type=\"CORNER\"):\n        \"\"\" Initializes the model.\n        Parameters:\n            transformer: torch module of the transformer architecture.\n            aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used.\n        \"\"\"\n        super().__init__()\n        self.backbone = transformer\n        self.box_head = box_head\n        \n        self.aux_loss = aux_loss\n        self.head_type = head_type\n        if head_type == \"CORNER\" or head_type == \"CENTER\":\n            self.feat_sz_s = int(box_head.feat_sz)\n            self.feat_len_s = int(box_head.feat_sz ** 2)\n\n        if self.aux_loss:\n            self.box_head = _get_clones(self.box_head, 6)\n    \n    def forward(self, \n                event_template_img: torch.Tensor,                # torch.Size([bs, 3, 128, 128])\n                event_search_img: torch.Tensor,                  # torch.Size([bs, 3, 256, 256])\n                # event_template: torch.Tensor,                  # torch.Size([bs, 1, 19, 1024])\n                # event_search: torch.Tensor,                    # torch.Size([bs, 1, 19, 4096])\n                x_template_img: torch.Tensor, \n                x_search_img: torch.Tensor,\n                ce_template_mask=None,\n                ce_keep_rate=None,\n                return_last_attn=False,\n                ):\n\n        # before feeding into backbone, we need to concat four vectors, or two two concat\n        x, attn = self.backbone(z=x_template_img, x=x_search_img,\n                                    ce_template_mask=ce_template_mask,\n                                    ce_keep_rate=ce_keep_rate,\n                                    return_last_attn=return_last_attn)\n        event_x,event_attn = self.backbone(z=event_template_img, x=event_search_img,\n                                    ce_template_mask=ce_template_mask,\n                                    ce_keep_rate=ce_keep_rate,\n                                    return_last_attn=return_last_attn)\n        \n        \n        # Forward head\n        feat_last = x\n        if isinstance(x, list):\n            feat_last = x[-1]\n        out_x = self.forward_head(feat_last,attn,None)\n\n        # \u65b0\u589eForward\u5934\n        event_feaat_last = event_x\n        if isinstance(event_x,list):\n            event_feat_last = event_x[-1]\n        out_event = self.forward_head(event_feaat_last,event_attn, None)\n        \n        return out_x,out_event # \u53cc\u5206\u652f\u8f93\u51fa\u5934\n\n    def forward_head(self, cat_feature, cat_attn, gt_score_map=None):\n        \"\"\"\n        cat_feature: output embeddings of the backbone, it can be (HW1+HW2, B, C) or (HW2, B, C)\n        \"\"\"\n        # \u9996\u5148\u6267\u884ccat_feature[:, -self.feat_len_s:]\uff0c\u63d0\u53d6event_search,\u8fd9\u91cc\u662fevent\u8fd8\u662fRGB\u6a21\u6001\uff0c\u53d6\u51b3\u4e8e\u8f93\u5165\u7684cat_feature\n\n        cat_search = cat_feature[:, -self.feat_len_s:]       # [bs, 256, 768]  cat_search \u8fd9\u91cc\u7684cat_search\u4ec5\u4ec5\u8868\u793a\u662fsearch\u6a21\u6001\u7684\u7279\u5f81,\u4e0e\u6a21\u6001\u65e0\u5173\n        opt = (cat_search.unsqueeze(-1)).permute((0, 3, 2, 1)).contiguous()  # cat_search.unsqueeze(-1) \u8868\u793a\u589e\u52a0\u4e00\u4e2a\u7ef4\u5ea6\uff1a\u539f\u59cb\u7ef4\u5ea6\u662f[bs, 256, 768]\uff0c\u589e\u52a0\u4e00\u4e2a\u7ef4\u5ea6\u540e\u53d8\u6210[bs, 256, 768, 1] \u7136\u540epermute((0, 3, 2, 1)) \u8868\u793a\u5c06\u7ef4\u5ea6\u8c03\u6574\u4e3a[bs, 1, 768, 256]\n        bs, Nq, C, HW = opt.size() # \u53d6\u51faopt\u7684size\uff0c\u5206\u522b\u662fbs, Nq, C, HW\uff0c\u5206\u522b\u8868\u793abatch size, query number, channel, height*width\n        opt_feat = opt.view(-1, C, self.feat_sz_s, self.feat_sz_s) # \u5c06opt\u7684\u7ef4\u5ea6\u8c03\u6574\u4e3a[-1, C, self.feat_sz_s, self.feat_sz_s]\uff0c\u8fd9\u91cc\u7684self.feat_sz_s\u662f256\uff0c\u8868\u793a\u7279\u5f81\u56fe\u7684\u5927\u5c0f\n\n        if self.head_type == \"CORNER\":\n            # run the corner head\n            pred_box, score_map = self.box_head(opt_feat, True)\n            outputs_coord = box_xyxy_to_cxcywh(pred_box)\n            outputs_coord_new = outputs_coord.view(bs, Nq, 4)\n            out = {'pred_boxes': outputs_coord_new,\n                   'score_map': score_map,\n                   }\n            return out\n        # head_type == \"CENTER\"\n        elif self.head_type == \"CENTER\":\n            # run the center head\n            score_map_ctr, bbox, size_map, offset_map = self.box_head(opt_feat, gt_score_map)\n            # outputs_coord = box_xyxy_to_cxcywh(bbox)\n            outputs_coord = bbox\n            outputs_coord_new = outputs_coord.view(bs, Nq, 4)\n            out = {\n                   'pred_boxes': outputs_coord_new,\n                   'score_map': score_map_ctr,\n                   'size_map': size_map,\n                   'offset_map': offset_map,\n                   'cat_attn': cat_attn\n                   }\n            return out\n        else:\n            raise NotImplementedError\n\ndef build_ceutrack(cfg, training=True):\n    current_dir = os.path.dirname(os.path.abspath(__file__))  # This is your Project Root\n    pretrained_path = os.path.join(current_dir, 'pretrained_models')\n    if cfg.MODEL.PRETRAIN_FILE and ('CEUTrack' not in cfg.MODEL.PRETRAIN_FILE) and training:\n        pretrained = os.path.join(pretrained_path, cfg.MODEL.PRETRAIN_FILE)\n    else:\n        pretrained = ''\n        \n    if cfg.MODEL.BACKBONE.TYPE == 'vit_base_patch16_224':\n        backbone_s = vit_base_patch16_224(pretrained, drop_path_rate=cfg.TRAIN.DROP_PATH_RATE)\n        hidden_dim = backbone_s.embed_dim\n        patch_start_index = 1\n\n    elif cfg.MODEL.BACKBONE.TYPE == 'vit_base_patch16_224_ce':\n        backbone = vit_base_patch16_224_ce(pretrained, drop_path_rate=cfg.TRAIN.DROP_PATH_RATE,\n                                           ce_loc=cfg.MODEL.BACKBONE.CE_LOC,\n                                           ce_keep_ratio=cfg.MODEL.BACKBONE.CE_KEEP_RATIO,\n                                           )\n        # hidden_dim = backbone_s.embed_dim*2\n        hidden_dim = backbone.embed_dim\n        patch_start_index = 1\n\n    elif cfg.MODEL.BACKBONE.TYPE == 'vit_large_patch16_224_ce':\n        backbone = vit_large_patch16_224_ce(pretrained, drop_path_rate=cfg.TRAIN.DROP_PATH_RATE,\n                                            ce_loc=cfg.MODEL.BACKBONE.CE_LOC,\n                                            ce_keep_ratio=cfg.MODEL.BACKBONE.CE_KEEP_RATIO,\n                                            )\n        hidden_dim = backbone.embed_dim\n        patch_start_index = 1\n    else:\n        raise NotImplementedError\n\n    backbone.finetune_track(cfg=cfg, patch_start_index=patch_start_index)\n    box_head = build_box_head(cfg, hidden_dim)\n\n    model = CEUTrack(\n        backbone,\n        box_head,\n        aux_loss=False,\n        head_type=cfg.MODEL.HEAD.TYPE,\n    )\n\n    return model\n\n    if 'CEUTrack' in cfg.MODEL.PRETRAIN_FILE and training:\n        checkpoint = torch.load(cfg.MODEL.PRETRAIN_FILE, map_location=\"cpu\")\n        missing_keys, unexpected_keys = model.load_state_dict(checkpoint[\"net\"], strict=False)\n        print('Load pretrained model from: ' + cfg.MODEL.PRETRAIN_FILE)\n\n    return model\n"},{"assistant_initiates":false,"asst_text":"\u5728\u63d0\u4f9b\u7684\u4ee3\u7801\u6bb5\u4e2d\uff0c`CEUTrack` \u6a21\u578b\u5305\u542b\u4e86\u4e24\u4e2a\u4e3b\u8981\u7684\u5904\u7406\u5206\u652f\uff0c\u5206\u522b\u7528\u4e8e\u5904\u7406\u4e24\u5bf9\u4e0d\u540c\u7c7b\u578b\u7684\u8f93\u5165\u6570\u636e\uff1a\u4e00\u5bf9\u662f\u6807\u51c6RGB\u56fe\u50cf\uff08\u7531 `x_template_img` \u548c `x_search_img` \u8868\u793a\uff09\uff0c\u53e6\u4e00\u5bf9\u662f\u4e8b\u4ef6\u76f8\u673a\u6570\u636e\uff08\u7531 `event_template_img` \u548c `event_search_img` \u8868\u793a\uff09\u3002\u56e0\u6b64\uff0c\u8fd9\u6784\u6210\u4e86\u4e24\u4e2a\u72ec\u7acb\u4f46\u7ed3\u6784\u76f8\u4f3c\u7684\u6570\u636e\u5904\u7406\u6d41\u7a0b\u6216\u5206\u652f\u3002\n\n### \u8f93\u51fa\u5206\u652f\uff1a\n\n1. **RGB\u56fe\u50cf\u5904\u7406\u5206\u652f**\uff1a\u8fd9\u4e2a\u5206\u652f\u5904\u7406\u6807\u51c6RGB\u56fe\u50cf\u5bf9\uff0c\u63d0\u53d6\u5176\u7279\u5f81\u548c\u6ce8\u610f\u529b\u4fe1\u606f\uff0c\u6700\u7ec8\u901a\u8fc7 `forward_head` \u65b9\u6cd5\u5f97\u5230RGB\u56fe\u50cf\u5bf9\u5e94\u7684\u8f93\u51fa\u3002\u8fd9\u4e2a\u8f93\u51fa\u5305\u62ec\u8fb9\u754c\u6846\u9884\u6d4b\u548c\u53ef\u80fd\u7684\u5176\u4ed6\u4e0e\u4efb\u52a1\u76f8\u5173\u7684\u9884\u6d4b\u3002\n\n2. **\u4e8b\u4ef6\u76f8\u673a\u6570\u636e\u5904\u7406\u5206\u652f**\uff1a\u8fd9\u4e2a\u5206\u652f\u4ee5\u76f8\u540c\u7684\u65b9\u5f0f\u5904\u7406\u4e8b\u4ef6\u76f8\u673a\u6570\u636e\uff0c\u63d0\u53d6\u7279\u5f81\u548c\u6ce8\u610f\u529b\u4fe1\u606f\uff0c\u5e76\u540c\u6837\u901a\u8fc7 `forward_head` \u65b9\u6cd5\u751f\u6210\u5bf9\u5e94\u7684\u8f93\u51fa\u7ed3\u679c\uff0c\u5305\u62ec\u8fb9\u754c\u6846\u548c\u5176\u4ed6\u53ef\u80fd\u7684\u9884\u6d4b\u3002\n\n### \u54cd\u5e94\u6570\u91cf\uff1a\n\n\u56e0\u4e3a\u8fd9\u4e24\u4e2a\u5206\u652f\u90fd\u72ec\u7acb\u5730\u751f\u6210\u5404\u81ea\u7684\u8f93\u51fa\uff0c\u6a21\u578b\u6700\u7ec8\u4f1a\u5f97\u5230**\u4e24\u7ec4\u54cd\u5e94**\uff08\u8f93\u51fa\uff09\uff0c\u6bcf\u7ec4\u54cd\u5e94\u5bf9\u5e94\u4e8e\u5904\u7406\u7684\u4e00\u5bf9\u8f93\u5165\u6570\u636e\u3002\u66f4\u5177\u4f53\u5730\u8bf4\uff1a\n\n- \u6765\u81ea **RGB\u56fe\u50cf\u5904\u7406\u5206\u652f** \u7684\u8f93\u51fa\uff0c\u53ef\u80fd\u5305\u542b\u7269\u4f53\u7684\u8fb9\u754c\u6846\u4f4d\u7f6e\u3001\u5f97\u5206\u6620\u5c04\u7b49\uff0c\u5e76\u5c06\u8fd9\u4e9b\u4fe1\u606f\u5c01\u88c5\u5728\u8f93\u51fa\u5bf9\u8c61\u6216\u5b57\u5178\u4e2d\uff08\u7531 `out_x` \u8868\u793a\uff09\u3002\n  \n- \u6765\u81ea **\u4e8b\u4ef6\u76f8\u673a\u6570\u636e\u5904\u7406\u5206\u652f** \u7684\u8f93\u51fa\uff0c\u540c\u6837\u5305\u542b\u7269\u4f53\u7684\u8fb9\u754c\u6846\u4f4d\u7f6e\u3001\u5f97\u5206\u6620\u5c04\u7b49\uff0c\u5bf9\u5e94\u4e8e\u4e8b\u4ef6\u76f8\u673a\u6570\u636e\u7684\u5904\u7406\u7ed3\u679c\uff0c\u5e76\u5c01\u88c5\u5728\u53e6\u4e00\u4e2a\u8f93\u51fa\u5bf9\u8c61\u6216\u5b57\u5178\u4e2d\uff08\u7531 `out_event` \u8868\u793a\uff09\u3002\n\n\u56e0\u6b64\uff0c\u5728\u8fd9\u4e2a\u6a21\u578b\u7684\u7ed3\u6784\u548c\u5b9e\u73b0\u4e2d\uff0c\u5b83\u662f\u4e3a\u4e86\u5e76\u884c\u5904\u7406\u4e24\u79cd\u4e0d\u540c\u7c7b\u578b\u7684\u8f93\u5165\u6570\u636e\u800c\u8bbe\u8ba1\u7684\uff0c\u6bcf\u79cd\u7c7b\u578b\u7684\u6570\u636e\u90fd\u901a\u8fc7\u81ea\u5df1\u7684\u5206\u652f\u8fdb\u884c\u5904\u7406\uff0c\u5e76\u6700\u7ec8\u751f\u6210\u5404\u81ea\u7684\u8f93\u51fa\u54cd\u5e94\u3002\u8fd9\u79cd\u8bbe\u8ba1\u5141\u8bb8\u6a21\u578b\u5728\u540c\u4e00\u9884\u6d4b\u6846\u67b6\u4e2d\u6574\u5408\u548c\u5229\u7528\u6765\u81ea\u4e0d\u540c\u6570\u636e\u6e90\u7684\u4fe1\u606f\uff0c\u4ece\u800c\u63d0\u9ad8\u5bf9\u7279\u5b9a\u4efb\u52a1\uff08\u5982\u76ee\u6807\u8ddf\u8e2a\u6216\u68c0\u6d4b\uff09\u7684\u6574\u4f53\u6027\u80fd\u3002","behavior":"degenerate_repetition","conversation_hash":"14db8724701ffbfd25fef4b777a282ee","conversation_redacted":false,"conversation_toxic":false,"country":"United States","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":816,"n_turns_in_conversation":12,"n_words":37,"source_row_offset":82407,"source_shard":"turns-00019.parquet","timestamp":"Fri, 12 Apr 2024 09:11:20 GMT","turn_id":"7e4802658c2f7c4405eaa413","turn_index":8,"user_text":"\u90a3\u4e48\u5728\u8fd9\u6bb5\u4ee3\u7801\u4e2d\uff0c\u5305\u542b\u51e0\u4e2a\u5206\u652f\uff1f\u8f93\u51fa\u662f\u51e0\u4e2a\u5206\u652f\uff1f\u5f97\u5230\u51e0\u4e2a\u54cd\u5e94\uff1f"},{"assistant_initiates":false,"asst_text":"\u5bf9\u6bd4\u539f\u59cb\u7684Vision Transformer (ViT) CE\u6a21\u578b\u548c\u4fee\u6539\u540e\u7684Vision Transformer CE_S\u6a21\u578b\uff0c\u53ef\u4ee5\u6982\u62ec\u5404\u81ea\u7684\u4f18\u52bf\u3001\u52a3\u52bf\uff0c\u5e76\u4ece\u4e2d\u5f97\u51fa\u4fee\u6539\u6a21\u578b\u7684\u52a8\u673a\u3002\n\n### \u539f\u59cbViT CE\u6a21\u578b\u7684\u4f18\u52bf\u548c\u52a3\u52bf\uff1a\n\n**\u4f18\u52bf**:\n1. **\u591a\u6a21\u6001\u5904\u7406\u80fd\u529b**\uff1a\u539f\u59cb\u6a21\u578b\u8bbe\u8ba1\u7528\u4e8e\u5904\u7406\u540c\u65f6\u5305\u542b\u6807\u51c6RGB\u56fe\u50cf\u548c\u4e8b\u4ef6\u76f8\u673a\u6570\u636e\u7684\u8f93\u5165\uff0c\u8fd9\u589e\u52a0\u4e86\u6a21\u578b\u5904\u7406\u4e0d\u540c\u6570\u636e\u6e90\u7684\u7075\u6d3b\u6027\u548c\u9002\u7528\u6027\u3002\n2. **\u6570\u636e\u878d\u5408**\uff1a\u901a\u8fc7\u5c06\u4e0d\u540c\u6a21\u6001\u7684\u8f93\u5165\u5408\u5e76\u4e3a\u4e00\u4e2a\u7edf\u4e00\u7684\u7279\u5f81\u8868\u793a\uff0c\u6a21\u578b\u5728\u6982\u5ff5\u4e0a\u80fd\u591f\u66f4\u597d\u5730\u878d\u5408\u6765\u81ea\u4e0d\u540c\u4f20\u611f\u5668\u7684\u4fe1\u606f\uff0c\u53ef\u80fd\u63d0\u9ad8\u5bf9\u590d\u6742\u73af\u5883\u7684\u7406\u89e3\u3002\n3. **\u5019\u9009\u6d88\u9664\uff08CE\uff09**\uff1a\u5229\u7528CE\u6a21\u5757\u53ef\u4ee5\u5728\u4fdd\u6301\u6a21\u578b\u6027\u80fd\u7684\u540c\u65f6\u51cf\u5c11\u8ba1\u7b97\u91cf\uff0c\u63d0\u9ad8\u6a21\u578b\u6548\u7387\u3002\n\n**\u52a3\u52bf**:\n1. **\u590d\u6742\u6027\u8f83\u9ad8**\uff1a\u5904\u7406\u591a\u6a21\u6001\u6570\u636e\u5e76\u6267\u884c\u5019\u9009\u6d88\u9664\u589e\u52a0\u4e86\u6a21\u578b\u7684\u590d\u6742\u6027\uff0c\u5bf9\u4e8e\u7406\u89e3\u3001\u5b9e\u73b0\u548c\u8c03\u4f18\u90fd\u5e26\u6765\u4e86\u6311\u6218\u3002\n2. **\u8ba1\u7b97\u6210\u672c**\uff1a\u5c3d\u7ba1\u6709CE\u6a21\u5757\u7684\u4f18\u5316\uff0c\u4f46\u540c\u65f6\u5904\u7406\u4e24\u79cd\u7c7b\u578b\u7684\u6570\u636e\u4ecd\u53ef\u80fd\u5bfc\u81f4\u8f83\u9ad8\u7684\u8ba1\u7b97\u6210\u672c\u3002\n3. **\u7279\u5b9a\u60c5\u5f62\u4e0b\u7684\u9002\u7528\u6027\u6709\u9650**\uff1a\u5982\u679c\u67d0\u4e9b\u4efb\u52a1\u6216\u573a\u666f\u4e2d\u53ea\u80fd\u83b7\u5f97\u4e00\u79cd\u7c7b\u578b\u7684\u8f93\u5165\u6570\u636e\uff0c\u90a3\u4e48\u539f\u59cb\u6a21\u578b\u4e2d\u7528\u4e8e\u5904\u7406\u53e6\u4e00\u79cd\u6a21\u6001\u7684\u90e8\u5206\u5219\u53ef\u80fd\u53d8\u5f97\u5197\u4f59\u3002\n\n### \u4fee\u6539\u540eViT CE_S\u6a21\u578b\u7684\u4f18\u52bf\u548c\u52a3\u52bf\uff1a\n\n**\u4f18\u52bf**:\n1. **\u7279\u5b9a\u6a21\u6001\u7126\u70b9**\uff1a\u4e13\u6ce8\u4e8e\u4f18\u5316\u548c\u6539\u8fdb\u5bf9\u5355\u4e00\u6570\u636e\u6a21\u6001\uff08\u50cf\u662f\u4ec5RGB\u56fe\u50cf\u6216\u4e8b\u4ef6\u76f8\u673a\u6570\u636e\uff09\u7684\u5904\u7406\u80fd\u529b\uff0c\u4f7f\u5f97\u6a21\u578b\u5728\u8fd9\u4e2a\u9886\u57df\u53ef\u80fd\u5177\u6709\u66f4\u4f18\u7684\u6027\u80fd\u3002\n2. **\u7b80\u5316\u6a21\u578b\u7ed3\u6784**\uff1a\u51cf\u5c11\u4e86\u5bf9\u7b2c\u4e8c\u6a21\u6001\u6570\u636e\u5904\u7406\u7684\u903b\u8f91\uff0c\u53ef\u4ee5\u964d\u4f4e\u6a21\u578b\u590d\u6742\u6027\uff0c\u7b80\u5316\u8bad\u7ec3\u548c\u63a8\u7406\u8fc7\u7a0b\u3002\n3. **\u8ba1\u7b97\u6548\u7387**\uff1a\u4e13\u6ce8\u4e8e\u5355\u4e00\u6570\u636e\u6e90\u7684\u5904\u7406\uff0c\u7f29\u5c0f\u6a21\u578b\u89c4\u6a21\u548c\u8ba1\u7b97\u91cf\uff0c\u6709\u52a9\u4e8e\u63d0\u9ad8\u8ba1\u7b97\u6548\u7387\u548c\u52a0\u901f\u6a21\u578b\u63a8\u7406\u3002\n\n**\u52a3\u52bf**:\n1. **\u4fe1\u606f\u5229\u7528\u6709\u9650**\uff1a\u5ffd\u7565\u4e86\u5229\u7528\u989d\u5916\u6a21\u6001\u6570\u636e\uff08\u5982\u4e8b\u4ef6\u76f8\u673a\u6570\u636e\uff09\u5e26\u6765\u7684\u4e92\u8865\u4fe1\u606f\uff0c\u53ef\u80fd\u5728\u4e00\u5b9a\u7a0b\u5ea6\u4e0a\u9650\u5236\u4e86\u6a21\u578b\u5904\u7406\u590d\u6742\u573a\u666f\u7684\u80fd\u529b\u3002\n2. **\u9002\u7528\u6027\u7a84**\uff1a\u4fee\u6539\u540e\u7684\u6a21\u578b\u4e13\u6ce8\u4e8e\u5904\u7406\u7279\u5b9a\u7c7b\u578b\u7684\u8f93\u5165\u6570\u636e\uff0c\u5176\u9002\u7528\u6027\u76f8\u6bd4\u539f\u6709\u8bbe\u8ba1\u66f4\u4e3a\u6709\u9650\u3002\n\n### \u4fee\u6539\u52a8\u673a\uff1a\n\n\u8003\u8651\u5230\u4e0a\u8ff0\u56e0\u7d20\uff0c\u4fee\u6539\u6a21\u578b\u7684\u5408\u7406\u52a8\u673a\u53ef\u80fd\u5305\u62ec\uff1a\n\n- **\u9488\u5bf9\u5e94\u7528\u4e13\u4f18**\uff1a\u5982\u679c\u76ee\u6807\u5e94\u7528\u573a\u666f\u4e2d\u53ea\u6709\u5355\u4e00\u7c7b\u578b\u7684\u6570\u636e\u6e90\u53ef\u7528\uff0c\u6216\u8005\u5df2\u77e5\u67d0\u4e00\u6570\u636e\u6a21\u6001\u660e\u663e\u6bd4\u53e6\u5916\u7684\u6570\u636e\u6a21\u6001\u66f4\u4e3a\u91cd\u8981\u548c\u6709\u6548\uff0c\u90a3\u4e48\u4e13\u6ce8\u4f18\u5316\u90a3\u4e00\u6a21\u6001\u7684\u5904\u7406\u80fd\u529b\u4fbf\u662f\u5408\u7406\u7684\u7b56\u7565\u3002\n- **\u8d44\u6e90\u9650\u5236\u8003\u8651**\uff1a\u5728\u8ba1\u7b97\u8d44\u6e90\u6709\u9650\u7684\u73af\u5883\u4e0b\uff0c\u4e3a\u4e86\u63d0\u9ad8\u6a21\u578b\u7684\u90e8\u7f72\u6548\u7387\u548c\u5b9e\u65f6\u6027\u80fd\uff0c\u7b80\u5316\u6a21\u578b\u5e76\u51cf\u5c11\u5176\u5bf9\u8ba1\u7b97\u8d44\u6e90\u7684\u9700\u6c42\u662f\u4e00\u79cd\u91cd\u8981\u7684\u4fee\u6539\u52a8\u673a\u3002\n- **\u7814\u7a76\u76ee\u7684**\uff1a\u4e3a\u4e86\u63a2\u7d22\u5355\u4e00\u6a21\u6001\u6570\u636e\uff08\u5982\u4ec5RGB\u56fe\u50cf\u6216\u4e8b\u4ef6\u6570\u636e\uff09\u5728\u7279\u5b9a\u4efb\u52a1\u4e0a\u7684\u6f5c\u529b\u548c\u5c40\u9650\u6027\uff0c\u4e13\u95e8\u8bbe\u8ba1\u548c\u4f18\u5316\u9488\u5bf9\u8be5\u6a21\u6001\u7684\u6a21\u578b\u6709\u5176\u7814\u7a76\u4ef7\u503c\u3002\n\n\u603b\u4e4b\uff0c\u4fee\u6539\u6a21\u578b\u7684\u8bbe\u8ba1\u5e94\u57fa\u4e8e\u5177\u4f53\u5e94\u7528\u573a\u666f\u7684\u9700\u6c42\u3001\u8ba1\u7b97\u8d44\u6e90\u7684\u53ef\u7528\u6027\u4ee5\u53ca\u7814\u7a76\u76ee\u6807\u7684\u660e\u786e\uff0c\u4ee5\u786e\u4fdd\u6a21\u578b\u4fee\u6539\u540e\u80fd\u66f4\u597d\u5730\u6ee1\u8db3\u7279\u5b9a\u7684\u9700\u6c42\u3002","behavior":"degenerate_repetition","conversation_hash":"14db8724701ffbfd25fef4b777a282ee","conversation_redacted":false,"conversation_toxic":false,"country":"United States","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":20019,"n_turns_in_conversation":12,"n_words":1587,"source_row_offset":82408,"source_shard":"turns-00019.parquet","timestamp":"Fri, 12 Apr 2024 09:11:20 GMT","turn_id":"b22567492a8ad8da7b9ade74","turn_index":9,"user_text":"\u76f8\u8f83\u4e8eimport math\nimport logging\nfrom functools import partial\nfrom collections import OrderedDict\nfrom copy import deepcopy\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom timm.models.layers import to_2tuple\n\nfrom lib.models.layers.patch_embed import PatchEmbed, PatchEmbed_event, xcorr_depthwise\nfrom .utils import combine_tokens, recover_tokens\nfrom .vit import VisionTransformer\nfrom ..layers.attn_blocks import CEBlock\n\n_logger = logging.getLogger(__name__)\n\n\nclass VisionTransformerCE(VisionTransformer):\n    \"\"\" Vision Transformer with candidate elimination (CE) module\n\n    A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`\n        - https://arxiv.org/abs/2010.11929\n\n    Includes distillation token & head support for `DeiT: Data-efficient Image Transformers`\n        - https://arxiv.org/abs/2012.12877\n    \"\"\"\n\n    def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,\n                 num_heads=12, mlp_ratio=4., qkv_bias=True, representation_size=None, distilled=False,\n                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0., embed_layer=PatchEmbed, norm_layer=None,\n                 act_layer=None, weight_init='',\n                 ce_loc=None, ce_keep_ratio=None):\n        \"\"\"\n        Args:\n            img_size (int, tuple): input image size\n            patch_size (int, tuple): patch size\n            in_chans (int): number of input channels\n            num_classes (int): number of classes for classification head\n            embed_dim (int): embedding dimension\n            depth (int): depth of transformer\n            num_heads (int): number of attention heads\n            mlp_ratio (int): ratio of mlp hidden dim to embedding dim\n            qkv_bias (bool): enable bias for qkv if True\n            representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set\n            distilled (bool): model includes a distillation token and head as in DeiT models\n            drop_rate (float): dropout rate\n            attn_drop_rate (float): attention dropout rate\n            drop_path_rate (float): stochastic depth rate\n            embed_layer (nn.Module): patch embedding layer\n            norm_layer: (nn.Module): normalization layer\n            weight_init: (str): weight init scheme\n        \"\"\"\n        # super().__init__()\n        super().__init__()\n        if isinstance(img_size, tuple):\n            self.img_size = img_size\n        else:\n            self.img_size = to_2tuple(img_size)\n        self.patch_size = patch_size\n        self.in_chans = in_chans\n\n        self.num_classes = num_classes\n        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models\n        self.num_tokens = 2 if distilled else 1\n        norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)\n        act_layer = act_layer or nn.GELU\n\n        self.patch_embed = embed_layer(\n            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)\n        num_patches = self.patch_embed.num_patches\n\n        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))\n        self.dist_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if distilled else None\n        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim))\n        self.pos_drop = nn.Dropout(p=drop_rate)\n        self.pos_embed_event = PatchEmbed_event(in_chans=32, embed_dim=768, kernel_size=4, stride=4)\n        # self.pos_embed_event = PatchEmbed_event(in_chans=32, embed_dim=768, kernel_size=4, stride=4)\n        # self.pos_embed_event_z = PatchEmbed_event(in_chans=32, embed_dim=768, kernel_size=3, stride=1)\n        # attn = CrossAttn(768, 4, 3072, 0.1, 'relu')\n        # self.cross_attn = Iter_attn(attn, 2)\n\n        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule\n        blocks = []\n        ce_index = 0\n        self.ce_loc = ce_loc\n        for i in range(depth):\n            ce_keep_ratio_i = 1.0\n            if ce_loc is not None and i in ce_loc:\n                ce_keep_ratio_i = ce_keep_ratio[ce_index]\n                ce_index += 1\n\n            blocks.append(\n                CEBlock(\n                    dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate,\n                    attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer,\n                    keep_ratio_search=ce_keep_ratio_i)\n            )\n\n        self.blocks = nn.Sequential(*blocks)\n        self.norm = norm_layer(embed_dim)\n\n        self.init_weights(weight_init)\n\n    def forward_features(self, z, x, event_z, event_x,\n                         mask_z=None, mask_x=None,\n                         ce_template_mask=None, ce_keep_rate=None,\n                         return_last_attn=False\n                         ):\n        B, H, W = x.shape[0], x.shape[2], x.shape[3]\n\n        event_z = self.pos_embed_event(event_z)     # [:,:,:,:1000]\n        event_x = self.pos_embed_event(event_x)     # B 768 1024\n        x = self.patch_embed(x)\n        z = self.patch_embed(z)\n\n        event_z += self.pos_embed_z\n        event_x += self.pos_embed_x\n        z += self.pos_embed_z\n        x += self.pos_embed_x\n\n        # attention mask handling   # B, H, W\n        if mask_z is not None and mask_x is not None:\n            mask_z = F.interpolate(mask_z[None].float(), scale_factor=1. / self.patch_size).to(torch.bool)[0]\n            mask_z = mask_z.flatten(1).unsqueeze(-1)\n\n            mask_x = F.interpolate(mask_x[None].float(), scale_factor=1. / self.patch_size).to(torch.bool)[0]\n            mask_x = mask_x.flatten(1).unsqueeze(-1)\n\n            mask_x = combine_tokens(mask_z, mask_x, mode=self.cat_mode)\n            mask_x = mask_x.squeeze(-1)\n\n        if self.add_cls_token:\n            cls_tokens = self.cls_token.expand(B, -1, -1)\n            cls_tokens = cls_tokens + self.cls_pos_embed\n\n        if self.add_sep_seg:\n            x += self.search_segment_pos_embed\n            z += self.template_segment_pos_embed\n\n        x = combine_tokens(z, event_z, x, event_x, mode=self.cat_mode)        # 64+64+256+256=640\n        # x = combine_tokens(z, x, event_z, event_x, mode=self.cat_mode)        # 64+64+256+256=640\n        if self.add_cls_token:\n            x = torch.cat([cls_tokens, x], dim=1)\n\n        x = self.pos_drop(x)\n        lens_z = self.pos_embed_z.shape[1]\n        lens_x = self.pos_embed_x.shape[1]\n\n        global_index_t = torch.linspace(0, lens_z - 1, lens_z).to(x.device)\n        global_index_t = global_index_t.repeat(B, 1)\n\n        global_index_s = torch.linspace(0, lens_x - 1, lens_x).to(x.device)\n        global_index_s = global_index_s.repeat(B, 1)\n        removed_indexes_s = []\n        for i, blk in enumerate(self.blocks):\n            x, global_index_t, global_index_s, removed_index_s, attn = \\\n                blk(x, global_index_t, global_index_s, mask_x, ce_template_mask, ce_keep_rate)\n\n            if self.ce_loc is not None and i in self.ce_loc:\n                removed_indexes_s.append(removed_index_s)\n\n        x = self.norm(x)\n        lens_x_new = global_index_s.shape[1]\n        lens_z_new = global_index_t.shape[1]\n\n        z = x[:, :lens_z_new*2]\n        x = x[:, lens_z_new*2:]\n\n        if removed_indexes_s and removed_indexes_s[0] is not None:\n            removed_indexes_cat = torch.cat(removed_indexes_s, dim=1)\n\n            pruned_lens_x = lens_x - lens_x_new\n            pad_x = torch.zeros([B, pruned_lens_x, x.shape[2]], device=x.device)\n            x = torch.cat([x, pad_x], dim=1)\n            index_all = torch.cat([global_index_s, removed_indexes_cat], dim=1)\n            # recover original token order\n            C = x.shape[-1]\n            x = torch.zeros_like(x).scatter_(dim=1, index=index_all.unsqueeze(-1).expand(B, -1, C).to(torch.int64), src=x)\n\n        x = recover_tokens(x, lens_z_new, lens_x, mode=self.cat_mode)\n        x = x[:, :lens_x]   # RGB head\n        x = torch.cat([event_x, x], dim=1)\n        # x = x[:, lens_x//2:]  # event head\n        # x = torch.cat([z, x], dim=1)\n        # re-concatenate with the template, which may be further used by other modules\n        # x, event_x = x[:, :lens_x//2], x[:, lens_x//2:]\n        # x = x[:, -lens_x//2:]\n        aux_dict = {\n            \"attn\": attn,\n            \"removed_indexes_s\": removed_indexes_s,  # used for visualization\n        }\n\n        return x, aux_dict\n\n    def forward(self, z, x, event_z, event_x,\n                ce_template_mask=None, ce_keep_rate=None,\n                tnc_keep_rate=None,\n                return_last_attn=False):\n\n        x, aux_dict = self.forward_features(z, x, event_z, event_x, ce_template_mask=ce_template_mask, ce_keep_rate=ce_keep_rate,)\n\n        return x, aux_dict\n\n\ndef _create_vision_transformer(pretrained=False, **kwargs):\n    model = VisionTransformerCE(**kwargs)\n\n    if pretrained:\n        if 'npz' in pretrained:\n            model.load_pretrained(pretrained, prefix='')\n        else:\n            checkpoint = torch.load(pretrained, map_location=\"cpu\")\n            missing_keys, unexpected_keys = model.load_state_dict(checkpoint[\"model\"], strict=False)\n            print('Load pretrained model from: ' + pretrained)\n\n    return model\n\n\ndef vit_base_patch16_224_ce(pretrained=False, **kwargs):\n    \"\"\" ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).\n    \"\"\"\n    model_kwargs = dict(\n        patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)\n    model = _create_vision_transformer(pretrained=pretrained, **model_kwargs)\n    return model\n\n\ndef vit_large_patch16_224_ce(pretrained=False, **kwargs):\n    \"\"\" ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929).\n    \"\"\"\n    model_kwargs = dict(\n        patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs)\n    model = _create_vision_transformer(pretrained=pretrained, **model_kwargs)\n    return model  \uff0c\u6211\u4eec\u7684\u6a21\u578b\uff1a# \u5c06 4\u8f93\u5165\u5206\u5f00\uff0c\u6784\u5efa\u65b0\u7684\u76f8\u540c\u6a21\u6001\u7ed3\u5408\u76842\u8f93\u5165\uff0c2\u5206\u652f\nimport math\nimport logging\nfrom functools import partial\nfrom collections import OrderedDict\nfrom copy import deepcopy\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom timm.models.layers import to_2tuple\n\nfrom lib.models.layers.patch_embed import PatchEmbed, PatchEmbed_event, xcorr_depthwise\nfrom .utils import combine_tokens, recover_tokens\nfrom .vit import VisionTransformer\nfrom ..layers.attn_blocks import CEBlock\n\n_logger = logging.getLogger(__name__)\n                            \n\nclass VisionTransformerCE_S(VisionTransformer):\n    \"\"\" Vision Transformer with candidate elimination (CE) module\n\n    A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`\n        - https://arxiv.org/abs/2010.11929\n\n    Includes distillation token & head support for `DeiT: Data-efficient Image Transformers`\n        - https://arxiv.org/abs/2012.12877\n    \"\"\"\n\n    def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,\n                 num_heads=12, mlp_ratio=4., qkv_bias=True, representation_size=None, distilled=False,\n                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0., embed_layer=PatchEmbed, norm_layer=None,\n                 act_layer=None, weight_init='',\n                 ce_loc=None, ce_keep_ratio=None):\n        super().__init__()\n        if isinstance(img_size, tuple):\n            self.img_size = img_size\n        else:\n            self.img_size = to_2tuple(img_size)\n        self.patch_size = patch_size\n        self.in_chans = in_chans\n\n        self.num_classes = num_classes\n        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models\n        self.num_tokens = 2 if distilled else 1\n        norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)\n        act_layer = act_layer or nn.GELU\n\n        self.patch_embed = embed_layer(\n            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)\n        num_patches = self.patch_embed.num_patches\n\n        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))\n        self.dist_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if distilled else None\n        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim))\n        self.pos_drop = nn.Dropout(p=drop_rate)\n        self.pos_embed_event = PatchEmbed_event(in_chans=32, embed_dim=768, kernel_size=4, stride=4)\n        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule\n        blocks = []\n        ce_index = 0\n        self.ce_loc = ce_loc\n        for i in range(depth):\n            ce_keep_ratio_i = 1.0\n            if ce_loc is not None and i in ce_loc:\n                ce_keep_ratio_i = ce_keep_ratio[ce_index]\n                ce_index += 1\n\n            blocks.append(\n                CEBlock(\n                    dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate,\n                    attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer,\n                    keep_ratio_search=ce_keep_ratio_i)\n            )\n\n        self.blocks = nn.Sequential(*blocks)\n        self.norm = norm_layer(embed_dim)\n\n        self.init_weights(weight_init)\n    \n    \n    def forward_features(self, z, x, event_z, event_x,\n                         mask_z=None, mask_x=None,\n                         ce_template_mask=None, \n                         ce_keep_rate=None,\n                         return_last_attn=False\n                         ):\n        # \u5206\u652f1 \u5904\u7406\u6d41\u7a0b\n        B = x.shape[0]\n        x = self.patch_embed(x)\n        z = self.patch_embed(z)\n        x += self.pos_embed_x\n        z += self.pos_embed_z\n        if mask_z is not None and mask_x is not None:\n            mask_z = F.interpolate(mask_z[None].float(), scale_factor=1. / self.patch_size).to(torch.bool)[0]\n            mask_z = mask_z.flatten(1).unsqueeze(-1)\n\n            mask_x = F.interpolate(mask_x[None].float(), scale_factor=1. / self.patch_size).to(torch.bool)[0]\n            mask_x = mask_x.flatten(1).unsqueeze(-1)\n\n            mask_x = combine_tokens(mask_z, mask_x, mode=self.cat_mode)\n            mask_x = mask_x.squeeze(-1)\n        if self.add_cls_token:\n            cls_tokens = self.cls_token.expand(B, -1, -1)\n            cls_tokens = cls_tokens + self.cls_pos_embed\n        if self.add_sep_seg:\n            x += self.search_segment_pos_embed\n            z += self.template_segment_pos_embed\n        x_combined = torch.cat([z, x], dim=1)    # student  64+256\n        if self.add_cls_token:\n            x_combined = torch.cat([cls_tokens, x_combined], dim=1)\n        x_combined = self.pos_drop(x_combined)\n        lens_z = self.pos_embed_z.shape[1]\n        lens_x = self.pos_embed_x.shape[1]\n        global_index_t = torch.linspace(0, lens_z - 1, lens_z).to(x.device)\n        global_index_t = global_index_t.repeat(B, 1)\n        global_index_s = torch.linspace(0, lens_x - 1, lens_x).to(x.device)\n        global_index_s = global_index_s.repeat(B, 1)\n        removed_indexes_s = []\n        for i, blk in enumerate(self.blocks):\n            x_combined, global_index_t, global_index_s, removed_index_s, attn = blk(x_combined, global_index_t, global_index_s, mask_x, ce_template_mask, ce_keep_rate)\n            \n            if self.ce_loc is not None and i in self.ce_loc:\n                removed_indexes_s.append(removed_index_s)\n        x_combined = self.norm(x_combined)\n        x = x_combined  # [bs, n_patch, dim] = [bs, 320, 768] 320 = 64 + 256\n        # \u5206\u652f2 \u5904\u7406\u6d41\u7a0b\n        event_x = self.pos_embed_event(event_x)\n        event_z = self.pos_embed_event(event_z)\n        event_x += self.pos_embed_x\n        event_z += self.pos_embed_z\n        if self.add_cls_token:\n            cls_tokens = self.cls_token.expand(B, -1, -1)\n            cls_tokens = cls_tokens + self.cls_pos_embed\n\n        if self.add_sep_seg:\n            event_x += self.search_segment_pos_embed\n            event_z += self.template_segment_pos_embed\n        event_combined = torch.cat([event_z, event_x], dim=1)    # student  64+256\n        if self.add_cls_token:\n            event_combined = torch.cat([cls_tokens, event_combined], dim=1)\n        event_combined = self.pos_drop(event_combined)\n        global_index_t1 = torch.linspace(0, lens_z - 1, lens_z).to(event_x.device)\n        global_index_t1 = global_index_t1.repeat(B, 1)\n        global_index_s1 = torch.linspace(0, lens_x - 1, lens_x).to(event_x.device)\n        global_index_s1 = global_index_s1.repeat(B, 1)\n        removed_indexes_s1 = []\n        for i, blk in enumerate(self.blocks):\n            event_combined, global_index_t1, global_index_s1, removed_index_s1, event_attn = blk(event_combined, global_index_t1, global_index_s1, mask_x, ce_template_mask, ce_keep_rate)\n            if self.ce_loc is not None and i in self.ce_loc:\n                removed_indexes_s1.append(removed_index_s1)\n        event_combined = self.norm(event_combined)\n        event_x = event_combined\n\n        return x, event_x, attn, event_attn\n\n    def forward(self,z,x,event_z,event_x,\n                ce_template_mask=None, \n                ce_keep_rate=None,\n                return_last_attn=False):\n        \n        x, event_x, attn, event_attn = self.forward_features(z, x,event_z,event_x,\n                                                             ce_template_mask, ce_keep_rate,\n                                                             return_last_attn)\n        return x, event_x, attn, event_attn\n\n\ndef _create_vision_transformer(pretrained=False, **kwargs):\n    model = VisionTransformerCE_S(**kwargs)\n    \n    if pretrained:\n        if 'npz' in pretrained:\n            model.load_pretrained(pretrained, prefix='')\n        else:\n            checkpoint = torch.load(pretrained, map_location=\"cpu\")\n            missing_keys, unexpected_keys = model.load_state_dict(checkpoint[\"model\"], strict=False)\n            print('Load pretrained model from: ' + pretrained)\n\n    return model\n\n\ndef vit_base_patch16_224_ce_s(pretrained=False, **kwargs):\n    \"\"\" ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).\n    \"\"\"\n    model_kwargs = dict(\n        patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)\n    model = _create_vision_transformer(pretrained=pretrained, **model_kwargs)\n    return model\n\n\ndef vit_large_patch16_224_ce_s(pretrained=False, **kwargs):\n    \"\"\" ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929).\n    \"\"\"\n    model_kwargs = dict(\n        patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs)\n    model = _create_vision_transformer(pretrained=pretrained, **model_kwargs)\n    return model \u6709\u4ec0\u4e48\u4f18\u52bf\u548c\u52a3\u52bf\uff0c\u6216\u8005\u6211\u4eec\u7684\u4fee\u6539\u6a21\u578b\u9700\u8981\u4e00\u4e2a\u5408\u7406\u7684\u52a8\u673a"},{"assistant_initiates":false,"asst_text":"\u5c06\u4e0d\u540c\u6a21\u6001\u6570\u636e\u5355\u72ec\u5904\u7406\u5e76\u5728\u540e\u7eed\u6b65\u9aa4\u4e2d\u589e\u52a0\u6a21\u6001\u95f4\u7684\u4fe1\u606f\u4ea4\u4e92\uff0c\u662f\u4e00\u79cd\u5728\u4fdd\u6301\u6a21\u6001\u7279\u5f02\u6027\u5904\u7406\u7684\u540c\u65f6\u4fc3\u8fdb\u6a21\u6001\u95f4\u878d\u5408\u7684\u7b56\u7565\u3002\u8fd9\u79cd\u8bbe\u8ba1\u4e0e\u539f\u59cbVision Transformer (ViT) CE\u6a21\u578b\u76f8\u6bd4\uff0c\u5177\u6709\u5176\u72ec\u7279\u7684\u4f18\u52bf\u548c\u52a3\u52bf\uff1a\n\n### \u4f18\u52bf\n\n1. **\u4e13\u6ce8\u7684\u6a21\u6001\u7279\u5b9a\u5904\u7406**\uff1a\u901a\u8fc7\u5355\u72ec\u5904\u7406\u6bcf\u79cd\u6a21\u6001\u7684\u6570\u636e\uff0c\u53ef\u4ee5\u9488\u5bf9\u6bcf\u79cd\u6570\u636e\u7684\u7279\u6709\u7279\u5f81\u548c\u6027\u8d28\u8bbe\u8ba1\u6700\u5408\u9002\u7684\u5904\u7406\u6d41\u7a0b\uff0c\u4ece\u800c\u63d0\u9ad8\u6a21\u578b\u5bf9\u4e8e\u6bcf\u79cd\u6a21\u6001\u4fe1\u606f\u7684\u628a\u63e1\u80fd\u529b\u3002\n\n2. **\u7075\u6d3b\u7684\u6a21\u6001\u878d\u5408**\uff1a\u5728\u6a21\u6001\u7279\u5b9a\u5904\u7406\u4e4b\u540e\u5f15\u5165\u4fe1\u606f\u4ea4\u4e92\u6b65\u9aa4\uff0c\u4f7f\u5f97\u6a21\u578b\u5728\u4fdd\u7559\u4e86\u5bf9\u5355\u4e00\u6a21\u6001\u6df1\u5165\u7406\u89e3\u7684\u540c\u65f6\uff0c\u8fd8\u80fd\u7075\u6d3b\u5730\u63a2\u7d22\u548c\u5229\u7528\u6a21\u6001\u95f4\u7684\u4e92\u8865\u4fe1\u606f\u3002\u8fd9\u6837\u7684\u878d\u5408\u7b56\u7565\u53ef\u80fd\u66f4\u52a0\u6709\u9488\u5bf9\u6027\uff0c\u4ece\u800c\u63d0\u9ad8\u6700\u7ec8\u4efb\u52a1\u7684\u6027\u80fd\u3002\n\n3. **\u53ef\u89e3\u91ca\u6027\u63d0\u5347**\uff1a\u5206\u9636\u6bb5\u5904\u7406\u548c\u878d\u5408\u4e0d\u540c\u6a21\u6001\u6570\u636e\u53ef\u80fd\u589e\u5f3a\u6a21\u578b\u7684\u53ef\u89e3\u91ca\u6027\uff0c\u56e0\u4e3a\u6a21\u578b\u7684\u6bcf\u4e00\u90e8\u5206\u90fd\u6709\u660e\u786e\u7684\u804c\u8d23\uff0c\u5982\u6a21\u6001\u7279\u5b9a\u7279\u5f81\u63d0\u53d6\u3001\u6a21\u6001\u95f4\u7684\u4fe1\u606f\u4ea4\u6362\u7b49\u3002\n\n4. **\u8c03\u4f18\u7075\u6d3b\u6027**\uff1a\u8fd9\u79cd\u8bbe\u8ba1\u63d0\u4f9b\u4e86\u989d\u5916\u7684\u8c03\u4f18\u70b9\uff0c\u6bd4\u5982\u5728\u6a21\u6001\u7279\u5b9a\u5904\u7406\u548c\u6a21\u6001\u95f4\u4ea4\u4e92\u4e4b\u95f4\u7684\u5e73\u8861\uff0c\u53ef\u4ee5\u6839\u636e\u4e0d\u540c\u4efb\u52a1\u7684\u9700\u6c42\u8fdb\u884c\u66f4\u7ec6\u81f4\u7684\u4f18\u5316\u3002\n\n### \u52a3\u52bf\n\n1. **\u8bbe\u8ba1\u548c\u5b9e\u73b0\u590d\u6742\u5ea6\u589e\u52a0**\uff1a\u76f8\u6bd4\u76f4\u63a5\u5904\u7406\u6240\u6709\u8f93\u5165\u6570\u636e\uff0c\u5206\u6b65\u9aa4\u5904\u7406\u4e0d\u540c\u6a21\u6001\u53ca\u5176\u95f4\u7684\u4fe1\u606f\u4ea4\u4e92\u589e\u52a0\u4e86\u6a21\u578b\u7684\u8bbe\u8ba1\u548c\u5b9e\u73b0\u590d\u6742\u5ea6\u3002\n\n2. **\u8bad\u7ec3\u96be\u5ea6\u53ef\u80fd\u589e\u52a0**\uff1a\u7531\u4e8e\u5f15\u5165\u4e86\u6a21\u6001\u95f4\u4fe1\u606f\u4ea4\u4e92\u673a\u5236\uff0c\u53ef\u80fd\u9700\u8981\u66f4\u7cbe\u7ec6\u7684\u8bad\u7ec3\u7b56\u7565\u6765\u786e\u4fdd\u8fd9\u4e9b\u4ea4\u4e92\u80fd\u591f\u6709\u6548\u53d1\u6325\u4f5c\u7528\uff0c\u8fd9\u53ef\u80fd\u589e\u52a0\u8bad\u7ec3\u96be\u5ea6\u3002\n\n3. **\u8ba1\u7b97\u5f00\u9500\u589e\u52a0**\uff1a\u5206\u522b\u5904\u7406\u6bcf\u79cd\u6a21\u6001\u6570\u636e\u4e4b\u540e\u518d\u8fdb\u884c\u4fe1\u606f\u4ea4\u4e92\uff0c\u53ef\u80fd\u4f1a\u589e\u52a0\u4e00\u4e9b\u8ba1\u7b97\u5f00\u9500\uff0c\u7279\u522b\u662f\u5f53\u6a21\u6001\u95f4\u4ea4\u4e92\u673a\u5236\u8f83\u4e3a\u590d\u6742\u65f6\u3002\n\n4. **\u6a21\u6001\u95f4\u4f9d\u8d56\u6027\u98ce\u9669**\uff1a\u8fc7\u5ea6\u4f9d\u8d56\u6a21\u6001\u95f4\u7684\u4fe1\u606f\u4ea4\u4e92\u53ef\u80fd\u5bfc\u81f4\u5728\u67d0\u4e9b\u6a21\u6001\u6570\u636e\u7f3a\u5931\u6216\u8d28\u91cf\u8f83\u5dee\u65f6\uff0c\u6a21\u578b\u6027\u80fd\u5927\u5e45\u4e0b\u964d\u3002\n\n### \u8bbe\u8ba1\u6539\u8fdb\u7684\u52a8\u673a\n\n\u91c7\u53d6\u8fd9\u6837\u7684\u7b56\u7565\u8bbe\u8ba1\uff0c\u53cd\u6620\u4e86\u8bd5\u56fe\u5728\u7ef4\u6301\u6a21\u6001\u72ec\u7acb\u6027\u5904\u7406\u7684\u57fa\u7840\u4e0a\uff0c\u63a2\u7d22\u6a21\u6001\u95f4\u7684\u4e92\u8865\u6027\uff0c\u4ee5\u5b9e\u73b0\u66f4\u5168\u9762\u7684\u4efb\u52a1\u7406\u89e3\u548c\u6027\u80fd\u63d0\u5347\u7684\u52a8\u673a\u3002\u5b83\u57fa\u4e8e\u7684\u5047\u8bbe\u662f\uff0c\u901a\u8fc7\u9996\u5148\u63d0\u53d6\u6bcf\u79cd\u6a21\u6001\u7684\u6df1\u5c42\u7279\u5f81\uff0c\u7136\u540e\u5728\u4e00\u4e2a\u6216\u591a\u4e2a\u540e\u7eed\u6b65\u9aa4\u4e2d\u4fc3\u8fdb\u8fd9\u4e9b\u7279\u5f81\u7684\u878d\u5408\uff0c\u53ef\u4ee5\u66f4\u6709\u6548\u5730\u5229\u7528\u591a\u6a21\u6001\u6570\u636e\u7684\u6f5c\u529b\u3002\u8fd9\u6837\u7684\u8bbe\u8ba1\u601d\u8def\u6709\u671b\u5728\u591a\u6a21\u6001\u5b66\u4e60\u4efb\u52a1\u4e2d\uff0c\u5982\u89c6\u89c9\u95ee\u7b54\uff08VQA\uff09\u548c\u673a\u5668\u4eba\u611f\u77e5\u7b49\u590d\u6742\u573a\u666f\u4e0b\uff0c\u63d0\u4f9b\u66f4\u4f18\u7684\u6027\u80fd\u3002","behavior":"degenerate_repetition","conversation_hash":"14db8724701ffbfd25fef4b777a282ee","conversation_redacted":false,"conversation_toxic":false,"country":"United States","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":972,"n_turns_in_conversation":12,"n_words":28,"source_row_offset":82409,"source_shard":"turns-00019.parquet","timestamp":"Fri, 12 Apr 2024 09:11:20 GMT","turn_id":"13c6c2cba0212a7702e70895","turn_index":10,"user_text":"\u6211\u4eec\u5c06\u4e0d\u540c\u6a21\u6001\u7684\u6570\u636e\u5355\u72ec\u5904\u7406\uff0c\u4ec5\u4ec5\u662f\u7b2c\u4e00\u6b65\uff0c\u63a5\u7740\u6211\u4eec\u5c06\u589e\u52a0\u4e0d\u540c\u6a21\u6001\u4e4b\u95f4\u7684\u4fe1\u606f\u4ea4\u4e92\uff0c\u90a3\u4e48\u8fd9\u6837\u7684\u8bbe\u8ba1\u8f83\u539f\u59cbvit_ce\u7684\u4f18\u52bf\u548c\u52a3\u52bf\u662f\u4ec0\u4e48\uff1f"},{"assistant_initiates":false,"asst_text":"\u5c06\u539f\u6709\u7684Vision Transformer with Candidate Elimination (ViT-CE) \u6a21\u578b\uff0c\u4ece\u5904\u7406\u56db\u4e2a\u878d\u5408\u8f93\u5165\u5230\u4e24\u4e2a\u72ec\u7acb\u5206\u652f\u5904\u7406\u4e0d\u540c\u6a21\u6001\u7684\u8f6c\u53d8\uff0c\u4ee3\u8868\u4e86\u4e00\u79cd\u4ece\u96c6\u4e2d\u5f0f\u5904\u7406\u5230\u6a21\u5757\u5316\u3001\u5206\u5e03\u5f0f\u5904\u7406\u7684\u7b56\u7565\u3002\u4ee5\u4e0b\u662f\u8fd9\u79cd\u6539\u53d8\u53ef\u80fd\u5e26\u6765\u7684\u5f71\u54cd\u548c\u5206\u6790\uff1a\n\n### \u539f\u59cbViT-CE\u7684\u5904\u7406\u65b9\u5f0f\uff1a\n\n\u5728\u539f\u59cb\u7684ViT-CE\u6a21\u578b\u4e2d\uff0c\u56db\u4e2a\u8f93\u5165\uff08\u5047\u8bbe\u4e3a\u4e24\u79cd\u6a21\u6001\u7684\u4e00\u5bf9\u6a21\u677f\u56fe\u50cf\u548c\u641c\u7d22\u56fe\u50cf\uff09\u88ab\u5408\u5e76\u6216\u4ee5\u67d0\u79cd\u65b9\u5f0f\u878d\u5408\u540e\u901a\u8fc7\u4e00\u4e2a\u7edf\u4e00\u7684Transformer backbone\u8fdb\u884c\u7279\u5f81\u63d0\u53d6\u3002\u8fd9\u79cd\u65b9\u5f0f\u81f4\u529b\u4e8e\u76f4\u63a5\u5728\u539f\u59cb\u6570\u636e\u7ea7\u522b\u5229\u7528\u8fd9\u4e9b\u4e0d\u540c\u6a21\u6001\u7684\u5408\u5e76\u7279\u5f81\uff0c\u4ece\u800c\u8fdb\u884c\u4e0b\u6e38\u4efb\u52a1\u7684\u5904\u7406\u3002\n\n### \u6539\u53d8\u4e3a\u4e24\u4e2a\u72ec\u7acb\u5206\u652f\uff1a\n\n\u5728\u4fee\u6539\u540e\u7684\u7ed3\u6784\u4e2d\uff0c\u4e24\u79cd\u6a21\u6001\u7684\u6570\u636e\u5206\u522b\u901a\u8fc7\u4e24\u4e2a\u76f8\u540c\u7684ViT\u6a21\u578b\uff08\u6216\u5206\u652f\uff09\u8fdb\u884c\u5904\u7406\uff0c\u6bcf\u4e2a\u5206\u652f\u72ec\u7acb\u5730\u5904\u7406\u4e00\u79cd\u7c7b\u578b\u7684\u6a21\u6001\u6570\u636e\u3002\n\n### \u5206\u6790\uff1a\n\n**\u4f18\u52bf**\n\n1. **\u6a21\u6001\u7279\u5b9a\u7684\u5904\u7406**\uff1a\u6bcf\u4e2a\u5206\u652f\u53ef\u4ee5\u4e13\u6ce8\u4e8e\u5bf9\u7279\u5b9a\u6a21\u6001\u7684\u4f18\u5316\u5904\u7406\uff0c\u5141\u8bb8\u6a21\u578b\u66f4\u6df1\u5165\u5730\u5b66\u4e60\u6bcf\u79cd\u6a21\u6001\u7684\u72ec\u7279\u7279\u6027\u548c\u6a21\u5f0f\u3002\n\n2. **\u7075\u6d3b\u5ea6\u4e0e\u53ef\u6269\u5c55\u6027**\uff1a\u8fd9\u79cd\u5206\u652f\u5f0f\u67b6\u6784\u7684\u8bbe\u8ba1\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7684\u7075\u6d3b\u5ea6\u548c\u53ef\u6269\u5c55\u6027\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u9488\u5bf9\u7279\u5b9a\u6a21\u6001\u5f15\u5165\u66f4\u9002\u5408\u7684\u524d\u5904\u7406\u6b65\u9aa4\u6216\u7279\u5b9a\u7684\u7f51\u7edc\u67b6\u6784\u6539\u8fdb\u3002\n\n3. **\u5e76\u884c\u5904\u7406**\uff1a\u4e24\u4e2a\u5206\u652f\u53ef\u4ee5\u5e76\u884c\u5904\u7406\uff0c\u63d0\u5347\u8ba1\u7b97\u6548\u7387\uff0c\u5c24\u5176\u662f\u5728\u591aGPU\u73af\u5883\u4e0b\u3002\n\n4. **\u8bef\u5dee\u9694\u79bb**\uff1a\u5f53\u67d0\u4e00\u6a21\u6001\u7684\u6570\u636e\u8d28\u91cf\u8f83\u4f4e\u6216\u5728\u7279\u5b9a\u60c5\u51b5\u4e0b\u65e0\u6cd5\u83b7\u53d6\u65f6\uff0c\u8be5\u6a21\u6001\u7684\u5904\u7406\u5206\u652f\u53ef\u80fd\u5bf9\u6574\u4f53\u6027\u80fd\u5f71\u54cd\u8f83\u5c0f\uff0c\u8fd9\u6709\u52a9\u4e8e\u6539\u5584\u6a21\u578b\u7684\u9c81\u68d2\u6027\u3002\n\n**\u52a3\u52bf**\n\n1. **\u6a21\u6001\u95f4\u4e92\u8865\u4fe1\u606f\u7684\u521d\u671f\u7f3a\u5931**\uff1a\u65e9\u671f\u9636\u6bb5\u7f3a\u4e4f\u4e0d\u540c\u6a21\u6001\u4e4b\u95f4\u7684\u4fe1\u606f\u4ea4\u4e92\uff0c\u53ef\u80fd\u672a\u80fd\u5145\u5206\u5229\u7528\u6a21\u6001\u95f4\u7684\u4e92\u8865\u6027\uff0c\u8fd9\u53ef\u80fd\u5728\u67d0\u79cd\u7a0b\u5ea6\u4e0a\u9650\u5236\u4e86\u6a21\u578b\u521d\u671f\u7279\u5f81\u63d0\u53d6\u7684\u8868\u73b0\u3002\n\n2. **\u589e\u52a0\u4e86\u878d\u5408\u8bbe\u8ba1\u7684\u590d\u6742\u6027**\uff1a\u867d\u7136\u5206\u652f\u72ec\u7acb\u5904\u7406\u7b80\u5316\u4e86\u6a21\u6001\u5185\u7684\u5904\u7406\uff0c\u4f46\u589e\u52a0\u4e86\u540e\u7eed\u5982\u4f55\u6709\u6548\u6574\u5408\u548c\u5229\u7528\u8fd9\u4e9b\u72ec\u7acb\u7279\u5f81\u7684\u8bbe\u8ba1\u96be\u5ea6\u548c\u590d\u6742\u6027\u3002\n\n3. **\u8d44\u6e90\u6d88\u8017**\uff1a\u4e24\u4e2a\u72ec\u7acb\u5206\u652f\u5904\u7406\u76f8\u540c\u7684ViT\u6a21\u578b\u53ef\u80fd\u4f1a\u6bd4\u5355\u4e00\u878d\u5408\u8f93\u5165\u7684\u6a21\u578b\u6d88\u8017\u66f4\u591a\u8ba1\u7b97\u8d44\u6e90\u3002\n\n### \u7ed3\u8bba\n\n\u91c7\u53d6\u5c06\u4e0d\u540c\u6a21\u6001\u6570\u636e\u5206\u4e3a\u4e24\u4e2a\u72ec\u7acb\u5206\u652f\u7684\u8bbe\u8ba1\uff0c\u5f15\u5165\u4e86\u5728\u7279\u5b9a\u6a21\u6001\u5904\u7406\u4f18\u5316\u4e0e\u6a21\u6001\u95f4\u4fe1\u606f\u4ea4\u4e92\u5e73\u8861\u4e4b\u95f4\u7684\u65b0\u6311\u6218\u3002\u968f\u540e\u7684\u6a21\u578b\u8bbe\u8ba1\u9700\u8981\u7740\u91cd\u8003\u8651\u5982\u4f55\u6709\u6548\u5730\u5728\u8fd9\u4e9b\u72ec\u7acb\u5904\u7406\u7684\u7279\u5f81\u4e0a\u5b9e\u65bd\u4ea4\u4e92\u673a\u5236\uff0c\u4ee5\u786e\u4fdd\u6a21\u578b\u80fd\u591f\u5145\u5206\u5229\u7528\u4e0d\u540c\u6a21\u6001\u95f4\u7684\u4e92\u8865\u4fe1\u606f\uff0c\u4ece\u800c\u5b9e\u73b0\u66f4\u597d\u7684\u603b\u4f53\u6027\u80fd\u3002\u8fd9\u79cd\u8bbe\u8ba1\u7b56\u7565\u7684\u5b9e\u9645\u8868\u73b0\u5c06\u9ad8\u5ea6\u4f9d\u8d56\u4e8e\u7279\u5b9a\u4efb\u52a1\u7684\u9700\u6c42\u4ee5\u53ca\u5982\u4f55\u5b9e\u73b0\u9ad8\u6548\u800c\u6709\u610f\u4e49\u7684\u6a21\u6001\u95f4\u4fe1\u606f\u878d\u5408\u3002","behavior":"degenerate_repetition","conversation_hash":"14db8724701ffbfd25fef4b777a282ee","conversation_redacted":false,"conversation_toxic":false,"country":"United States","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":1085,"n_turns_in_conversation":12,"n_words":36,"source_row_offset":82410,"source_shard":"turns-00019.parquet","timestamp":"Fri, 12 Apr 2024 09:11:20 GMT","turn_id":"ebd9d82bbdf25cc46ea70055","turn_index":11,"user_text":"\u90a3\u4e48\u5c31\u5148\u5206\u6790\u4e00\u4e0b\uff1a\u539f\u59cb\u7684vit_ce\u4e2d4\u4e2a\u8f93\u5165\u7ecf\u8fc7backbone\u5f97\u5230\u4e00\u4e2a\u7edf\u4e00\u7684features\uff0c\u6211\u4eec\u5c062\u79cd\u6a21\u6001\u5206\u4e3a2\u4e2a\u5206\u652f\uff0c\u6267\u884c\u76f8\u540c\u7684vit\u6a21\u578b"},{"assistant_initiates":false,"asst_text":"/imagine prompt: Illustrate a cover image split in the middle portraying two couples; on the left, an Asian couple with the man leading the woman up an escalator, the woman holding flowers and numerous gifts, on the right, another Asian couple with the woman leading the man up the escalator, the man holding various electronic gadgets. The image should be realistic with a rich background, distant view, in a 3:4 aspect ratio. --ar 3:4 --v 5\n\n/imagine prompt: Create an image showcasing two sets of Asian couples on an escalator; the left couple consists of a man guiding a woman with flowers and gifts, while the right couple depicts the woman leading a man with electronic gadgets. The scene should have a detailed background, a distant perspective, and a 3:4 aspect ratio. --ar 3:4 --v 5\n\n/imagine prompt: Visualize a split cover image depicting two pairs of Asian couples on an escalator; the left couple shows a man guiding a woman holding flowers and gifts, while the right couple portrays a woman leading a man with electronic gadgets. The setting should be realistic with a detailed background, distant view, in a 3:4 aspect ratio. --ar 3:4 --v 5\n\n/imagine prompt: Design an image with a split cover format featuring two Asian couples on an escalator; one pair shows a man leading a woman with flowers and gifts, the other depicts a woman leading a man with electronic gadgets. The image should be realistic, with a rich background, distant perspective, and a 3:4 aspect ratio. --ar 3:4 --v 5","behavior":"degenerate_repetition","conversation_hash":"df750ee60787c897af27b50a8316f940","conversation_redacted":false,"conversation_toxic":false,"country":"Singapore","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":5406,"n_turns_in_conversation":1,"n_words":710,"source_row_offset":82411,"source_shard":"turns-00019.parquet","timestamp":"Fri, 12 Apr 2024 09:13:06 GMT","turn_id":"2fc41efaf573debc100b784d","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] = \u56fe\u72471: \u5e2e\u6211\u753b\u4e00\u4e2a\u5c01\u9762\u56fe \u4ece\u4e2d\u95f4\u5206\u5f00\u91cc\u9762\u6709\u4e24\u5bf9\u60c5\u4fa3\uff0c\u5de6\u8fb9\u662f\u4e00\u5bf9\u4e9a\u6d32\u60c5\u4fa3\uff0c\u7537\u751f\u7275\u7740\u5973\u751f\u5728\u7535\u68af\u6b65\u68af\u4e0a\uff0c\u5973\u751f\u624b\u4e0a\u6709\u9c9c\u82b1\uff0c\u548c\u975e\u5e38\u591a\u7684\u793c\u7269\uff1b\u53f3\u8fb9\u662f\u4e00\u5bf9\u4e9a\u6d32\u60c5\u4fa3\uff0c\u5973\u751f\u7275\u7740\u7537\u751f\u5728\u7535\u68af\u6b65\u68af\uff0c\u7537\u751f\u624b\u91cc\u6709\u5f88\u591a\u7535\u5b50\u4ea7\u54c1\u3002\u903c\u771f\u5f62\u8c61\uff0c\u80cc\u666f\u4e30\u5bcc\uff0c\u8fdc\u666f\uff0c3:4\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":"1. Expliquer la diff\u00e9rence entre un syst\u00e8me d'exploitation Windows et un syst\u00e8me d'exploitation Linux.\n\nR\u00e9ponse: Un syst\u00e8me d'exploitation Windows est un syst\u00e8me d'exploitation propri\u00e9taire d\u00e9velopp\u00e9 par Microsoft, tandis qu'un syst\u00e8me d'exploitation Linux est un syst\u00e8me d'exploitation libre et open source bas\u00e9 sur le noyau Linux.\n\n2. Qu'est-ce qu'un navigateur web et donnez des exemples de navigateurs populaires.\n\nR\u00e9ponse: Un navigateur web est un logiciel utilis\u00e9 pour acc\u00e9der \u00e0 des pages web sur Internet. Exemples de navigateurs populaires : Google Chrome, Mozilla Firefox, Safari, Microsoft Edge.\n\n3. Qu'est-ce qu'un algorithme et pourquoi est-il important en informatique ?\n\nR\u00e9ponse: Un algorithme est une s\u00e9quence d'instructions pr\u00e9cises pour r\u00e9soudre un probl\u00e8me ou effectuer une t\u00e2che. Il est important en informatique car il permet de structurer et d'optimiser les processus de traitement des donn\u00e9es.\n\n4. Quelle est la diff\u00e9rence entre un logiciel de gestion de base de donn\u00e9es et un tableur ?\n\nR\u00e9ponse: Un logiciel de gestion de base de donn\u00e9es est utilis\u00e9 pour stocker, organiser et manipuler des donn\u00e9es structur\u00e9es, tandis qu'un tableur est un outil permettant de manipuler des donn\u00e9es de mani\u00e8re tabulaire.\n\n5. Expliquer ce qu'est un r\u00e9seau informatique et donner des exemples de types de r\u00e9seaux.\n\nR\u00e9ponse: Un r\u00e9seau informatique est un ensemble de dispositifs interconnect\u00e9s permettant le partage de ressources et d'informations. Exemples de types de r\u00e9seaux : LAN (r\u00e9seau local), WAN (r\u00e9seau \u00e9tendu), WLAN (r\u00e9seau local sans fil).","behavior":"degenerate_repetition","conversation_hash":"8cfee39339dffa8e507b5af727d8da52","conversation_redacted":false,"conversation_toxic":false,"country":"DR Congo","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"French","model":"gpt-3.5-turbo-0125","n_chars":1673,"n_turns_in_conversation":3,"n_words":248,"source_row_offset":82412,"source_shard":"turns-00019.parquet","timestamp":"Fri, 12 Apr 2024 09:13:20 GMT","turn_id":"27d2edd43cf6c2067bef4542","turn_index":0,"user_text":"proposer moi les items d'examens d'\u00e9tat de l'informatique avec des r\u00e9ponses  pour les \u00e9l\u00e8ves de la commerciale gestion "},{"assistant_initiates":false,"asst_text":"1. Qu'est-ce qu'un ordinateur et quelles sont ses principales composantes ?\n\nR\u00e9ponse: Un ordinateur est une machine \u00e9lectronique capable d'ex\u00e9cuter des instructions pour traiter des donn\u00e9es. Ses principales composantes sont le processeur, la m\u00e9moire (RAM), le disque dur, la carte m\u00e8re et les p\u00e9riph\u00e9riques d'entr\u00e9e/sortie.\n\n2. Quelle est la diff\u00e9rence entre un logiciel et un mat\u00e9riel informatique ?\n\nR\u00e9ponse: Le mat\u00e9riel informatique d\u00e9signe les composantes physiques d'un ordinateur, tandis que le logiciel est un programme informatique qui permet d'ex\u00e9cuter des t\u00e2ches sp\u00e9cifiques sur l'ordinateur.\n\n3. Quels sont les principaux syst\u00e8mes d'exploitation utilis\u00e9s sur les ordinateurs et donnez un exemple de chacun ?\n\nR\u00e9ponse: Les principaux syst\u00e8mes d'exploitation sont Windows (exemple: Windows 10), macOS (exemple: macOS Mojave) et Linux (exemple: Ubuntu).\n\n4. Qu'est-ce qu'un navigateur web et comment utilise-t-on un moteur de recherche ?\n\nR\u00e9ponse: Un navigateur web est un logiciel permettant d'acc\u00e9der \u00e0 des pages web sur Internet. Pour utiliser un moteur de recherche, il suffit de saisir des mots-cl\u00e9s dans la barre de recherche du moteur pour obtenir des r\u00e9sultats pertinents.\n\n5. Expliquer ce qu'est un virus informatique et comment se prot\u00e9ger contre les menaces informatiques.\n\nR\u00e9ponse: Un virus informatique est un programme malveillant con\u00e7u pour endommager ou voler des donn\u00e9es sur un ordinateur. Pour se prot\u00e9ger contre les menaces informatiques, il est important d'installer un antivirus, de ne pas ouvrir des pi\u00e8ces jointes suspectes et de ne pas t\u00e9l\u00e9charger des logiciels provenant de sources non fiables.","behavior":"degenerate_repetition","conversation_hash":"8cfee39339dffa8e507b5af727d8da52","conversation_redacted":false,"conversation_toxic":false,"country":"DR Congo","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"French","model":"gpt-3.5-turbo-0125","n_chars":1718,"n_turns_in_conversation":3,"n_words":252,"source_row_offset":82413,"source_shard":"turns-00019.parquet","timestamp":"Fri, 12 Apr 2024 09:13:20 GMT","turn_id":"ae7cd3f33b0222075017b6d7","turn_index":1,"user_text":"items d'informatique  avec des accessions pour les \u00e9l\u00e8ves de 6 iem commerciale et gestion "},{"assistant_initiates":false,"asst_text":"Voici quelques th\u00e8mes d'informatique sp\u00e9cifiquement pour les \u00e9l\u00e8ves de 6\u00e8me humanit\u00e9s commerciale en RDC pour les ann\u00e9es 2020, 2021, 2022 et 2023:\n\n1. Les principaux composants d'un ordinateur et leur r\u00f4le.\n2. Les diff\u00e9rents types de logiciels et leurs utilisations.\n3. Les principaux syst\u00e8mes d'exploitation utilis\u00e9s sur les ordinateurs.\n4. Les bases de la s\u00e9curit\u00e9 informatique et les bonnes pratiques pour se prot\u00e9ger contre les cyberattaques.\n5. Les diff\u00e9rentes \u00e9tapes de la cr\u00e9ation d'un site web basique.\n6. La distinction entre un r\u00e9seau local et un r\u00e9seau \u00e9tendu.\n7. Les principes de base de la programmation et les langages de programmation populaires.\n8. L'importance de la sauvegarde des donn\u00e9es et les diff\u00e9rents moyens de sauvegarde disponibles.\n9. Les fonctions principales d'un navigateur web et la navigation sur Internet.\n10. L'impact des nouvelles technologies sur le monde du travail et de l'\u00e9ducation.\n\nCes th\u00e8mes peuvent servir de base pour l'enseignement de l'informatique aux \u00e9l\u00e8ves de 6\u00e8me humanit\u00e9s commerciale en RDC dans les ann\u00e9es 2020, 2021, 2022 et 2023.","behavior":"degenerate_repetition","conversation_hash":"8cfee39339dffa8e507b5af727d8da52","conversation_redacted":false,"conversation_toxic":false,"country":"DR Congo","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"French","model":"gpt-3.5-turbo-0125","n_chars":1184,"n_turns_in_conversation":3,"n_words":182,"source_row_offset":82414,"source_shard":"turns-00019.parquet","timestamp":"Fri, 12 Apr 2024 09:13:20 GMT","turn_id":"7b536d4bd063e2c1aed46236","turn_index":2,"user_text":"items informatique de la rdc pour les eleves de 6 iem humanites commerciale de2020,2021, 2022,2023,"},{"assistant_initiates":false,"asst_text":"/imagine prompt: Realistic style depiction of a futuristic Chengdu cityscape in 2024, blending advanced technology with traditional Chinese culture, bustling streets filled with neon lights and flying vehicles, towering skyscrapers juxtaposed with ancient temples, vibrant night market stalls selling a fusion of street food, surrounded by a misty mountain backdrop, captured with a Canon EOS R5 camera, 24-70mm lens, medium depth of field, composition highlighting the contrast between old and new, in a cyberpunk art style. --ar 16:9 --v 5\n\n/imagine prompt: Streets of Chengdu in 2024 transformed into a colorful fusion of modern and traditional elements, traditional Sichuan architecture interspersed with sleek glass structures, people in futuristic attire mingling with locals in traditional clothing, street vendors selling high-tech gadgets alongside classic street foods, a harmonious blend of the past and future, captured in a vibrant and lively atmosphere, rendered in a digital painting style. --ar 16:9 --v 5\n\n/imagine prompt: A realistic portrayal of a future Chengdu scene in 2024, with a mix of modern technology and traditional Chinese elements, teahouses with holographic tea ceremonies, streets lined with ancient trees and automated vehicles, a serene blend of old and new in a tranquil setting, captured with a Sony A7III camera, 35mm lens, wide depth of field, composition focusing on the harmony of elements, in a high-definition photography style. --ar 16:9\n\n/imagine prompt: A vision of Chengdu city streets in 2024, a seamless integration of futuristic tech and ancient culture, bustling markets brimming with augmented reality displays and traditional crafts, a unique blend of innovation and heritage, captured in a vibrant and energetic atmosphere, depicted in a modern illustration style with bold colors and dynamic compositions. --ar 16:9 --v 5","behavior":"degenerate_repetition","conversation_hash":"168ad92b2f98a4b4ae732a67c2aedcba","conversation_redacted":false,"conversation_toxic":false,"country":"China","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":5697,"n_turns_in_conversation":1,"n_words":730,"source_row_offset":82415,"source_shard":"turns-00019.parquet","timestamp":"Fri, 12 Apr 2024 09:15:06 GMT","turn_id":"b26921540086a9e520c6c825","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] = \u5199\u5b9e\u98ce\u683c\u76842024\u5e74\u4e2d\u56fd\u6210\u90fd\u5e02\u8857\u5934\uff0c\u672a\u6765\u79d1\u6280\u4e0e\u4f20\u7edf\u6587\u5316\u878d\u5408\u7684\u666f\u89c2\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                                                        "}],"shard":{"index":19,"name":"turns-00019.parquet","rows":86991}}