{"behavior":"degenerate_repetition","label_filter":null,"limit":10,"next_offset":76601,"offset":76591,"previous_offset":null,"rows":[{"assistant_initiates":false,"asst_text":"\u4ee3\u7801\u2461\u4e2d\u7684`feature_consistency_loss`\u8ba1\u7b97\u65b9\u6cd5\u57fa\u672c\u662f\u6b63\u786e\u7684\uff0c\u4f46\u662f\u5426\u5b8c\u5168\u9002\u7528\u53d6\u51b3\u4e8e\u4f60\u7684\u5177\u4f53\u9700\u6c42\u548c\u76ee\u6807\u3002\u7406\u8bba\u4f9d\u636e\u662f\u8ddd\u79bb\u5ea6\u91cf\uff08\u4f8b\u5982\uff0c\u5747\u65b9\u8bef\u5deeMSE\uff09\u901a\u5e38\u7528\u4e8e\u8bc4\u4f30\u4e24\u4e2a\u7279\u5f81\u4e4b\u95f4\u7684\u76f8\u4f3c\u6027\u6216\u4e00\u81f4\u6027\u3002\u8fd9\u91cc`feature_consistency_loss`\u662f\u7528\u6765\u8861\u91cf\u7ecf\u8fc7\u7279\u5f81\u589e\u5f3a\uff08`counter_guide`\u6a21\u5757\u5904\u7406\u540e\uff09\u7684\u7279\u5f81(`A_enhanced_x`, `A_enhanced_event_x`)\u4e0e\u539f\u59cb\u7279\u5f81(`A_original_x`, `A_original_event_x`)\u4e4b\u95f4\u7684\u76f8\u4f3c\u6027\u3002\u901a\u8fc7\u6700\u5c0f\u5316\u8fd9\u4e2a\u635f\u5931\uff0c\u6a21\u578b\u88ab\u6fc0\u52b1\u5728\u589e\u5f3a\u7279\u5f81\u7684\u540c\u65f6\uff0c\u4ecd\u4fdd\u7559\u539f\u59cb\u7279\u5f81\u4e2d\u91cd\u8981\u7684\u4fe1\u606f\u3002\n\n\u4ee3\u7801\u4e2d\u7684\u76f8\u5e94\u90e8\u5206\u662f\uff1a\n```python\nfeature_consistency_loss = F.mse_loss(pred_dict['A_enhanced_x'], pred_dict['A_original_x']) + F.mse_loss(pred_dict['A_enhanced_event_x'], pred_dict['A_original_event_x'])\n```\n\n\u8fd9\u6bb5\u8ba1\u7b97\u4e86RGB\u901a\u9053\u548c\u4e8b\u4ef6\u901a\u9053\u7ecf\u8fc7`counter_guide`\u589e\u5f3a\u524d\u540e\u7279\u5f81\u7684MSE\u635f\u5931\uff0c\u5e76\u5c06\u8fd9\u4e24\u4e2a\u635f\u5931\u76f8\u52a0\u3002\u8fd9\u79cd\u505a\u6cd5\u5e0c\u671b\u6a21\u578b\u5728\u589e\u5f3a\u7279\u5f81\u7684\u540c\u65f6\u4e0d\u7834\u574f\u539f\u7279\u5f81\u4e2d\u7684\u4fe1\u606f\u3002\n\n\u8bc4\u4ef7\u8fd9\u79cd\u8bbe\u8ba1\u6700\u91cd\u8981\u7684\u662f\u8003\u8651\u4ee5\u4e0b\u51e0\u70b9\uff1a\n\n1. **\u76ee\u7684\u660e\u786e**\uff1a\u8fd9\u79cd\u7279\u5f81\u4e00\u81f4\u6027\u635f\u5931\u662f\u5426\u80fd\u591f\u5e2e\u52a9\u4f60\u7684\u6a21\u578b\u66f4\u597d\u5730\u5b8c\u6210\u65e2\u5b9a\u4efb\u52a1\uff1f\u5728\u4e00\u4e9b\u4efb\u52a1\u4e2d\uff0c\u4fdd\u6301\u7279\u5f81\u7684\u4e00\u81f4\u6027\u662f\u5f88\u91cd\u8981\u7684\uff0c\u4f46\u5728\u5176\u4ed6\u4efb\u52a1\u4e2d\uff0c\u53ef\u80fd\u66f4\u9700\u8981\u7684\u662f\u63d0\u53d6\u66f4\u5177\u6709\u533a\u5206\u6027\u7684\u7279\u5f81\u3002\n\n2. **\u6743\u91cd\u9009\u62e9**\uff1a\u6700\u540e\uff0c`feature_consistency_loss`\u52a0\u5230\u603b\u635f\u5931\u4e2d\u7684\u6743\u91cd\u662f`0.5`\u3002\u8fd9\u4e2a\u6743\u91cd\u662f\u5426\u5408\u9002\uff0c\u9700\u8981\u901a\u8fc7\u5b9e\u9a8c\u8bc1\u660e\u3002\u6a21\u578b\u7684\u8bad\u7ec3\u901a\u5e38\u5bf9\u635f\u5931\u51fd\u6570\u4e2d\u5404\u9879\u7684\u6743\u91cd\u6bd4\u8f83\u654f\u611f\uff0c\u8fd9\u4e2a\u6743\u91cd\u53ef\u80fd\u9700\u8981\u901a\u8fc7\u5b9e\u9a8c\u6765\u8c03\u6574\uff0c\u4ee5\u4fbf\u627e\u5230\u6700\u4f73\u7684\u8bbe\u7f6e\u3002\n\n3. **\u7279\u6027\u4fdd\u7559**\uff1a\u867d\u7136\u8fd9\u79cd\u505a\u6cd5\u53ef\u4ee5\u5e2e\u52a9\u6a21\u578b\u5728\u589e\u5f3a\u7279\u5f81\u7684\u540c\u65f6\u4fdd\u7559\u539f\u6709\u7684\u7279\u5f81\u4fe1\u606f\uff0c\u4f46\u4e5f\u53ef\u80fd\u9650\u5236\u6a21\u578b\u63d0\u53d6\u4e0e\u4efb\u52a1\u5bc6\u5207\u76f8\u5173\u4f46\u5728\u539f\u59cb\u6570\u636e\u4e2d\u4e0d\u660e\u663e\u7684\u7279\u5f81\u3002\u662f\u5426\u4f7f\u7528\u8fd9\u79cd\u65b9\u6848\u9700\u8981\u6839\u636e\u5b9e\u9645\u60c5\u51b5\u548c\u6a21\u578b\u8868\u73b0\u6765\u5224\u65ad\u3002\n\n4. **\u5b9e\u9a8c\u9a8c\u8bc1**\uff1a\u52a1\u5fc5\u901a\u8fc7\u5b9e\u9a8c\u6765\u9a8c\u8bc1\u8fd9\u79cd\u8bbe\u8ba1\u7684\u6709\u6548\u6027\uff0c\u53ef\u4ee5\u901a\u8fc7\u6bd4\u8f83\u6dfb\u52a0`feature_consistency_loss`\u524d\u540e\u6a21\u578b\u7684\u6027\u80fd\u6765\u505a\u51fa\u5224\u65ad\u3002\n\n\u603b\u4f53\u6765\u8bf4\uff0c\u8fd9\u79cd\u8bbe\u8ba1\u5728\u6982\u5ff5\u4e0a\u662f\u5408\u7406\u7684\uff0c\u4f46\u5176\u6548\u679c\u548c\u9002\u7528\u6027\u9700\u8981\u901a\u8fc7\u5177\u4f53\u7684\u5b9e\u9a8c\u6765\u786e\u5b9a\uff0c\u5e76\u6839\u636e\u5b9e\u9a8c\u7ed3\u679c\u8fdb\u884c\u8c03\u6574\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u8fd8\u9700\u8981\u8003\u8651\u4e0d\u540c\u635f\u5931\u9879\u4e4b\u95f4\u7684\u5e73\u8861\uff0c\u786e\u4fdd\u6a21\u578b\u80fd\u591f\u517c\u987e\u589e\u5f3a\u7279\u5f81\u7684\u8868\u8fbe\u529b\u548c\u5bf9\u539f\u59cb\u7279\u5f81\u4fe1\u606f\u7684\u4fdd\u7559\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":16878,"n_turns_in_conversation":16,"n_words":1199,"source_row_offset":76591,"source_shard":"turns-00020.parquet","timestamp":"Sun, 28 Apr 2024 12:51:46 GMT","turn_id":"28e7e5e894ec4d03de195c4f","turn_index":1,"user_text":"\u5f53\u524d\u4ee3\u7801\u2460 # \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\nfrom .new_counter_guide import Counter_Guide\n# from .ad_counter_guide import Counter_Guide_Enhanced\nfrom .ad_counter_guide_downdim import Counter_Guide_Enhanced\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    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        # \u6dfb\u52a0\u4ea4\u4e92\u6a21\u5757counter_guide\n        self.counter_guide = Counter_Guide_Enhanced(768, 768)\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        # \u5206\u652f1 \u5904\u7406\u6d41\u7a0b\n        B, H, W = x.shape[0], x.shape[2], x.shape[3]\n\n        x = self.patch_embed(x)\n        z = self.patch_embed(z)\n        z += self.pos_embed_z\n        x += self.pos_embed_x        \n\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        if self.add_sep_seg:\n            x += self.search_segment_pos_embed\n            z += self.template_segment_pos_embed\n        x = combine_tokens(z, x, mode=self.cat_mode)\n        if self.add_cls_token:\n            x = torch.cat([cls_tokens, x], dim=1)\n        x = self.pos_drop(x)\n\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        # # \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\n        event_x = combine_tokens(event_z, event_x, mode=self.cat_mode)\n\n        if self.add_cls_token:\n            event_x = torch.cat([cls_tokens, event_x], dim=1)\n\n        lens_z = self.pos_embed_z.shape[1]\n        lens_x = self.pos_embed_x.shape[1]\n\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\n        removed_indexes_s1 = []\n\n        for i, blk in enumerate(self.blocks):\n            # \u7b2c\u4e00\u4e2a\u5206\u652f\u5904\u7406\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            # \u7b2c\u4e8c\u4e2a\u5206\u652f\u5904\u7406\n            event_x, global_index_t1, global_index_s1, removed_index_s1, attn1 = \\\n                blk(event_x, global_index_t1, global_index_s1, mask_x, ce_template_mask, ce_keep_rate)\n\n\n            if self.ce_loc is not None and i in self.ce_loc:\n                removed_indexes_s.append(removed_index_s)\n                removed_indexes_s1.append(removed_index_s1)\n\n            # \u5728\u7b2c1\u5c42\u589e\u52a0counter_guide\u6a21\u5757,\u9a8c\u8bc1\u65e9\u671f\u878d\u5408\u6548\u679c\n            if i == 0 :\n                # \u65b0\u589e\u539f\u59cb\u7279\u5f81\uff08\u5728counter_guide\u4e4b\u524d\u7684\u7279\u5f81\uff0c\u4fbf\u4e8e\u540e\u7eed\u7684loss\u8ba1\u7b97\uff09\n                A_original_x = x.clone() # \u4fdd\u5b58\u539f\u59cbx\u7279\u5f81 \uff08RGB\uff09\n                A_original_event_x = event_x.clone() # \u4fdd\u5b58\u539f\u59cbevent\u7279\u5f81 \uff08event\uff09\n\n                # \u5f15\u5165counter_guide\u8fdb\u884c\u7279\u5f81\u589e\u5f3a\n                enhanced_x, enhanced_event_x = self.counter_guide(x, event_x)\n\n                # \u4fdd\u5b58\u7ecf\u8fc7counter_guide\u589e\u5f3a\u7684\u7279\u5f81\n                A_enhanced_x = enhanced_x  # \u4fdd\u5b58\u589e\u5f3a\u540e\u7684\u7279\u5f81\n                A_enhanced_event_x = enhanced_event_x  # \u4fdd\u5b58\u589e\u5f3a\u540e\u7684\u7279\u5f81\n                # \u5c06\u589e\u5f3a\u540e\u7684\u7279\u5f81\u4e0e\u539f\u7279\u5f81\u76f8\u52a0\n                x = x + enhanced_x\n                event_x = event_x + enhanced_event_x\n        # \u5e94\u7528LayerNorm\u5f52\u4e00\u5316\u5904\u7406\n        x = self.norm(x) \n        event_x = self.norm(event_x)\n\n        x_cat = torch.cat([event_x,x], dim=1)\n        x = x_cat\n        aux_dict = {\n            \"attn\": attn,\n            'attn1': attn1,\n            \"removed_indexes_s\": removed_indexes_s,  # used for visualization\n            'removed_indexes_s1': removed_indexes_s1,\n            # \u5728aux_dict\u4e2d\u8fd4\u56de\u539f\u59cb\u7279\u5f81\u548c\u589e\u5f3a\u7279\u5f81\n            'A_original_x': A_original_x,\n            'A_original_event_x': A_original_event_x,\n            'A_enhanced_x': A_enhanced_x,\n            'A_enhanced_event_x': A_enhanced_event_x,\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\u548c\u4ee3\u7801\u2461 from . 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#\nimport torch.nn.functional as F\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\n        #######\n        # \u8ba1\u7b97\u7279\u5f81\u4e00\u81f4\u6027\u635f\u5931\n        feature_consistency_loss = F.mse_loss(pred_dict['A_enhanced_x'], pred_dict['A_original_x']) + F.mse_loss(pred_dict['A_enhanced_event_x'], pred_dict['A_original_event_x'])\n\n        # \u6807\u91cf\u5316\u7279\u5f81\u4e00\u81f4\u6027\u635f\u5931\n        feature_consistency_loss = feature_consistency_loss.mean()\n        \n        # weighted sum\n        loss = self.loss_weight['giou'] * giou_loss + self.loss_weight['l1'] * l1_loss + self.loss_weight['focal'] * location_loss + 0.5 * feature_consistency_loss\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/feature_consistency': feature_consistency_loss.item(),\n                      \"IoU\": mean_iou.item()}\n            return loss, status\n        else:\n            return loss\n\u5176\u4e2d\u7684feature_consistency_loss\u8bbe\u8ba1\u6b63\u786e\u5417\uff1f"},{"assistant_initiates":false,"asst_text":"\u5728\u60a8\u4ee3\u7801\u4e2d\u5c06`feature_consistency_loss`\u7684\u6743\u91cd\u8bbe\u7f6e\u4e3a0.5\uff0c\u5bf9\u6a21\u578b\u8bad\u7ec3\u548c\u6027\u80fd\u4f1a\u6709\u4e00\u5b9a\u7684\u5f71\u54cd\uff0c\u5177\u4f53\u5982\u4e0b\uff1a\n\n1. **\u635f\u5931\u6743\u91cd\u7684\u5f71\u54cd**\uff1a\u5728\u635f\u5931\u51fd\u6570\u4e2d\uff0c\u5404\u90e8\u5206\u635f\u5931\u7684\u6743\u91cd\u51b3\u5b9a\u4e86\u6a21\u578b\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u5bf9\u8fd9\u4e9b\u635f\u5931\u7684\u91cd\u89c6\u7a0b\u5ea6\u3002\u5c06`feature_consistency_loss`\u7684\u6743\u91cd\u8bbe\u7f6e\u4e3a0.5\u76f8\u5bf9\u4e8e\u5176\u4ed6\u635f\u5931\uff08\u5982`giou_loss`, `l1_loss`, `location_loss`\uff09\u610f\u5473\u7740\u60a8\u8ba4\u4e3a\u7279\u5f81\u4e00\u81f4\u6027\u5728\u603b\u635f\u5931\u4e2d\u5360\u636e\u8f83\u91cd\u8981\u7684\u4f4d\u7f6e\u3002\u8fd9\u4f1a\u5f71\u54cd\u6a21\u578b\u4f18\u5316\u7684\u65b9\u5411\uff0c\u4f7f\u6a21\u578b\u5728\u51cf\u5c11\u4e00\u81f4\u6027\u635f\u5931\uff08\u5373\u4fdd\u6301\u7279\u5f81\u589e\u5f3a\u524d\u540e\u7684\u4e00\u81f4\u6027\uff09\u65b9\u9762\u6295\u5165\u66f4\u591a\u7684\u7cbe\u529b\u3002\n\n2. **\u5e73\u8861\u5404\u9879\u635f\u5931**\uff1a\u5982\u679c0.5\u8fd9\u4e2a\u6743\u91cd\u80fd\u591f\u5f88\u597d\u5730\u5e73\u8861\u7279\u5f81\u4e00\u81f4\u6027\u4e0e\u5176\u4ed6\u4efb\u52a1\u76f8\u5173\u635f\u5931\u4e4b\u95f4\u7684\u5173\u7cfb\uff0c\u5219\u53ef\u4ee5\u4fc3\u8fdb\u6a21\u578b\u540c\u65f6\u4f18\u5316\u6240\u6709\u8003\u8651\u7684\u6307\u6807\u3002\u7136\u800c\uff0c\u5982\u679c\u8fd9\u4e2a\u6743\u91cd\u8bbe\u7f6e\u8fc7\u9ad8\u6216\u8fc7\u4f4e\uff0c\u90fd\u53ef\u80fd\u5bfc\u81f4\u6a21\u578b\u8fc7\u5206\u5173\u6ce8\u7279\u5f81\u4e00\u81f4\u6027\u800c\u5ffd\u7565\u5176\u4ed6\u91cd\u8981\u7684\u76ee\u6807\uff0c\u6216\u8005\u53cd\u4e4b\uff0c\u5f71\u54cd\u6a21\u578b\u6574\u4f53\u7684\u8868\u73b0\u3002\n\n3. **\u6a21\u578b\u6cdb\u5316\u80fd\u529b**\uff1a\u7406\u60f3\u60c5\u51b5\u4e0b\uff0c\u7279\u5f81\u4e00\u81f4\u6027\u7684\u4f18\u5316\u6709\u52a9\u4e8e\u63d0\u5347\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\uff0c\u56e0\u4e3a\u8fd9\u9f13\u52b1\u4e86\u6a21\u578b\u63d0\u53d6\u66f4\u672c\u8d28\u548c\u7a33\u5b9a\u7684\u7279\u5f81\u8868\u793a\u3002\u4f46\u662f\uff0c\u6743\u91cd\u7684\u9009\u62e9\u9700\u8981\u8c28\u614e\uff0c\u8fc7\u5206\u5f3a\u8c03\u8fd9\u4e00\u9879\u53ef\u80fd\u5bfc\u81f4\u6a21\u578b\u5728\u8bad\u7ec3\u96c6\u4e0a\u8868\u73b0\u826f\u597d\uff0c\u4f46\u5728\u9a8c\u8bc1\u96c6\u548c\u6d4b\u8bd5\u96c6\u4e0a\u6027\u80fd\u4e0b\u964d\uff0c\u5373\u8fc7\u62df\u5408\u7684\u98ce\u9669\u3002\n\n\u603b\u7ed3\u6765\u8bf4\uff0c\u8bbe\u7f6e\u6743\u91cd\u4e3a0.5\u5bf9\u6a21\u578b\u8bad\u7ec3\u548c\u6700\u7ec8\u6027\u80fd\u7684\u5f71\u54cd\u53d6\u51b3\u4e8e\u591a\u79cd\u56e0\u7d20\uff0c\u5305\u62ec\u4efb\u52a1\u7684\u5177\u4f53\u9700\u6c42\u3001\u6570\u636e\u7684\u7279\u6027\u3001\u4ee5\u53ca\u635f\u5931\u51fd\u6570\u4e2d\u5176\u4ed6\u9879\u7684\u6743\u91cd\u8bbe\u7f6e\u3002\u9009\u62e9\u5408\u9002\u7684\u6743\u91cd\u901a\u5e38\u9700\u8981\u57fa\u4e8e\u5bf9\u95ee\u9898\u7684\u7406\u89e3\uff0c\u4ee5\u53ca\u901a\u8fc7\u5927\u91cf\u5b9e\u9a8c\u6765\u4e0d\u65ad\u5c1d\u8bd5\u548c\u9a8c\u8bc1\u3002\u4e00\u4e2a\u597d\u7684\u5f00\u59cb\u662f\u6839\u636e\u7ecf\u9a8c\u548c\u73b0\u6709\u6587\u732e\u4e2d\u7684\u7c7b\u4f3c\u4efb\u52a1\u6765\u8bbe\u7f6e\u4e00\u4e2a\u521d\u59cb\u6743\u91cd\uff0c\u7136\u540e\u6839\u636e\u6a21\u578b\u5728\u9a8c\u8bc1\u96c6\u4e0a\u7684\u8868\u73b0\u6765\u8c03\u6574\u8fd9\u4e2a\u6743\u91cd\uff0c\u627e\u5230\u6700\u4f18\u7684\u5e73\u8861\u70b9\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":711,"n_turns_in_conversation":16,"n_words":11,"source_row_offset":76592,"source_shard":"turns-00020.parquet","timestamp":"Sun, 28 Apr 2024 12:51:46 GMT","turn_id":"bb6f20cfa8802cc26bef276e","turn_index":2,"user_text":"\u90a3\u4e48\u6b64\u5904\u8bbe\u7f6e0.5\u6709\u4f55\u5f71\u54cd\uff1f"},{"assistant_initiates":false,"asst_text":"\u5728\u914d\u7f6e\u6587\u4ef6\u4e2d\u9488\u5bf9\u635f\u5931\u6743\u91cd\u7684\u8bbe\u7f6e\u8868\u660e\u4e86\u6bcf\u79cd\u635f\u5931\u7c7b\u578b\u6240\u5360\u7684\u76f8\u5bf9\u91cd\u8981\u6027\u3002\u4ece\u914d\u7f6e\u4e2d\u53ef\u4ee5\u770b\u51fa\uff0c\u5bf9\u4e8e\u4e0d\u540c\u79cd\u7c7b\u7684\u635f\u5931\uff0c\u5982GIOU\u635f\u5931\u3001L1\u635f\u5931\u548cFocal\u635f\u5931\uff0c\u5206\u522b\u8d4b\u4e88\u4e86\u4e0d\u540c\u7684\u6743\u91cd\uff1a\n\n```yaml\nTRAIN:\n  GIOU_WEIGHT: 1     # GIoU\u635f\u5931\u7684\u6743\u91cd\n  L1_WEIGHT: 14      # L1\u635f\u5931\u7684\u6743\u91cd\n  FOCAL_WEIGHT: 1.0  # Focal\u635f\u5931\u7684\u6743\u91cd\n```\n\n\u8fd9\u91cc\u7684\u6743\u91cd\u8bbe\u7f6e\u662f\u6309\u7167\u4efb\u52a1\u7684\u5177\u4f53\u9700\u6c42\u548c\u5148\u9a8c\u7ecf\u9a8c\u6765\u5b9a\u7684\u3002\u4f8b\u5982\uff0c\u5982\u679c\u4f60\u53d1\u73b0\u4f4d\u7f6e\u56de\u5f52\u5bf9\u4e8e\u6a21\u578b\u6027\u80fd\u81f3\u5173\u91cd\u8981\uff0c\u4f60\u53ef\u80fd\u4f1a\u589e\u52a0`L1_WEIGHT`\u6765\u5f3a\u8c03L1\u635f\u5931\u7684\u91cd\u8981\u6027\u3002\u540c\u6837\uff0c\u5982\u679c\u6a21\u578b\u5728\u9ad8\u5ea6\u4e0d\u5e73\u8861\u7684\u5206\u7c7b\u4efb\u52a1\u4e2d\u8868\u73b0\u4e0d\u4f73\uff0c\u53ef\u80fd\u9700\u8981\u8c03\u6574`FOCAL_WEIGHT`\u6765\u63d0\u9ad8\u6a21\u578b\u5bf9\u5c0f\u7c7b\u522b\u7684\u8bc6\u522b\u80fd\u529b\u3002\n\n\u4ee5\u4e0b\u662f\u8fd9\u4e9b\u8bbe\u7f6e\u53ef\u80fd\u5bf9\u6a21\u578b\u8bad\u7ec3\u548c\u6027\u80fd\u4ea7\u751f\u7684\u5f71\u54cd\uff1a\n\n1. **GIOU_WEIGHT**: GIoU\uff08Generalized Intersection over Union\uff09\u635f\u5931\u7528\u4e8e\u4f18\u5316\u8fb9\u754c\u6846\u9884\u6d4b\u3002\u5b83\u8003\u8651\u4e86\u8fb9\u754c\u6846\u4e0e\u771f\u5b9e\u6846\u4e4b\u95f4\u7684\u91cd\u53e0\u60c5\u51b5\u548c\u5f62\u72b6\u5dee\u5f02\uff0c\u63d0\u4f9b\u4e86\u6bd4\u4f20\u7edfIoU\u635f\u5931\u66f4\u5168\u9762\u7684\u53cd\u9988\u3002\u6743\u91cd\u4e3a1\u610f\u5473\u7740\u4e0e\u5176\u4ed6\u635f\u5931\u76f8\u6bd4\uff0c\u5b83\u5177\u6709\u76f8\u7b49\u7684\u91cd\u8981\u6027\u3002\n\n2. **L1_WEIGHT**: L1\u635f\u5931\uff08\u4e5f\u79f0\u4e3a\u7edd\u5bf9\u8bef\u5dee\u635f\u5931\uff09\u5bf9\u4e8e\u8fb9\u754c\u6846\u7684\u5750\u6807\u56de\u5f52\u975e\u5e38\u6709\u6548\u3002`L1_WEIGHT`\u8bbe\u7f6e\u4e3a14\uff0c\u8fdc\u9ad8\u4e8eGIOU\u548cFocal\u635f\u5931\uff0c\u8868\u660e\u4f4d\u7f6e\u51c6\u786e\u6027\u5bf9\u4e8e\u6a21\u578b\u6027\u80fd\u81f3\u5173\u91cd\u8981\u3002\n\n3. **FOCAL_WEIGHT**: Focal\u635f\u5931\u4e3b\u8981\u7528\u4e8e\u89e3\u51b3\u5206\u7c7b\u95ee\u9898\u4e2d\u7684\u7c7b\u522b\u4e0d\u5e73\u8861\u95ee\u9898\uff0c\u901a\u8fc7\u7ed9\u4e88\u96be\u4ee5\u5206\u7c7b\u6837\u672c\u66f4\u9ad8\u7684\u6743\u91cd\uff0c\u6765\u589e\u5f3a\u6a21\u578b\u7684\u8bc6\u522b\u80fd\u529b\u3002\u6743\u91cd\u8bbe\u7f6e\u4e3a1.0\uff0c\u8868\u660e\u5b83\u4e0eGIOU\u635f\u5931\u76f8\u7b49\u91cd\u8981\uff0c\u4f46\u8fdc\u4e0d\u5982L1\u635f\u5931\u91cd\u8981\u3002\n\n\u6743\u91cd\u7684\u8c03\u6574\u9700\u8981\u6839\u636e\u5b9e\u9645\u60c5\u51b5\u6765\u505a\u51b3\u5b9a\uff0c\u5982\u6a21\u578b\u5728\u9a8c\u8bc1\u96c6\u4e0a\u7684\u8868\u73b0\u6216\u7279\u5b9a\u4efb\u52a1\u7684\u9700\u6c42\u3002\u6ce8\u610f\uff0c\u6743\u91cd\u914d\u7f6e\u5e76\u6ca1\u6709\u76f4\u63a5\u5305\u542b\u4e4b\u524d\u63d0\u5230\u7684`feature_consistency_loss`\u7684\u6743\u91cd\uff0c\u8fd9\u53ef\u80fd\u662f\u56e0\u4e3a\u8be5\u6743\u91cd\u662f\u5728\u6a21\u578b\u6216\u8bad\u7ec3\u811a\u672c\u5185\u90e8\u9759\u6001\u8bbe\u7f6e\u7684\u3002\u5982\u679c\u4f60\u9700\u8981\u8c03\u6574\u5b83\uff0c\u53ef\u80fd\u9700\u8981\u5728\u6a21\u578b\u5b9a\u4e49\u6216\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u4fee\u6539\u76f8\u5173\u4ee3\u7801\u3002\n\n\u6b64\u5916\uff0c\u8fd9\u4e9b\u6743\u91cd\u7684\u8bbe\u7f6e\u5e94\u8be5\u57fa\u4e8e\u5145\u5206\u7684\u5b9e\u9a8c\u6765\u8fdb\u884c\u5fae\u8c03\u3002\u5b9e\u9a8c\u7ed3\u679c\u7684\u53cd\u9988\u6709\u52a9\u4e8e\u66f4\u597d\u5730\u7406\u89e3\u6bcf\u79cd\u7c7b\u578b\u7684\u635f\u5931\u5bf9\u6574\u4f53\u6027\u80fd\u7684\u5f71\u54cd\uff0c\u4ece\u800c\u627e\u5230\u6700\u4f73\u7684\u6743\u91cd\u7ec4\u5408\uff0c\u4ee5\u63d0\u5347\u6a21\u578b\u6027\u80fd\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":2428,"n_turns_in_conversation":16,"n_words":201,"source_row_offset":76593,"source_shard":"turns-00020.parquet","timestamp":"Sun, 28 Apr 2024 12:51:46 GMT","turn_id":"8a7308c9ec6ed346e0e4dc08","turn_index":3,"user_text":"\u5728\u914d\u7f6e\u6587\u4ef6\u4e2d\u635f\u5931\u6743\u91cd\u8fdb\u884c\u4e86\u8bbe\u7f6e\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: 32\n  EPOCH: 60\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: 35\n  SEARCH_FACTOR: 4.0\n  SEARCH_SIZE: 256\n  TEMPLATE_FACTOR: 2.0\n  TEMPLATE_SIZE: 128"},{"assistant_initiates":false,"asst_text":"\u8003\u8651\u5230`feature_consistency_loss`\u662f\u7528\u6765\u7ef4\u6301\u6216\u589e\u5f3a\u6a21\u578b\u7279\u5f81\u4e00\u81f4\u6027\u7684\u635f\u5931\uff0c\u9009\u62e9\u4e00\u4e2a\u5408\u9002\u7684\u6743\u91cd\u975e\u5e38\u91cd\u8981\uff0c\u65e2\u4e0d\u80fd\u592a\u5927\u4ee5\u81f3\u4e8e\u5f71\u54cd\u6a21\u578b\u5b66\u4e60\u5176\u4ed6\u91cd\u8981\u7279\u5f81\uff0c\u4e5f\u4e0d\u80fd\u592a\u5c0f\u4ee5\u81f3\u4e8e\u5176\u6548\u679c\u5fae\u4e4e\u5176\u5fae\u3002\u6545\u6743\u91cd\u7684\u8bbe\u7f6e\u4e3b\u8981\u53d6\u51b3\u4e8e\u4ee5\u4e0b\u56e0\u7d20\uff1a\n\n### 1. \u4efb\u52a1\u7684\u91cd\u8981\u6027\n\u5982\u679c\u4f60\u7684\u4efb\u52a1\u7279\u522b\u4f9d\u8d56\u4e8e\u4fdd\u6301\u589e\u5f3a\u524d\u540e\u7684\u7279\u5f81\u4e00\u81f4\u6027\uff0c\u90a3\u4e48\u8fd9\u4e2a\u6743\u91cd\u503c\u5e94\u8be5\u8bbe\u7f6e\u5f97\u76f8\u5bf9\u8f83\u9ad8\u3002\u7136\u800c\uff0c\u5982\u679c\u8fd9\u4e2a\u76ee\u6807\u76f8\u5bf9\u6b21\u8981\uff0c\u6743\u91cd\u5e94\u8be5\u8f83\u4f4e\u3002\n\n### 2. \u5176\u4ed6\u635f\u5931\u9879\u7684\u6743\u91cd\n\u6839\u636e\u914d\u7f6e\u6587\u4ef6\uff0c`GIOU_WEIGHT`\u4e3a1\uff0c`L1_WEIGHT`\u4e3a14\uff0c`FOCAL_WEIGHT`\u4e3a1\u3002\u8fd9\u610f\u5473\u7740L1\u635f\u5931\u88ab\u8d4b\u4e88\u6700\u9ad8\u7684\u91cd\u89c6\u3002\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c`feature_consistency_loss`\u7684\u6743\u91cd\u5e94\u8be5\u660e\u663e\u4f4e\u4e8e14\uff0c\u4ee5\u907f\u514d\u4e0eL1\u635f\u5931\u7ade\u4e89\u8fc7\u591a\u7684\u4f18\u5316\u8d44\u6e90\u3002\n\n### 3. \u5b9e\u9a8c\u53cd\u9988\n\u6700\u7ec8\uff0c\u6700\u5408\u9002\u7684\u6743\u91cd\u8bbe\u7f6e\u8fd8\u662f\u8981\u901a\u8fc7\u5b9e\u9a8c\u53cd\u9988\u6765\u786e\u5b9a\u3002\u7406\u60f3\u7684\u5b9e\u9a8c\u8fc7\u7a0b\u662f\u5148\u4ece\u4e00\u4e2a\u76f8\u5bf9\u4f4e\u7684\u6743\u91cd\u5f00\u59cb\uff0c\u59820.1\u62160.5\uff0c\u5728\u51e0\u4e2a\u8bad\u7ec3\u5468\u671f\u540e\uff0c\u89c2\u5bdf\u6a21\u578b\u5728\u9a8c\u8bc1\u96c6\u4e0a\u7684\u8868\u73b0\u3002\u5982\u679c\u53d1\u73b0\u589e\u52a0`feature_consistency_loss`\u7684\u6743\u91cd\u5bf9\u6a21\u578b\u6027\u80fd\u6709\u660e\u663e\u63d0\u5347\uff0c\u53ef\u4ee5\u8fdb\u4e00\u6b65\u589e\u52a0\u6743\u91cd\uff1b\u82e5\u6027\u80fd\u4e0b\u964d\u6216\u53d8\u5316\u4e0d\u5927\uff0c\u53ef\u80fd\u5c31\u9700\u8981\u51cf\u5c11\u6743\u91cd\u3002\n\n### \u5efa\u8bae\u7684\u521d\u59cb\u8bbe\u7f6e\n\u7efc\u4e0a\u6240\u8ff0\uff0c\u4e00\u4e2a\u5408\u7406\u7684\u8d77\u70b9\u53ef\u80fd\u662f\u5c06`feature_consistency_loss`\u7684\u6743\u91cd\u8bbe\u7f6e\u57280.5\u52302\u4e4b\u95f4\uff0c\u8fd9\u662f\u4e00\u4e2a\u53ef\u80fd\u65e2\u4e0d\u4f1a\u8fc7\u5206\u5f71\u54cd\u6a21\u578b\u5bf9\u5176\u4ed6\u4efb\u52a1\u7684\u5b66\u4e60\uff0c\u53c8\u80fd\u786e\u4fdd\u7279\u5f81\u4e00\u81f4\u6027\u635f\u5931\u6709\u8db3\u591f\u7684\u5f71\u54cd\u529b\u7684\u8303\u56f4\u3002\u7136\u540e\u6839\u636e\u4f60\u7684\u5b9e\u9a8c\u7ed3\u679c\uff0c\u9010\u6b65\u8c03\u6574\u8fd9\u4e2a\u503c\u3002\n\n\u8bb0\u4f4f\uff0c\u8fd9\u4e2a\u6743\u91cd\u7684\u6700\u4f73\u503c\u9ad8\u5ea6\u4f9d\u8d56\u4e8e\u5177\u4f53\u4efb\u52a1\u3001\u6570\u636e\u4ee5\u53ca\u6a21\u578b\u7ed3\u6784\uff0c\u53ef\u80fd\u9700\u8981\u591a\u6b21\u5b9e\u9a8c\u6765\u627e\u5230\u6700\u4f18\u8bbe\u7f6e\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":746,"n_turns_in_conversation":16,"n_words":18,"source_row_offset":76594,"source_shard":"turns-00020.parquet","timestamp":"Sun, 28 Apr 2024 12:51:46 GMT","turn_id":"53dda1d32ba7132a33c867de","turn_index":4,"user_text":"\u90a3\u4e48\u6839\u636e\u4e0a\u6587\u4e2d\u4e0d\u540closs\u7684\u8bbe\u8ba1\uff0c\u6211\u4eec\u7684feature_consistency\u8bbe\u7f6e\u4e3a\u591a\u5c11\u66f4\u4e3a\u5408\u9002\uff1f"},{"assistant_initiates":false,"asst_text":"\u5728\u60a8\u63d0\u4f9b\u7684\u4ee3\u7801\u4e2d\uff0c\u5b9a\u4e49\u4e86\u4e00\u4e2a\u540d\u4e3a`loss_rank`\u7684\u51fd\u6570\uff0c\u8be5\u51fd\u6570\u65e8\u5728\u8ba1\u7b97\u4e0e\u6ce8\u610f\u529b\u673a\u5236\uff08`attn`\uff09\u76f8\u5173\u7684\u4e00\u79cd\u6392\u540d\u635f\u5931\uff08rank loss\uff09\uff0c\u770b\u8d77\u6765\u76ee\u7684\u5728\u4e8e\u901a\u8fc7\u589e\u5f3a\u6a21\u578b\u6ce8\u610f\u529b\u7684\u96c6\u4e2d\u7a0b\u5ea6\u6765\u6539\u8fdb\u6a21\u578b\u6027\u80fd\u3002\u6839\u636e\u4ee3\u7801\u7247\u6bb5\u7684\u63cf\u8ff0\u548c\u7ed3\u6784\uff0c\u8fd9\u91cc\u6709\u4e00\u4e9b\u5173\u952e\u7684\u89c2\u5bdf\u548c\u5206\u6790\uff1a\n\n### \u5173\u4e8e`rank_loss`\u7684\u8bbe\u8ba1\n\n1. **\u8bbe\u8ba1\u5408\u7406\u6027**\uff1a\u6839\u636e`rank_loss`\u7684\u8ba1\u7b97\u65b9\u6cd5\uff0c\u76ee\u6807\u662f\u901a\u8fc7singular value decomposition\uff08SVD\uff09\u6765\u8bc4\u4f30\u6ce8\u610f\u529b\u77e9\u9635`attn`\u7684\u96c6\u4e2d\u7a0b\u5ea6\uff0c\u5e76\u901a\u8fc7\u4f18\u5316`rank_loss`\u6765\u95f4\u63a5\u5f15\u5bfc`attn`\u5206\u5e03\u7684\u96c6\u4e2d\u3002\u8fd9\u662f\u4e00\u4e2a\u5408\u7406\u7684\u8bbe\u8ba1\u601d\u8def\uff0c\u56e0\u4e3a\u5b83\u5c1d\u8bd5\u901a\u8fc7\u7279\u5b9a\u7684\u635f\u5931\u673a\u5236\u6765\u6539\u5584\u6ce8\u610f\u529b\u6a21\u578b\u7684\u6027\u80fd\u3002\n\n2. **\u5b9e\u73b0\u65b9\u5f0f**\uff1a\u60a8\u7684`loss_rank`\u51fd\u6570\u6267\u884c\u4e86SVD\u64cd\u4f5c\uff0c\u5e76\u8ba1\u7b97\u4e86singular values\uff08`s1`\uff09\u4e0e1\u7684\u5dee\u7684\u7edd\u5bf9\u503c\uff08`s01`\uff09\uff0c\u901a\u8fc7\u6700\u5c0f\u5316\u8fd9\u4e2a\u503c\u6765\u9f13\u52b1singular values\u8d8b\u8fd1\u4e8e1\uff0c\u4ece\u800c\u63d0\u9ad8\u6ce8\u610f\u529b\u7684\u96c6\u4e2d\u5ea6\u3002\u8fd9\u4e00\u64cd\u4f5c\u662f\u6709\u610f\u4e49\u7684\uff0c\u56e0\u4e3asingular values\u53cd\u6620\u4e86\u6570\u636e\u5728\u5404\u4e2a\u65b9\u5411\u4e0a\u7684\u6269\u5c55\u7a0b\u5ea6\uff0c\u5176\u503c\u63a5\u8fd11\u610f\u5473\u7740\u5728\u8fd9\u4e9b\u65b9\u5411\u4e0a\u4fe1\u606f\u7684\u5206\u5e03\u76f8\u5bf9\u5747\u5300\uff0c\u6709\u5229\u4e8e\u6ce8\u610f\u529b\u673a\u5236\u7684\u4f18\u5316\u3002\n\n### \u5173\u4e8e`attn`\u548c`attn1`\u7684\u53d6\u503c\n\n\u60a8\u7684\u4ee3\u7801\u4e2d\u63d0\u5230\u4e86`attn`\u548c\u4f7f\u7528\u4e86\u4e00\u4e2a\u540d\u4e3a`attn1`\u7684\u53d8\u91cf\uff1a\n- \u4ee3\u7801\u5c1d\u8bd5\u901a\u8fc7\u62fc\u63a5\u64cd\u4f5c\u91cd\u65b0\u7ec4\u5408`attn`\u4e2d\u7684\u90e8\u5206\u5f20\u91cf\u751f\u6210\u4e86`attn1`\u548c`attn2`\uff0c\u7136\u540e\u518d\u6b21\u62fc\u63a5\u5b83\u4eec\u751f\u6210\u65b0\u7684`attn`\u3002\u8fd9\u91cc\u770b\u8d77\u6765\u662f\u60f3\u5bf9`attn`\u7684\u90e8\u5206\u533a\u57df\u8fdb\u884c\u67d0\u79cd\u5f62\u5f0f\u7684\u5904\u7406\u6216\u91cd\u6392\u3002\u7136\u800c\uff0c\u8fd9\u6837\u7684\u5904\u7406\u662f\u5426\u9002\u5408\u53d6\u51b3\u4e8e\u5177\u4f53\u4efb\u52a1\u548c`attn`\u5f20\u91cf\u4ee3\u8868\u7684\u542b\u4e49\u3002\n- \u4ece\u4ee3\u7801\u903b\u8f91\u4e0a\u770b\uff0c`attn`\u548c`attn1`\u7684\u53d8\u6362\u4e0e\u62fc\u63a5\u4f3c\u4e4e\u662f\u4f01\u56fe\u5bf9\u6ce8\u610f\u529b\u77e9\u9635\u505a\u7279\u5b9a\u533a\u57df\u7684\u5904\u7406\u3002\u4f46\u662f\uff0c\u4ece\u7ed9\u51fa\u7684\u4ee3\u7801\u7247\u6bb5\u4e2d\u5f88\u96be\u5b8c\u5168\u7406\u89e3\u8fd9\u6837\u5904\u7406\u7684\u76ee\u7684\u548c\u671f\u671b\u7684\u6548\u679c\u3002\n\n### \u7efc\u5408\u5206\u6790\n\n- \u5982\u679c`attn`\u4ee3\u8868\u7684\u662f\u6a21\u578b\u8f93\u51fa\u7684\u6ce8\u610f\u529b\u77e9\u9635\uff0c\u90a3\u4e48\u9488\u5bf9`attn`\u548c\u901a\u8fc7\u5b83\u8ba1\u7b97\u7684`rank_loss`\u8fdb\u884c\u4f18\u5316\u662f\u4e00\u4e2a\u6709\u8da3\u4e14\u53ef\u80fd\u6709\u6548\u7684\u5c1d\u8bd5\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6539\u8fdb\u6a21\u578b\u5bf9\u7279\u5f81\u7684\u96c6\u4e2d\u548c\u6355\u6349\u80fd\u529b\u3002\n- \u4f46\u662f\uff0c`attn`\u548c`attn1`\u7684\u91cd\u65b0\u7ec4\u5408\u770b\u8d77\u6765\u6709\u4e9b\u590d\u6742\uff0c\u662f\u5426\u6709\u5fc5\u8981\u8fdb\u884c\u8fd9\u6837\u7684\u64cd\u4f5c\u9700\u8981\u6839\u636e\u6a21\u578b\u7684\u5177\u4f53\u7ed3\u6784\u548c\u4efb\u52a1\u9700\u6c42\u6765\u51b3\u5b9a\u3002\n- \u635f\u5931\u51fd\u6570\u7684\u8bbe\u8ba1\u548c\u5b9e\u73b0\u5e94\u8be5\u56f4\u7ed5\u6a21\u578b\u7684\u6574\u4f53\u67b6\u6784\u548c\u6700\u7ec8\u7684\u76ee\u6807\u8fdb\u884c\uff0c\u786e\u4fdd\u5b83\u80fd\u591f\u6709\u6548\u5730\u5f15\u5bfc\u6a21\u578b\u5b66\u4e60\u5230\u6709\u7528\u7684\u7279\u6027\u3002\n\n\u7efc\u4e0a\u6240\u8ff0\uff0c`rank_loss`\u7684\u8bbe\u8ba1\u662f\u4e00\u4e2a\u6709\u8da3\u7684\u5c1d\u8bd5\uff0c\u5173\u4e8e`attn`\u548c`attn1`\u53d6\u503c\u5e76\u8fdb\u884c\u64cd\u4f5c\u7684\u5408\u7406\u6027\uff0c\u9700\u8981\u6839\u636e\u5177\u4f53\u7684\u4efb\u52a1\u548c\u5b9e\u9a8c\u7ed3\u679c\u6765\u8fdb\u4e00\u6b65\u9a8c\u8bc1\u3002\u5982\u679c\u53ef\u80fd\uff0c\u6700\u597d\u80fd\u6709\u66f4\u8be6\u7ec6\u7684\u80cc\u666f\u4ecb\u7ecd\u548c\u6a21\u578b\u67b6\u6784\u63cf\u8ff0\uff0c\u8fd9\u6837\u53ef\u4ee5\u66f4\u7cbe\u786e\u5730\u8bc4\u4f30\u8fd9\u79cd\u8bbe\u8ba1\u7684\u5408\u7406\u6027\u548c\u6709\u6548\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":29225,"n_turns_in_conversation":16,"n_words":2324,"source_row_offset":76595,"source_shard":"turns-00020.parquet","timestamp":"Sun, 28 Apr 2024 12:51:46 GMT","turn_id":"7e9517f87f249c5eda287a82","turn_index":5,"user_text":"\u5f53\u524d\u7684\u4ee3\u7801\u2460\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\nfrom .ad_counter_guide import Counter_Guide_Enhanced\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    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        # \u6dfb\u52a0\u4ea4\u4e92\u6a21\u5757counter_guide\n        # self.counter_guide = Counter_Guide(768, 768)  \n        self.counter_guide = Counter_Guide_Enhanced(768, 768)\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        # \u5206\u652f1 \u5904\u7406\u6d41\u7a0b\n        B, H, W = x.shape[0], x.shape[2], x.shape[3]\n\n        x = self.patch_embed(x)\n        z = self.patch_embed(z)\n        z += self.pos_embed_z\n        x += self.pos_embed_x        \n\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        if self.add_sep_seg:\n            x += self.search_segment_pos_embed\n            z += self.template_segment_pos_embed\n        x = combine_tokens(z, x, mode=self.cat_mode)\n        if self.add_cls_token:\n            x = torch.cat([cls_tokens, x], dim=1)\n        x = self.pos_drop(x)\n\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        # # \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\n        event_x = combine_tokens(event_z, event_x, mode=self.cat_mode)\n\n        if self.add_cls_token:\n            event_x = torch.cat([cls_tokens, event_x], dim=1)\n\n        lens_z = self.pos_embed_z.shape[1]\n        lens_x = self.pos_embed_x.shape[1]\n\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\n        removed_indexes_s1 = []\n\n        for i, blk in enumerate(self.blocks):\n            # \u7b2c\u4e00\u4e2a\u5206\u652f\u5904\u7406\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            # \u7b2c\u4e8c\u4e2a\u5206\u652f\u5904\u7406\n            event_x, global_index_t1, global_index_s1, removed_index_s1, attn1 = \\\n                blk(event_x, global_index_t1, global_index_s1, mask_x, ce_template_mask, ce_keep_rate)\n\n\n            if self.ce_loc is not None and i in self.ce_loc:\n                removed_indexes_s.append(removed_index_s)\n                removed_indexes_s1.append(removed_index_s1)\n            # \u5728\u7b2c1\u5c42\u589e\u52a0counter_guide\u6a21\u5757,\u9a8c\u8bc1\u65e9\u671f\u878d\u5408\u6548\u679c\n            if i == 0:\n                enhanced_x, enhanced_event_x = self.counter_guide(x, event_x)\n                # \u5c06\u589e\u5f3a\u540e\u7684\u7279\u5f81\u4e0e\u539f\u7279\u5f81\u76f8\u52a0\n                x = x + enhanced_x\n                event_x = event_x + enhanced_event_x\n        # \u5e94\u7528LayerNorm\u5f52\u4e00\u5316\u5904\u7406\n        x = self.norm(x) \n        event_x = self.norm(event_x)\n\n        x_cat = torch.cat([event_x,x], dim=1)\n        x = x_cat\n        aux_dict = {\n            \"attn\": attn,\n            \"attn1\": attn1,\n            \"removed_indexes_s\": removed_indexes_s,  # used for visualization\n            \"removed_indexes_s1\": removed_indexes_s1,\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 \u5bf9\u5e94\u7684loss\u662f\u4ee3\u7801\u2461 \uff1a# from . import BaseActor\n# from lib.utils.misc import NestedTensor\n# from lib.utils.box_ops import box_cxcywh_to_xyxy, box_xywh_to_xyxy\n# import torch\n# from lib.utils.merge import merge_template_search\n# from ...utils.heapmap_utils import generate_heatmap\n# from ...utils.ce_utils import generate_mask_cond, adjust_keep_rate\n\n\n# class 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#         # \u65b0\u589e rank loss\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#         # loss = self.loss_weight['giou'] * giou_loss + self.loss_weight['l1'] * l1_loss + self.loss_weight['focal'] * location_loss\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\n# # \u65b0\u589e\u9ad8\u8d28\u6b63\u5219\u5316\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#         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#         attn = torch.cat([attn1, attn2],dim=1)\n#         B, C, H, W = attn.shape\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\nfrom . 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        # \u65b0\u589e rank loss \u533a\u5206 attn \u548c attn1\n        rank_loss_attn = self.loss_rank(pred_dict['attn'], gt_dict['search_anno'], gt_dict['template_anno'])\n        rank_loss_attn1 = self.loss_rank(pred_dict['attn1'], gt_dict['search_anno'], gt_dict['template_anno'])\n        rank_loss = (rank_loss_attn + rank_loss_attn1) / 2\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        # loss = self.loss_weight['giou'] * giou_loss + self.loss_weight['l1'] * l1_loss + self.loss_weight['focal'] * location_loss\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_loss.item(),\n                      \"IoU\": mean_iou.item()}\n            return loss, status\n        else:\n            return loss\n\n\n# \u65b0\u589e\u9ad8\u8d28\u6b63\u5219\u5316\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        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        attn = torch.cat([attn1, attn2],dim=1)\n        B, C, H, W = attn.shape\n        _,s1,_ = torch.svd(attn.reshape([B*C, H, W]))\n\n        s01 = torch.abs(s1 - 1)\n\n        return torch.mean(s01)\uff0c\u5176\u4e2d\u7684rank loss\u8bbe\u8ba1\u6b63\u786e\u5417\uff1f\u5176\u4e2d\u7684attn\u548cattn1\u53d6\u503c\u5bf9\u5417"},{"assistant_initiates":false,"asst_text":"\u5728\u4ee3\u7801\u7247\u6bb5\u4e2d\uff0c\u60a8\u5bf9\u539f\u6765\u7684`CEUTrackActor`\u7c7b\u8fdb\u884c\u4e86\u4e00\u4e9b\u4fee\u6539\uff0c\u5f15\u5165\u4e86\u9488\u5bf9\u4e8b\u4ef6\uff08event\uff09\u5206\u652f\u7684\u6ce8\u610f\u529b\u635f\u5931\uff08`loss_rank_event`\uff09\uff0c\u4ee5\u53ca\u6b63\u5219\u5316\u8ba1\u7b97\u3002\u8fd9\u4f3c\u4e4e\u662f\u5bf9\u6a21\u578b\u5728\u5904\u7406\u65f6\u95f4\u4fe1\u606f\u65f6\u7684\u6ce8\u610f\u529b\u673a\u5236\u8fdb\u884c\u6b63\u5219\u5316\uff0c\u4ee5\u6b64\u6765\u6539\u5584\u6a21\u578b\u5bf9\u65f6\u95f4\u548c\u7a7a\u95f4\u4fe1\u606f\u7ed3\u5408\u7684\u5904\u7406\u80fd\u529b\u3002\u8fd9\u91cc\u6709\u4e00\u4e9b\u5173\u952e\u70b9\uff1a\n\n1. **\u8bbe\u8ba1\u5408\u7406\u6027**\uff1a\u5f15\u5165\u9488\u5bf9\u4e8b\u4ef6\u5206\u652f\u7684\u7279\u5b9a\u6ce8\u610f\u529b\u635f\u5931\u662f\u4e00\u4e2a\u5408\u7406\u7684\u5c1d\u8bd5\uff0c\u5c24\u5176\u662f\u5728\u5904\u7406\u6d89\u53ca\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u6216\u8005\u9700\u8981\u5229\u7528\u65f6\u95f4\u4fe1\u606f\u8f85\u52a9\u7406\u89e3\u7a7a\u95f4\u5185\u5bb9\u7684\u4efb\u52a1\u4e2d\u3002\u901a\u8fc7\u5bf9\u4e8b\u4ef6\u5206\u652f\u7684\u6ce8\u610f\u529b\u52a0\u5f3a\u6b63\u5219\u5316\uff0c\u53ef\u4ee5\u9f13\u52b1\u6a21\u578b\u66f4\u597d\u5730\u5173\u6ce8\u4e8e\u4e0e\u4efb\u52a1\u76f8\u5173\u7684\u65f6\u95f4\u4fe1\u606f\uff0c\u4ece\u800c\u63d0\u5347\u6a21\u578b\u7684\u6574\u4f53\u6027\u80fd\u3002\n\n2. **\u5b9e\u73b0\u65b9\u6cd5**\uff1a\u4ece\u5b9e\u73b0\u4e0a\u770b\uff0c`loss_rank_event`\u4e0e\u4e4b\u524d\u7684`loss_rank`\u5728\u5f62\u5f0f\u4e0a\u5f88\u7c7b\u4f3c\uff0c\u533a\u522b\u5728\u4e8e\u5b83\u4f5c\u7528\u4e8e\u4e8b\u4ef6\uff08event\uff09\u5206\u652f\u3002\u8fd9\u610f\u5473\u7740\u4f60\u8bd5\u56fe\u5bf9\u4e24\u79cd\u4e0d\u540c\u7c7b\u578b\u7684\u4fe1\u606f\uff08\u53ef\u80fd\u662f\u56fe\u50cf\u4fe1\u606f\u548c\u4e8b\u4ef6\u4fe1\u606f\uff09\u5e94\u7528\u7c7b\u4f3c\u7684\u6ce8\u610f\u529b\u6b63\u5219\u5316\u673a\u5236\uff0c\u4ece\u800c\u4fc3\u8fdb\u6a21\u578b\u5728\u4e24\u4e2a\u5206\u652f\u4e0a\u5747\u80fd\u4fdd\u6301\u5408\u9002\u7684\u6ce8\u610f\u529b\u96c6\u4e2d\u5ea6\u3002\n\n3. **\u4ee3\u7801\u6539\u8fdb**\uff1a\u6ce8\u610f\u529b\u635f\u5931\u8ba1\u7b97\u4e2d\uff0c\u5b58\u5728\u4e00\u4e2apattern\u7684\u91cd\u590d\uff0c\u5373\u5c06`attn`\u5207\u5206\u3001\u91cd\u7ec4\u548cSVD\u5206\u89e3\u7684\u8fc7\u7a0b\u5728`loss_rank`\u548c`loss_rank_event`\u4e2d\u90fd\u6709\u3002\u5728\u5b9e\u9645\u9879\u76ee\u4e2d\uff0c\u4e3a\u4e86\u63d0\u9ad8\u4ee3\u7801\u7684\u590d\u7528\u6027\u548c\u53ef\u7ef4\u62a4\u6027\uff0c\u53ef\u4ee5\u8003\u8651\u5c06\u8fd9\u4e00\u8fc7\u7a0b\u62bd\u53d6\u6210\u4e00\u4e2a\u72ec\u7acb\u7684\u51fd\u6570\u6216\u65b9\u6cd5\u3002\n\n4. **\u8d85\u53c2\u6570\u8c03\u6574**\uff1a\u6ce8\u610f\u529b\u635f\u5931\u7684\u6743\u91cd\uff08\u5728\u8ba1\u7b97\u603b\u635f\u5931\u65f6\u7684\u6bd4\u91cd\uff09\u662f\u91cd\u8981\u7684\u8d85\u53c2\u6570\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u9700\u8981\u901a\u8fc7\u4ea4\u53c9\u9a8c\u8bc1\u7b49\u65b9\u6cd5\u6765\u786e\u5b9a\u8fd9\u4e9b\u6743\u91cd\u7684\u6700\u4f18\u503c\uff0c\u786e\u4fdd\u6a21\u578b\u80fd\u591f\u5728\u5173\u6ce8\u4e3b\u8981\u4efb\u52a1\u7684\u540c\u65f6\uff0c\u4e5f\u9002\u5f53\u5730\u8003\u8651\u65f6\u95f4\u7ef4\u5ea6\u7684\u4fe1\u606f\u3002\n\n\u603b\u800c\u8a00\u4e4b\uff0c\u4f60\u7684\u6539\u8fdb\u662f\u5bf9\u73b0\u6709\u6a21\u578b\u7684\u6709\u76ca\u5c1d\u8bd5\uff0c\u6709\u52a9\u4e8e\u63d0\u5347\u6a21\u578b\u5bf9\u4e0d\u540c\u4fe1\u606f\u901a\u9053\uff08\u5982\u56fe\u50cf\u548c\u4e8b\u4ef6\uff09\u7684\u5904\u7406\u80fd\u529b\u3002\u4f46\u662f\uff0c\u6700\u7ec8\u8fd9\u4e9b\u4fee\u6539\u662f\u5426\u80fd\u5e26\u6765\u5b9e\u9645\u6027\u80fd\u63d0\u5347\uff0c\u8fd8\u9700\u8981\u901a\u8fc7\u5b9e\u9a8c\u8fdb\u884c\u9a8c\u8bc1\u3002\u6b64\u5916\uff0c\u6ce8\u610f\u5230\u76f8\u5173\u4ee3\u7801\u7684\u91cd\u7528\u6027\u548c\u6a21\u5757\u5316\u8bbe\u8ba1\uff0c\u4ee5\u53ca\u8d85\u53c2\u6570\u7684\u5408\u7406\u9009\u62e9\uff0c\u90fd\u662f\u8fd9\u4e00\u8fc7\u7a0b\u4e2d\u9700\u8981\u91cd\u70b9\u5173\u6ce8\u7684\u56e0\u7d20\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":11237,"n_turns_in_conversation":16,"n_words":818,"source_row_offset":76596,"source_shard":"turns-00020.parquet","timestamp":"Sun, 28 Apr 2024 12:51:46 GMT","turn_id":"0f2bf22a8484f3c46021effa","turn_index":6,"user_text":"\u73b0\u5728\u7684\u4ee3\u7801\u2461\u6539\u4e3a\uff1afrom . 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        # 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 + rank_loss_event*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\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)"},{"assistant_initiates":false,"asst_text":"\u770b\u8d77\u6765\u60a8\u63d0\u51fa\u7684\u6539\u52a8\u5b9e\u9645\u4e0a\u5e76\u6ca1\u6709\u4fee\u6539\u539f\u6709\u7684\u6743\u91cd\u5206\u914d\u65b9\u5f0f\uff0c\u4f9d\u7136\u4fdd\u7559\u4e86`rank_loss`\u548c`rank_loss_event`\u5404\u81ea\u4e58\u4ee51.2\u7684\u6743\u91cd\u8bbe\u7f6e\u3002\u8fd9\u6837\u7684\u8bbe\u7f6e\u8868\u660e\uff0c\u60a8\u5e0c\u671b\u8fd9\u4e24\u4e2a\u635f\u5931\u9879\u76f8\u5bf9\u4e8e`giou_loss`\u3001`l1_loss`\u548c`focal_loss`\u6709\u66f4\u9ad8\u7684\u6743\u91cd\uff0c\u4ece\u800c\u66f4\u5f3a\u8c03\u6ce8\u610f\u529b\u6a21\u5757\u7684\u4f18\u5316\u3002\u8fd9\u662f\u5426\u5408\u9002\uff0c\u53d6\u51b3\u4e8e\u51e0\u4e2a\u56e0\u7d20\uff1a\n\n1. **\u4efb\u52a1\u7684\u9700\u6c42**\uff1a\u5982\u679c\u60a8\u7684\u6a21\u578b\u7279\u522b\u4f9d\u8d56\u4e8e\u6709\u6548\u7684\u6ce8\u610f\u529b\u673a\u5236\u4ee5\u6355\u6349\u65f6\u95f4\u548c\u7a7a\u95f4\u4fe1\u606f\uff0c\u90a3\u4e48\u7ed9\u8fd9\u4e24\u4e2a\u635f\u5931\u9879\u8d4b\u4e88\u8f83\u9ad8\u7684\u6743\u91cd\u53ef\u80fd\u662f\u5408\u7406\u7684\u3002\n\n2. **\u5b9e\u9a8c\u9a8c\u8bc1**\uff1a\u4efb\u4f55\u6743\u91cd\u7684\u8c03\u6574\u90fd\u9700\u8981\u901a\u8fc7\u5b9e\u9a8c\u8bc1\u660e\u5176\u6709\u6548\u6027\u3002\u5728\u6539\u52a8\u6743\u91cd\u540e\uff0c\u89c2\u5bdf\u6a21\u578b\u5728\u9a8c\u8bc1\u96c6\u4e0a\u7684\u8868\u73b0\uff0c\u770b\u662f\u5426\u6709\u6240\u63d0\u5347\uff0c\u6216\u8005\u662f\u5426\u51fa\u73b0\u8fc7\u62df\u5408\uff08\u6a21\u578b\u8fc7\u5ea6\u9002\u5e94\u8bad\u7ec3\u6570\u636e\uff0c\u800c\u5728\u672a\u89c1\u8fc7\u7684\u6570\u636e\u4e0a\u8868\u73b0\u4e0d\u4f73\uff09\u3002\n\n3. **\u9010\u6b65\u8c03\u6574**\uff1a\u8c03\u6574\u635f\u5931\u51fd\u6570\u4e2d\u5404\u9879\u7684\u6743\u91cd\u5e94\u8be5\u662f\u4e00\u4e2a\u9010\u6b65\u7684\u8fc7\u7a0b\u3002\u60a8\u53ef\u4ee5\u4ece\u8f83\u4f4e\u7684\u6743\u91cd\u5f00\u59cb\uff0c\u9010\u6e10\u589e\u52a0\uff08\u6216\u51cf\u5c11\uff09\uff0c\u6bcf\u6b21\u53d8\u52a8\u540e\u90fd\u4ed4\u7ec6\u89c2\u5bdf\u6a21\u578b\u7684\u8868\u73b0\u3002\n\n\u5f53\u60a8\u8003\u8651\u4f7f\u7528\u591a\u4e2a\u635f\u5931\u9879\u65f6\uff0c\u5e73\u8861\u5b83\u4eec\u4e4b\u95f4\u7684\u5173\u7cfb\u975e\u5e38\u5173\u952e\u3002\u8fc7\u9ad8\u7684\u6743\u91cd\u53ef\u80fd\u4f1a\u5bfc\u81f4\u6a21\u578b\u8fc7\u5206\u5173\u6ce8\u67d0\u4e2a\u65b9\u9762\uff0c\u800c\u5ffd\u7565\u4e86\u5176\u4ed6\u53ef\u80fd\u540c\u6837\u91cd\u8981\u7684\u65b9\u9762\u3002\u4e00\u79cd\u53ef\u80fd\u7684\u65b9\u6cd5\u662f\uff0c\u8bbe\u7f6e\u4e00\u4e2a\u76f8\u5bf9\u8f83\u4f4e\u7684\u521d\u59cb\u6743\u91cd\uff0c\u968f\u540e\u6839\u636e\u6a21\u578b\u5728\u9a8c\u8bc1\u96c6\u4e0a\u7684\u8868\u73b0\u8fdb\u884c\u8c03\u6574\u3002\u6b64\u5916\uff0c\u8fd8\u53ef\u4ee5\u5c1d\u8bd5\u81ea\u52a8\u6743\u91cd\u8c03\u6574\u7b56\u7565\uff0c\u5982\u57fa\u4e8e\u9a8c\u8bc1\u96c6\u8868\u73b0\u81ea\u52a8\u8c03\u6574\u6743\u91cd\uff0c\u6216\u8005\u4f7f\u7528\u7aef\u5230\u7aef\u7684\u5b66\u4e60\u7b56\u7565\u6765\u81ea\u52a8\u5e73\u8861\u4e0d\u540c\u635f\u5931\u9879\u7684\u8d21\u732e\u3002\n\n\u603b\u800c\u8a00\u4e4b\uff0c\u7ed9`rank_loss`\u548c`rank_loss_event`\u8d4b\u4e88\u8f83\u9ad8\u7684\u6743\u91cd\u53ef\u80fd\u662f\u4e00\u4e2a\u6709\u76ca\u7684\u5c1d\u8bd5\uff0c\u4f46\u6700\u7ec8\u662f\u5426\u91c7\u7528\u8fd9\u6837\u7684\u8bbe\u7f6e\uff0c\u5e94\u5f53\u57fa\u4e8e\u4ed4\u7ec6\u7684\u5b9e\u9a8c\u8bc4\u4f30\u548c\u5206\u6790\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":1432,"n_turns_in_conversation":16,"n_words":65,"source_row_offset":76597,"source_shard":"turns-00020.parquet","timestamp":"Sun, 28 Apr 2024 12:51:46 GMT","turn_id":"71afd0a702df23c214176429","turn_index":7,"user_text":"\u73b0\u5728\u5c06rank_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        # 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 + rank_loss_event*1.2\n\u6539\u4e3a\uff1arank_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        # 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 + rank_loss_event*1.2"},{"assistant_initiates":false,"asst_text":"\u5c06`rank_loss`\u548c`rank_loss_event`\u5408\u5e76\u4e3a`rank`\u7136\u540e\u4e58\u4ee5\u7edf\u4e00\u7684\u6743\u91cd\u503c(`1.2`)\u662f\u4e00\u4e2a\u5408\u7406\u7684\u8c03\u6574\uff0c\u4f7f\u5f97\u6574\u4e2a\u635f\u5931\u51fd\u6570\u7684\u8ba1\u7b97\u66f4\u52a0\u6e05\u6670\u7b80\u6d01\u3002\u8fd9\u79cd\u8c03\u6574\u65b9\u5f0f\u6709\u4ee5\u4e0b\u597d\u5904\uff1a\n\n1. **\u7b80\u5316\u6743\u91cd\u8c03\u6574**\uff1a\u5c06\u4e24\u4e2a\u4e0e\u6ce8\u610f\u529b\u76f8\u5173\u7684\u635f\u5931\u9879\u5408\u5e76\u540e\u7edf\u4e00\u52a0\u6743\uff0c\u7b80\u5316\u4e86\u6743\u91cd\u8c03\u6574\u8fc7\u7a0b\u3002\u73b0\u5728\uff0c\u60a8\u53ea\u9700\u8981\u8c03\u6574\u4e00\u4e2a\u6743\u91cd\u53c2\u6570(`1.2`)\u5c31\u53ef\u4ee5\u63a7\u5236\u4e0e\u6ce8\u610f\u529b\u76f8\u5173\u7684\u6574\u4f53\u635f\u5931\u8d21\u732e\uff0c\u800c\u4e0d\u662f\u5206\u522b\u4e3a`rank_loss`\u548c`rank_loss_event`\u8c03\u6574\u3002\n\n2. **\u4fdd\u6301\u5e73\u8861**\uff1a\u5c06\u8fd9\u4e24\u4e2a\u635f\u5931\u9879\u5408\u5e76\u4e3a\u4e00\u4e2a\u53ef\u4ee5\u786e\u4fdd\u4e24\u8005\u5bf9\u6a21\u578b\u8bad\u7ec3\u7684\u8d21\u732e\u662f\u5e73\u8861\u7684\u3002\u8fd9\u907f\u514d\u4e86\u56e0\u4e3a\u4e00\u4e2a\u9879\u6743\u91cd\u8c03\u6574\u8fc7\u9ad8\u800c\u5bf9\u53e6\u4e00\u4e2a\u9879\u4ea7\u751f\u7684\u4e0d\u5229\u5f71\u54cd\u3002\n\n3. **\u5f3a\u8c03\u6ce8\u610f\u529b\u673a\u5236\u7684\u91cd\u8981\u6027**\uff1a\u901a\u8fc7\u4e3a\u5408\u5e76\u540e\u7684`rank`\u635f\u5931\u8d4b\u4e88\u660e\u786e\u7684\u6743\u91cd\uff08\u8fd9\u91cc\u662f`1.2`\uff09\uff0c\u60a8\u5f3a\u8c03\u4e86\u5bf9\u6a21\u578b\u6765\u8bf4\u4f18\u5316\u6ce8\u610f\u529b\u673a\u5236\u662f\u91cd\u8981\u7684\u3002\u8fd9\u6709\u5229\u4e8e\u6a21\u578b\u5b66\u4f1a\u805a\u7126\u4e8e\u5173\u952e\u4fe1\u606f\uff0c\u53ef\u80fd\u5bf9\u6574\u4f53\u6027\u80fd\u6709\u6b63\u9762\u5f71\u54cd\u3002\n\n\u5728\u5b9e\u73b0\u8fd9\u79cd\u8c03\u6574\u540e\uff0c\u91cd\u8981\u7684\u662f\u8981\u901a\u8fc7\u5b9e\u9a8c\u9a8c\u8bc1\u5176\u6709\u6548\u6027\u3002\u5efa\u8bae\u8fdb\u884c\u4ee5\u4e0b\u6b65\u9aa4\uff1a\n\n- **A/B\u6d4b\u8bd5**\uff1a\u5bf9\u6bd4\u8c03\u6574\u524d\u540e\u7684\u6a21\u578b\u8868\u73b0\uff0c\u7279\u522b\u662f\u5728\u9a8c\u8bc1\u96c6\u548c\u6d4b\u8bd5\u96c6\u4e0a\u7684\u6027\u80fd\u3002\u89c2\u5bdf\u5408\u5e76\u540e\u7684`rank`\u635f\u5931\u9879\u5bf9\u6a21\u578b\u6027\u80fd\u7684\u5f71\u54cd\u3002\n\n- **\u6743\u91cd\u8c03\u6574**\uff1a\u57fa\u4e8e\u5b9e\u9a8c\u7ed3\u679c\uff0c\u5fae\u8c03`rank`\u635f\u5931\u7684\u6743\u91cd(`1.2`)\u3002\u5f00\u59cb\u65f6\u53ef\u4ee5\u9009\u62e9\u4e00\u4e2a\u57fa\u7ebf\u503c\u8fdb\u884c\u6d4b\u8bd5\uff0c\u6839\u636e\u6a21\u578b\u7684\u8868\u73b0\u9010\u6e10\u8c03\u6574\u6743\u91cd\u503c\u3002\n\n- **\u907f\u514d\u8fc7\u62df\u5408**\uff1a\u76d1\u63a7\u8fc7\u62df\u5408\u7684\u60c5\u51b5\uff0c\u786e\u4fdd\u6a21\u578b\u5728\u8bad\u7ec3\u96c6\u4e0a\u7684\u6539\u8fdb\u4e0d\u4f1a\u4ee5\u727a\u7272\u6cdb\u5316\u80fd\u529b\u4e3a\u4ee3\u4ef7\u3002\n\n\u6700\u540e\uff0c\u8bf7\u8bb0\u5f97\u8fd9\u6837\u7684\u6743\u91cd\u9009\u62e9\u662f\u6839\u636e\u7279\u5b9a\u4efb\u52a1\u548c\u6570\u636e\u96c6\u6709\u6240\u53d8\u5316\u7684\u3002\u56e0\u6b64\uff0c\u60a8\u53ef\u80fd\u9700\u8981\u6839\u636e\u81ea\u5df1\u7684\u5177\u4f53\u60c5\u51b5\u8fdb\u884c\u8c03\u6574\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":1093,"n_turns_in_conversation":16,"n_words":47,"source_row_offset":76598,"source_shard":"turns-00020.parquet","timestamp":"Sun, 28 Apr 2024 12:51:46 GMT","turn_id":"2dbbd63b453f48bb6412ed2a","turn_index":8,"user_text":"\u90a3\u4e48\u73b0\u5728\u6539\u4e3a\uff1a        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"},{"assistant_initiates":false,"asst_text":"\u6211\u6ce8\u610f\u5230\u60a8\u4e24\u6b21\u8d34\u51fa\u7684\u4ee3\u7801\u51e0\u4e4e\u5b8c\u5168\u76f8\u540c\uff0c\u4f3c\u4e4e\u60a8\u60f3\u5bf9\u6bd4\u539f\u59cb\u7684\u635f\u5931\u8bbe\u7f6e\u548c\u60a8\u6240\u8ba1\u5212\u7684\u4fee\u6539\u3002\u7136\u800c\uff0c\u5728\u60a8\u63d0\u51fa\u7684\u4fee\u6539\u4e2d\uff0c\u770b\u8d77\u6765\u5e76\u6ca1\u6709\u5305\u542b\u4efb\u4f55\u65b0\u7684\u4ee3\u7801\u6216\u5bf9\u539f\u59cb\u635f\u5931\u8bbe\u7f6e\u7684\u66f4\u6539\u3002\u5982\u679c\u60a8\u7684\u76ee\u6807\u662f\u8ba8\u8bba\u6216\u8be2\u95ee\u5173\u4e8e\u635f\u5931\u51fd\u6570\u7684\u8bbe\u5b9a\u6216\u5982\u4f55\u8fdb\u884c\u6539\u8fdb\uff0c\u8bf7\u786e\u4fdd\u63d0\u4f9b\u5177\u4f53\u7684\u4fee\u6539\u6216\u8be6\u7ec6\u8bf4\u660e\u60a8\u7684\u7591\u95ee\u3002\n\n\u5982\u679c\u60a8\u7684\u610f\u56fe\u662f\u8be2\u95ee\u5173\u4e8e\u5982\u4f55\u6539\u8fdb\u635f\u5931\u51fd\u6570\u7684\u8bbe\u7f6e\uff0c\u4ee5\u4e0b\u662f\u4e00\u4e9b\u666e\u904d\u7684\u5efa\u8bae\uff1a\n\n1. **\u7ed3\u5408\u591a\u4efb\u52a1\u5b66\u4e60**\uff1a\u5982\u679c\u60a8\u7684\u6a21\u578b\u6709\u591a\u4e2a\u8f93\u51fa\uff0c\u8003\u8651\u4e3a\u6bcf\u4e2a\u8f93\u51fa\u8bbe\u7f6e\u4e0d\u540c\u7684\u635f\u5931\u51fd\u6570\uff0c\u5e76\u4e14\u6839\u636e\u4efb\u52a1\u7684\u96be\u5ea6\u6216\u91cd\u8981\u6027\u6765\u8c03\u6574\u5404\u4e2a\u635f\u5931\u51fd\u6570\u7684\u6743\u91cd\u3002\n\n2. **\u4f7f\u7528\u52a8\u6001\u6743\u91cd**\uff1a\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u52a8\u6001\u8c03\u6574\u635f\u5931\u9879\u6743\u91cd\uff0c\u8fd9\u53ef\u4ee5\u57fa\u4e8e\u6bcf\u4e2a\u635f\u5931\u5bf9\u6a21\u578b\u5728\u9a8c\u8bc1\u96c6\u4e0a\u6027\u80fd\u7684\u8d21\u732e\u5ea6\u6765\u5b9e\u73b0\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528\u201c\u635f\u5931\u52a0\u6743\u5e73\u5747\u201d\u7b56\u7565\uff0c\u901a\u8fc7\u635f\u5931\u9879\u5bf9\u6a21\u578b\u6539\u8fdb\u7684\u8d21\u732e\u5927\u5c0f\u6765\u52a8\u6001\u5730\u8c03\u6574\u5b83\u4eec\u7684\u6743\u91cd\u3002\n\n3. **\u5f15\u5165\u6b63\u5219\u9879**\uff1a\u8003\u8651\u5728\u635f\u5931\u51fd\u6570\u4e2d\u52a0\u5165\u6b63\u5219\u9879\uff0c\u5982L1\u6216L2\u6b63\u5219\uff0c\u4ee5\u907f\u514d\u6a21\u578b\u8fc7\u62df\u5408\u3002\u8fd9\u5c24\u5176\u6709\u7528\u4e8e\u6a21\u578b\u53c2\u6570\u4f17\u591a\u65f6\u3002\n\n4. **\u4f7f\u7528\u81ea\u5b9a\u4e49\u635f\u5931\u51fd\u6570**\uff1a\u6839\u636e\u5177\u4f53\u7684\u4efb\u52a1\u9700\u6c42\uff0c\u53ef\u80fd\u9700\u8981\u8bbe\u8ba1\u81ea\u5b9a\u4e49\u7684\u635f\u5931\u51fd\u6570\u3002\u4f8b\u5982\uff0c\u5982\u679c\u4f60\u5173\u6ce8\u7684\u662f\u6a21\u578b\u8f93\u51fa\u7684\u6392\u540d\uff0c\u53ef\u4ee5\u8003\u8651\u4f7f\u7528\u6392\u5e8f\u635f\u5931\uff08\u5982pairwise loss\u6216listwise loss\uff09\u3002\n\n5. **\u4e0d\u540c\u9636\u6bb5\u4f7f\u7528\u4e0d\u540c\u635f\u5931\u8bbe\u7f6e**\uff1a\u5728\u6a21\u578b\u8bad\u7ec3\u7684\u4e0d\u540c\u9636\u6bb5\u4f7f\u7528\u4e0d\u540c\u7684\u635f\u5931\u51fd\u6570\u6216\u6743\u91cd\u914d\u7f6e\u4e5f\u662f\u4e00\u4e2a\u9009\u62e9\u3002\u4f8b\u5982\uff0c\u521d\u59cb\u9636\u6bb5\u4f7f\u7528\u66f4\u91cd\u7684\u6b63\u5219\u5316\u6765\u9632\u6b62\u8fc7\u62df\u5408\uff0c\u540e\u9762\u9010\u6e10\u51cf\u5c0f\u6b63\u5219\u5316\u6743\u91cd\u4ee5\u5141\u8bb8\u6a21\u578b\u5bf9\u6570\u636e\u8fdb\u884c\u66f4\u6df1\u5165\u7684\u62df\u5408\u3002\n\n6. **\u5b9e\u9a8c\u548c\u9a8c\u8bc1**\uff1a\u65e0\u8bba\u4f60\u5982\u4f55\u8c03\u6574\u635f\u5931\u51fd\u6570\uff0c\u90fd\u5e94\u8be5\u901a\u8fc7\u5b9e\u9a8c\u5728\u9a8c\u8bc1\u96c6\u4e0a\u9a8c\u8bc1\u5176\u6548\u679c\u3002\u540c\u65f6\uff0c\u8bb0\u5f55\u6bcf\u6b21\u5b9e\u9a8c\u7684\u8bbe\u7f6e\u548c\u7ed3\u679c\uff0c\u4ee5\u4fbf\u627e\u5230\u6700\u4f18\u914d\u7f6e\u3002\n\n\u5e0c\u671b\u8fd9\u4e9b\u5efa\u8bae\u80fd\u5bf9\u60a8\u6709\u6240\u5e2e\u52a9\uff01\u5982\u679c\u60a8\u6709\u5177\u4f53\u7684\u60f3\u6cd5\u6216\u8005\u4ee3\u7801\u4fee\u6539\u90e8\u5206\uff0c\u8bf7\u63d0\u4f9b\u66f4\u8be6\u7ec6\u7684\u4fe1\u606f\u4ee5\u4fbf\u8fdb\u4e00\u6b65\u8ba8\u8bba\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":12182,"n_turns_in_conversation":16,"n_words":835,"source_row_offset":76599,"source_shard":"turns-00020.parquet","timestamp":"Sun, 28 Apr 2024 12:51:46 GMT","turn_id":"d4f393e81cd0576956408da3","turn_index":9,"user_text":"\u539f\u59cb\u7684loss\u8bbe\u7f6e\uff1afrom . 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\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        # weighted sum\n        loss = self.loss_weight['giou'] * giou_loss + self.loss_weight['l1'] * l1_loss + self.loss_weight['focal'] * location_loss\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 \u73b0\u5728loss\u8bbe\u7f6e\uff1afrom . 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\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        # weighted sum\n        loss = self.loss_weight['giou'] * giou_loss + self.loss_weight['l1'] * l1_loss + self.loss_weight['focal'] * location_loss\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 "},{"assistant_initiates":false,"asst_text":"\u6839\u636e\u60a8\u63d0\u4f9b\u7684\u8bad\u7ec3\u914d\u7f6e\uff0c`rank loss`\u7684\u7cfb\u6570\u8bbe\u7f6e\u662f\u5426\u5408\u7406\uff0c\u53d6\u51b3\u4e8e`rank loss`\u5728\u6574\u4f53\u635f\u5931\u51fd\u6570\u4e2d\u7684\u4f5c\u7528\u4ee5\u53ca\u60a8\u7684\u7279\u5b9a\u76ee\u6807\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u8003\u8651\u56e0\u7d20\uff1a\n\n1. **\u635f\u5931\u6743\u91cd\u5e73\u8861**\uff1a`GIOU_WEIGHT`\u3001`L1_WEIGHT`\u548c`FOCAL_WEIGHT`\u5728\u60a8\u7684\u914d\u7f6e\u4e2d\u5206\u522b\u88ab\u8bbe\u7f6e\u4e3a`1`\u3001`14`\u548c`1.0`\u3002\u8fd9\u8bf4\u660eL1\u635f\u5931\u88ab\u7ed9\u4e88\u4e86\u76f8\u5bf9\u8f83\u5927\u7684\u91cd\u89c6\uff0c\u8fd9\u53ef\u80fd\u662f\u56e0\u4e3a\u4f4d\u7f6e\u7cbe\u5ea6\u5bf9\u4e8e\u60a8\u7684\u4efb\u52a1\u975e\u5e38\u5173\u952e\u3002\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c`rank loss`\u7684\u6743\u91cd\uff08\u4f8b\u5982\u662f\u5426\u91c7\u7528`1.2`\u4e58\u4ee5`rank`\u548c`rank_event`\u7684\u603b\u5408\uff09\u9700\u8981\u4e0e\u8fd9\u4e9b\u5df2\u6709\u7684\u6743\u91cd\u76f8\u5e73\u8861\u3002\u5982\u679c`rank loss`\u548c`rank_loss_event`\u4e3b\u8981\u662f\u4e3a\u4e86\u4f18\u5316\u6ce8\u610f\u529b\u673a\u5236\u7684\u7ec6\u8282\uff0c\u800c\u8fd9\u5bf9\u4e8e\u6574\u4f53\u6027\u80fd\u7684\u63d0\u5347\u662f\u6b21\u8981\u7684\uff0c\u5219\u5176\u7cfb\u6570\u4e0d\u5e94\u592a\u9ad8\uff0c\u4ee5\u514d\u5206\u6563\u5bf9\u4e3b\u8981\u635f\u5931\u9879\u7684\u4f18\u5316\u7126\u70b9\u3002\n\n2. **\u4efb\u52a1\u76f8\u5173\u6027**\uff1a`rank loss`\u7684\u4f5c\u7528\u662f\u5f3a\u5316\u6a21\u578b\u7684\u6ce8\u610f\u529b\u673a\u5236\uff0c\u5982\u679c\u8fd9\u5bf9\u4e8e\u60a8\u7684\u5e94\u7528\uff08\u4f8b\u5982\uff0c\u5904\u7406\u5927\u91cf\u5e8f\u5217\u6570\u636e\uff0c\u9700\u8981\u6a21\u578b\u96c6\u4e2d\u6ce8\u610f\u529b\u5728\u5173\u952e\u5e27\u6216\u4e8b\u4ef6\u4e0a\uff09\u662f\u81f3\u5173\u91cd\u8981\u7684\uff0c\u90a3\u4e48\u7ed9\u4e88\u5b83\u8f83\u9ad8\u7684\u6743\u91cd\u53ef\u80fd\u662f\u5408\u7406\u7684\u3002\n\n3. **\u5b9e\u9a8c\u53cd\u9988**\uff1a\u6700\u7ec8\uff0c`rank loss`\u7684\u6743\u91cd\u5e94\u8be5\u57fa\u4e8e\u5b9e\u9a8c\u53cd\u9988\u8fdb\u884c\u8c03\u6574\u3002\u5f00\u59cb\u65f6\uff0c\u60a8\u53ef\u4ee5\u8bbe\u7f6e\u4e00\u4e2a\u57fa\u672c\u5047\u8bbe\u7684\u6743\u91cd\uff0c\u5982`1.2`\uff0c\u7136\u540e\u6839\u636e\u6a21\u578b\u5728\u9a8c\u8bc1\u96c6\u4e0a\u7684\u6027\u80fd\u8868\u73b0\u8c03\u6574\u3002\u5982\u679c\u589e\u52a0`rank loss`\u7684\u6743\u91cd\u80fd\u660e\u663e\u63d0\u9ad8\u6a21\u578b\u6027\u80fd\uff0c\u90a3\u4e48\u4fdd\u6301\u6216\u8fdb\u4e00\u6b65\u8c03\u9ad8\u6743\u91cd\u662f\u5408\u9002\u7684\uff1b\u5982\u679c\u6027\u80fd\u63d0\u9ad8\u6709\u9650\u6216\u8005\u6a21\u578b\u51fa\u73b0\u8fc7\u62df\u5408\u7b49\u95ee\u9898\uff0c\u5c31\u9700\u8981\u9002\u5f53\u964d\u4f4e\u6743\u91cd\u3002\n\n4. **\u8d85\u53c2\u6570\u8c03\u4f18**\uff1a\u8003\u8651\u4f7f\u7528\u81ea\u52a8\u8c03\u53c2\u5de5\u5177\uff08\u5982\u7f51\u683c\u641c\u7d22\u3001\u968f\u673a\u641c\u7d22\u3001\u8d1d\u53f6\u65af\u4f18\u5316\u7b49\uff09\u6765\u5bfb\u627e\u6700\u4f18\u7684`rank loss`\u6743\u91cd\uff0c\u8fd9\u6837\u53ef\u4ee5\u66f4\u7cfb\u7edf\u5730\u8bc4\u4f30\u4e0d\u540c\u6743\u91cd\u5bf9\u6a21\u578b\u6027\u80fd\u7684\u5f71\u54cd\u3002\n\n\u7efc\u4e0a\u6240\u8ff0\uff0c\u65e0\u6cd5\u76f4\u63a5\u65ad\u8a00\u60a8\u8bbe\u7f6e\u7684`rank loss`\u7684\u7cfb\u6570\u662f\u5426\u7edd\u5bf9\u5408\u7406\uff0c\u9700\u8981\u6839\u636e\u60a8\u7684\u5177\u4f53\u4efb\u52a1\u9700\u6c42\u3001\u6a21\u578b\u7ed3\u6784\u4ee5\u53ca\u901a\u8fc7\u5927\u91cf\u5b9e\u9a8c\u6765\u786e\u5b9a\u3002\u5b9e\u9a8c\u8fed\u4ee3\u662f\u5bfb\u627e\u6700\u4f73\u8bbe\u7f6e\u7684\u5173\u952e\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":2295,"n_turns_in_conversation":16,"n_words":190,"source_row_offset":76600,"source_shard":"turns-00020.parquet","timestamp":"Sun, 28 Apr 2024 12:51:46 GMT","turn_id":"d7d9e97a98334fae99110135","turn_index":10,"user_text":"\u5f53\u524d\u7684\u8bad\u7ec3\u914d\u7f6e\u4e2d\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: 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\u4e48rank loss\u7684\u7cfb\u6570\u8bbe\u7f6e\u5408\u7406\u5417\uff1f"}],"shard":{"index":20,"name":"turns-00020.parquet","rows":90031}}