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question:由于counter_guide的输入维度是768,那么引入的参数过多，是否可以进行降维，以防止过拟合？
answer:当Counter_Guide的输入维度为768时，确实，对于一些较小的数据集或较为简单的任务，较高的维度可能会引入不必要的复杂性和过拟合的风险。降维是处理这一问题的有效策略之一，可以通过以下几种方式实现： # 1. 线性降维层 在Counter_Guide中引入一个线性变换层或多个线性层来显式地减少特征的维度。例如，在Cross_Attention模块的输入前加入： ```python class DimensionReduction(nn.Module): def __init__(self, input_dim, output_dim): super(DimensionReduction, self).__init__() self.linear = nn.Linear(input_dim, output_dim) def forward(self, x): return self.linear(x) ``` 对于768维的输入，可以通过设置`output_dim`为更小的值（如256或512）来减少特征维度，以降低模型复杂度。 # 2. 嵌入式降维 在模型的Counter_Guide部分使用嵌入技术（如自注意力机制或压缩感知技术）来隐式地减少特征的维度。这种方法可以通过设计更加精巧的、用于特征压缩的自注意力结构来实现。 例如，在Cross_Attention中，可以将`query`、`key`和`value`的输出维度设计得小于输入维度： ```python self.query = nn.Linear(input_channels, reduced_channels) self.key = nn.Linear(input_channels, reduced_channels) self.value = nn.Linear(input_channels, reduced_channels) ``` 这里，`reduced_channels`代表经过降维后的特征维度，例如可以将768降维到256或更少。 # 3. 卷积降维 对于图像和视频等空间数据，可以通过使用卷积层来实现降维。一维或二维卷积层（取决于数据结构）可以用来替代输入的线性层，实现降维的同时保留空间或时序信息。 ```python class Conv1dReduction(nn.Module): def __init__(self, input_channels, output_channels): super(Conv1dReduction, self).__init__() self.conv = nn.Conv1d(input_channels, output_channels, kernel_size=1) def forward(self, x): # 假设x的形状为(Batch, Channels, Seq_len), 与Conv1d期望的输入一致 return self.conv(x) ``` # 4. 自动编码器 还可以设计一个简单的自动编码器（AE）或变分自动编码器（VAE）进行降维，这种方法既可以以无监督的方式学习到数据的压缩表示，也可以在模型训练中端到端地进行优化。 引入降维操作可以减少模型的参数数量和计算复杂度，有助于缓解过拟合问题，并提高模型的泛化能力。然而，同时也要注意，过度降维可能会导致重要信息的丢失，因此应该通过实验来确定合适的降维程度。
question:那么是否先不进行降维，以及训练配置进行观察？
answer:如果您担心直接引入降维会导致重要信息的丢失，或想要首先验证Counter_Guide构建的模型在没有降维情况下的表现，确实，先不进行降维是一个合理的初步方案。这种方式允许您： 1. **基线性能评估**：提供一个没有降维的模型性能基线，使您能够在后续实验中，通过比较评估降维对模型性能的具体影响。 2. **检测过拟合程度**：观察未经降维的模型在训练和验证/测试数据上的性能差异，以评估模型是否存在过拟合问题，以及过拟合的严重程度。 3. **模型能力评估**：未经降维，模型将处理较高维度的特征，这有助于全面评估模型处理复杂特征空间的能力。 # 训练配置进行观察的建议： 在初始阶段，您可以保持训练配置中的大部分参数不变，但也可以考虑对以下几点进行轻微调整以优化训练过程： - **学习率调整**：依据模型的复杂度和数据集的大小，精细调整学习率，以保证模型稳定学习。 - **正则化策略**：启用或增加Dropout、权重衰减等正则化策略，以防过拟合并提高模型的泛化能力。 - **早停策略(Early Stopping)**：在验证集损失不再下降时停止训练，以避免过拟合。 - **学习率调度**：根据验证集的性能改变学习率（如使用学习率衰减，或当性能停滞时降低学习率）。 进行这些初步实验后，您可以根据模型的具体表现决定是否需要降维，以及是否需要对训练配置做更精细的调整。重要的是，要定期评估模型在训练集和验证集上的性能，以确保模型能够有效地从数据中学习，并具有良好的泛化能力。 # 监控指标： - **训练集和验证集上的损失**：追踪这些指标以评估过拟合情况。 - **关键性能指标**：例如精度、召回率、F1分数等，根据您的任务目标选取合适的指标。 在收集到足够的信息后，您可以更有信心地做出是否进行降维等进一步优化决策。
question:分析一下：原始模型的train loss是0.4489，val loss是1.205，修改模型train loss是1.164，val loss是1.553，修改后的模型的SR分数较低，但是PR和NPR分数较高。
answer:根据提供的信息，原始模型在训练集上有较低的损失（0.4489）并且在验证集上的损失相对较高（1.205），修改后的模型在训练集和验证集上的损失分别是1.164和1.553。这些数据表明了几个关键点： 1. **过拟合**： - 原始模型的表现显示出一定程度的过拟合，即在训练集上表现很好，但在验证集上表现变差。这是由于模型损失在训练集上较低，而在验证集上显著更高的事实所暗示的。 - 修改后的模型在训练集和验证集上的损失更接近，表明该模型可能不如原始模型过拟合严重。不过，两者的损失值都较高，尤其是验证损失，这意味着可能存在一定的欠拟合，或者模型容量不足以捕捉数据中的关键信息。 2. **模型表现（PR、NPR和SR分数）**： - 修改后的模型尽管在验证集上损失较高，但是PR（精确率）和NPR（负精确率）分数较高，说明模型在一定程度上能够更准确地识别正样本和负样本。 - SR（成功率）分数较低表明，在某些情况下，尽管模型能准确识别样本，但在整体任务（如跟踪、分类等）的执行上不尽如人意。成功率通常涵盖了任务的整体表现，而PR和NPR更关注分类的准确性。 3. **分析和优化方向**： - **欠拟合与模型容量**：考虑到修改后的模型的损失值较高，可能需要增加模型的容量或进一步优化模型结构，以便更好地捕捉和学习数据中的复杂特征和关系。 - **针对SR分数的优化**：考虑到SR分数的降低，可能需要更深入地分析模型在执行整体任务时的短板，识别是哪部分的性能不足导致了整体成功率的下降，并针对性地进行优化。 - **过拟合与正则化**：对于原始模型表现出的过拟合迹象，进一步采取正则化手段，如增大Dropout率、引入数据增强或使用更强的权重衰减等，可能帮助改善模型在验证集上的泛化能力。 总而言之，模型的修改虽然提高了PR和NPR，但若想提升整体的任务性能（如SR分数），需要进一步调整和优化模型结构、增强模型的泛化能力以及加强对关键特征的捕捉能力。
question:代码① import math import logging from functools import partial from collections import OrderedDict from copy import deepcopy import torch import torch.nn as nn import torch.nn.functional as F from timm.models.layers import to_2tuple from lib.models.layers.patch_embed import PatchEmbed, PatchEmbed_event, xcorr_depthwise from .utils import combine_tokens, recover_tokens from .vit import VisionTransformer from ..layers.attn_blocks import CEBlock _logger = logging.getLogger(__name__) class VisionTransformerCE(VisionTransformer): """ Vision Transformer with candidate elimination (CE) module A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` - https://arxiv.org/abs/2010.11929 Includes distillation token & head support for `DeiT: Data-efficient Image Transformers` - https://arxiv.org/abs/2012.12877 """ def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True, representation_size=None, distilled=False, drop_rate=0., attn_drop_rate=0., drop_path_rate=0., embed_layer=PatchEmbed, norm_layer=None, act_layer=None, weight_init='', ce_loc=None, ce_keep_ratio=None): """ Args: img_size (int, tuple): input image size patch_size (int, tuple): patch size in_chans (int): number of input channels num_classes (int): number of classes for classification head embed_dim (int): embedding dimension depth (int): depth of transformer num_heads (int): number of attention heads mlp_ratio (int): ratio of mlp hidden dim to embedding dim qkv_bias (bool): enable bias for qkv if True representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set distilled (bool): model includes a distillation token and head as in DeiT models drop_rate (float): dropout rate attn_drop_rate (float): attention dropout rate drop_path_rate (float): stochastic depth rate embed_layer (nn.Module): patch embedding layer norm_layer: (nn.Module): normalization layer weight_init: (str): weight init scheme """ # super().__init__() super().__init__() if isinstance(img_size, tuple): self.img_size = img_size else: self.img_size = to_2tuple(img_size) self.patch_size = patch_size self.in_chans = in_chans self.num_classes = num_classes self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models self.num_tokens = 2 if distilled else 1 norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) act_layer = act_layer or nn.GELU self.patch_embed = embed_layer( img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) num_patches = self.patch_embed.num_patches self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) self.dist_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if distilled else None self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim)) self.pos_drop = nn.Dropout(p=drop_rate) self.pos_embed_event = PatchEmbed_event(in_chans=32, embed_dim=768, kernel_size=4, stride=4) # self.pos_embed_event = PatchEmbed_event(in_chans=32, embed_dim=768, kernel_size=4, stride=4) # self.pos_embed_event_z = PatchEmbed_event(in_chans=32, embed_dim=768, kernel_size=3, stride=1) # attn = CrossAttn(768, 4, 3072, 0.1, 'relu') # self.cross_attn = Iter_attn(attn, 2) dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule blocks = [] ce_index = 0 self.ce_loc = ce_loc for i in range(depth): ce_keep_ratio_i = 1.0 if ce_loc is not None and i in ce_loc: ce_keep_ratio_i = ce_keep_ratio[ce_index] ce_index += 1 blocks.append( CEBlock( dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer, keep_ratio_search=ce_keep_ratio_i) ) self.blocks = nn.Sequential(*blocks) self.norm = norm_layer(embed_dim) self.init_weights(weight_init) def forward_features(self, z, x, event_z, event_x, mask_z=None, mask_x=None, ce_template_mask=None, ce_keep_rate=None, return_last_attn=False ): B, H, W = x.shape[0], x.shape[2], x.shape[3] event_z = self.pos_embed_event(event_z) # [:,:,:,:1000] event_x = self.pos_embed_event(event_x) # B 768 1024 x = self.patch_embed(x) z = self.patch_embed(z) event_z += self.pos_embed_z event_x += self.pos_embed_x z += self.pos_embed_z x += self.pos_embed_x # attention mask handling # B, H, W if mask_z is not None and mask_x is not None: mask_z = F.interpolate(mask_z[None].float(), scale_factor=1. / self.patch_size).to(torch.bool)[0] mask_z = mask_z.flatten(1).unsqueeze(-1) mask_x = F.interpolate(mask_x[None].float(), scale_factor=1. / self.patch_size).to(torch.bool)[0] mask_x = mask_x.flatten(1).unsqueeze(-1) mask_x = combine_tokens(mask_z, mask_x, mode=self.cat_mode) mask_x = mask_x.squeeze(-1) if self.add_cls_token: cls_tokens = self.cls_token.expand(B, -1, -1) cls_tokens = cls_tokens + self.cls_pos_embed if self.add_sep_seg: x += self.search_segment_pos_embed z += self.template_segment_pos_embed x = combine_tokens(z, event_z, x, event_x, mode=self.cat_mode) # 64+64+256+256=640 # x = combine_tokens(z, x, event_z, event_x, mode=self.cat_mode) # 64+64+256+256=640 if self.add_cls_token: x = torch.cat([cls_tokens, x], dim=1) x = self.pos_drop(x) lens_z = self.pos_embed_z.shape[1] lens_x = self.pos_embed_x.shape[1] global_index_t = torch.linspace(0, lens_z - 1, lens_z).to(x.device) global_index_t = global_index_t.repeat(B, 1) global_index_s = torch.linspace(0, lens_x - 1, lens_x).to(x.device) global_index_s = global_index_s.repeat(B, 1) removed_indexes_s = [] for i, blk in enumerate(self.blocks): x, global_index_t, global_index_s, removed_index_s, attn = blk(x, global_index_t, global_index_s, mask_x, ce_template_mask, ce_keep_rate) if self.ce_loc is not None and i in self.ce_loc: removed_indexes_s.append(removed_index_s) x = self.norm(x) lens_x_new = global_index_s.shape[1] lens_z_new = global_index_t.shape[1] z = x[:, :lens_z_new*2] x = x[:, lens_z_new*2:] if removed_indexes_s and removed_indexes_s[0] is not None: removed_indexes_cat = torch.cat(removed_indexes_s, dim=1) pruned_lens_x = lens_x - lens_x_new pad_x = torch.zeros([B, pruned_lens_x, x.shape[2]], device=x.device) x = torch.cat([x, pad_x], dim=1) index_all = torch.cat([global_index_s, removed_indexes_cat], dim=1) # recover original token order C = x.shape[-1] x = torch.zeros_like(x).scatter_(dim=1, index=index_all.unsqueeze(-1).expand(B, -1, C).to(torch.int64), src=x) x = recover_tokens(x, lens_z_new, lens_x, mode=self.cat_mode) x = x[:, :lens_x] # RGB head x = torch.cat([event_x, x], dim=1) # x = x[:, lens_x//2:] # event head # x = torch.cat([z, x], dim=1) # re-concatenate with the template, which may be further used by other modules # x, event_x = x[:, :lens_x//2], x[:, lens_x//2:] # x = x[:, -lens_x//2:] aux_dict = { "attn": attn, "removed_indexes_s": removed_indexes_s, # used for visualization } return x, aux_dict def forward(self, z, x, event_z, event_x, ce_template_mask=None, ce_keep_rate=None, tnc_keep_rate=None, return_last_attn=False): x, aux_dict = self.forward_features(z, x, event_z, event_x, ce_template_mask=ce_template_mask, ce_keep_rate=ce_keep_rate,) return x, aux_dict def _create_vision_transformer(pretrained=False, **kwargs): model = VisionTransformerCE(**kwargs) if pretrained: if 'npz' in pretrained: model.load_pretrained(pretrained, prefix='') else: checkpoint = torch.load(pretrained, map_location="cpu") missing_keys, unexpected_keys = model.load_state_dict(checkpoint["model"], strict=False) print('Load pretrained model from: ' + pretrained) return model def vit_base_patch16_224_ce(pretrained=False, **kwargs): """ ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929). """ model_kwargs = dict( patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs) model = _create_vision_transformer(pretrained=pretrained, **model_kwargs) return model def vit_large_patch16_224_ce(pretrained=False, **kwargs): """ ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929). """ model_kwargs = dict( patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs) model = _create_vision_transformer(pretrained=pretrained, **model_kwargs) return model 代码② # 将 4输入分开，构建新的相同模态结合的2输入，2分支 import math import logging from functools import partial from collections import OrderedDict from copy import deepcopy import torch import torch.nn as nn import torch.nn.functional as F from timm.models.layers import to_2tuple from lib.models.layers.patch_embed import PatchEmbed, PatchEmbed_event, xcorr_depthwise from .utils import combine_tokens, recover_tokens from .vit import VisionTransformer from ..layers.attn_blocks import CEBlock _logger = logging.getLogger(__name__) class VisionTransformerCE(VisionTransformer): """ Vision Transformer with candidate elimination (CE) module A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` - https://arxiv.org/abs/2010.11929 Includes distillation token & head support for `DeiT: Data-efficient Image Transformers` - https://arxiv.org/abs/2012.12877 """ def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True, representation_size=None, distilled=False, drop_rate=0., attn_drop_rate=0., drop_path_rate=0., embed_layer=PatchEmbed, norm_layer=None, act_layer=None, weight_init='', ce_loc=None, ce_keep_ratio=None): super().__init__() if isinstance(img_size, tuple): self.img_size = img_size else: self.img_size = to_2tuple(img_size) self.patch_size = patch_size self.in_chans = in_chans self.num_classes = num_classes self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models self.num_tokens = 2 if distilled else 1 norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) act_layer = act_layer or nn.GELU self.patch_embed = embed_layer( img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) num_patches = self.patch_embed.num_patches self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) self.dist_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if distilled else None self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim)) self.pos_drop = nn.Dropout(p=drop_rate) self.pos_embed_event = PatchEmbed_event(in_chans=32, embed_dim=768, kernel_size=4, stride=4) dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule blocks = [] ce_index = 0 self.ce_loc = ce_loc for i in range(depth): ce_keep_ratio_i = 1.0 if ce_loc is not None and i in ce_loc: ce_keep_ratio_i = ce_keep_ratio[ce_index] ce_index += 1 blocks.append( CEBlock( dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer, keep_ratio_search=ce_keep_ratio_i) ) self.blocks = nn.Sequential(*blocks) self.norm = norm_layer(embed_dim) self.init_weights(weight_init) def forward_features(self, z, x, event_z, event_x, mask_z=None, mask_x=None, ce_template_mask=None, ce_keep_rate=None, return_last_attn=False ): # 分支1 处理流程 B, H, W = x.shape[0], x.shape[2], x.shape[3] x = self.patch_embed(x) z = self.patch_embed(z) z += self.pos_embed_z x += self.pos_embed_x if mask_z is not None and mask_x is not None: mask_z = F.interpolate(mask_z[None].float(), scale_factor=1. / self.patch_size).to(torch.bool)[0] mask_z = mask_z.flatten(1).unsqueeze(-1) mask_x = F.interpolate(mask_x[None].float(), scale_factor=1. / self.patch_size).to(torch.bool)[0] mask_x = mask_x.flatten(1).unsqueeze(-1) mask_x = combine_tokens(mask_z, mask_x, mode=self.cat_mode) mask_x = mask_x.squeeze(-1) if self.add_cls_token: cls_tokens = self.cls_token.expand(B, -1, -1) cls_tokens = cls_tokens + self.cls_pos_embed if self.add_sep_seg: x += self.search_segment_pos_embed z += self.template_segment_pos_embed x = combine_tokens(z, x, mode=self.cat_mode) if self.add_cls_token: x = torch.cat([cls_tokens, x], dim=1) x = self.pos_drop(x) lens_z = self.pos_embed_z.shape[1] lens_x = self.pos_embed_x.shape[1] global_index_t = torch.linspace(0, lens_z - 1, lens_z).to(x.device) global_index_t = global_index_t.repeat(B, 1) global_index_s = torch.linspace(0, lens_x - 1, lens_x).to(x.device) global_index_s = global_index_s.repeat(B, 1) removed_indexes_s = [] for i, blk in enumerate(self.blocks): x, global_index_t, global_index_s, removed_index_s, attn = blk(x, global_index_t, global_index_s, mask_x, ce_template_mask, ce_keep_rate) if self.ce_loc is not None and i in self.ce_loc: removed_indexes_s.append(removed_index_s) x = self.norm(x) # # [bs, n_patch, dim] = [bs, 320, 768] 320 = 64 + 256 # # 分支2 处理流程 event_x = self.pos_embed_event(event_x) event_z = self.pos_embed_event(event_z) event_x += self.pos_embed_x event_z += self.pos_embed_z event_x = combine_tokens(event_z, event_x, mode=self.cat_mode) if self.add_cls_token: event_x = torch.cat([cls_tokens, event_x], dim=1) lens_z = self.pos_embed_z.shape[1] lens_x = self.pos_embed_x.shape[1] global_index_t1 = torch.linspace(0, lens_z - 1, lens_z).to(event_x.device) global_index_t1 = global_index_t1.repeat(B, 1) global_index_s1 = torch.linspace(0, lens_x - 1, lens_x).to(event_x.device) global_index_s1 = global_index_s1.repeat(B, 1) removed_indexes_s1 = [] for i, blk in enumerate(self.blocks): event_x, global_index_t1, global_index_s1, removed_index_s1, attn = blk(event_x, global_index_t1, global_index_s1, mask_x, ce_template_mask, ce_keep_rate) if self.ce_loc is not None and i in self.ce_loc: removed_indexes_s1.append(removed_index_s1) event_x = self.norm(event_x) # print('x.shape: ',x.shape) #x.shape: torch.Size([2, 320, 768]) # print('event_x.shape: ',event_x.shape) # event_x.shape: torch.Size([2, 320, 768]) x_cat = torch.cat([x, event_x], dim=1) # print('x_cat.shape: ',x_cat.shape) # x_cat.shape: torch.Size([2, 640, 768]) x = x_cat aux_dict = { "attn": attn, "removed_indexes_s": removed_indexes_s, # used for visualization } return x, aux_dict def forward(self, z, x, event_z, event_x, ce_template_mask=None, ce_keep_rate=None, tnc_keep_rate=None, return_last_attn=False): x, aux_dict = self.forward_features(z, x, event_z, event_x, ce_template_mask=ce_template_mask, ce_keep_rate=ce_keep_rate,) return x, aux_dict def _create_vision_transformer(pretrained=False, **kwargs): model = VisionTransformerCE(**kwargs) if pretrained: if 'npz' in pretrained: model.load_pretrained(pretrained, prefix='') else: checkpoint = torch.load(pretrained, map_location="cpu") missing_keys, unexpected_keys = model.load_state_dict(checkpoint["model"], strict=False) print('Load pretrained model from: ' + pretrained) return model def vit_base_patch16_224_ce(pretrained=False, **kwargs): """ ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929). """ model_kwargs = dict( patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs) model = _create_vision_transformer(pretrained=pretrained, **model_kwargs) return model def vit_large_patch16_224_ce(pretrained=False, **kwargs): """ ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929). """ model_kwargs = dict( patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs) model = _create_vision_transformer(pretrained=pretrained, **model_kwargs) return model 代码③ # 将 4输入分开，构建新的相同模态结合的2输入，2分支 import math import logging from functools import partial from collections import OrderedDict from copy import deepcopy import torch import torch.nn as nn import torch.nn.functional as F from timm.models.layers import to_2tuple from lib.models.layers.patch_embed import PatchEmbed, PatchEmbed_event, xcorr_depthwise from .utils import combine_tokens, recover_tokens from .vit import VisionTransformer from ..layers.attn_blocks import CEBlock from .new_counter_guide import Counter_Guide _logger = logging.getLogger(__name__) class VisionTransformerCE(VisionTransformer): """ Vision Transformer with candidate elimination (CE) module A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` - https://arxiv.org/abs/2010.11929 Includes distillation token & head support for `DeiT: Data-efficient Image Transformers` - https://arxiv.org/abs/2012.12877 """ def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True, representation_size=None, distilled=False, drop_rate=0., attn_drop_rate=0., drop_path_rate=0., embed_layer=PatchEmbed, norm_layer=None, act_layer=None, weight_init='', ce_loc=None, ce_keep_ratio=None): super().__init__() if isinstance(img_size, tuple): self.img_size = img_size else: self.img_size = to_2tuple(img_size) self.patch_size = patch_size self.in_chans = in_chans self.num_classes = num_classes self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models self.num_tokens = 2 if distilled else 1 norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) act_layer = act_layer or nn.GELU self.patch_embed = embed_layer( img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) num_patches = self.patch_embed.num_patches self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) self.dist_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if distilled else None self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim)) self.pos_drop = nn.Dropout(p=drop_rate) self.pos_embed_event = PatchEmbed_event(in_chans=32, embed_dim=768, kernel_size=4, stride=4) dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule blocks = [] ce_index = 0 self.ce_loc = ce_loc for i in range(depth): ce_keep_ratio_i = 1.0 if ce_loc is not None and i in ce_loc: ce_keep_ratio_i = ce_keep_ratio[ce_index] ce_index += 1 blocks.append( CEBlock( dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer, keep_ratio_search=ce_keep_ratio_i) ) self.blocks = nn.Sequential(*blocks) self.norm = norm_layer(embed_dim) self.init_weights(weight_init) # 添加交互模块counter_guide self.counter_guide = Counter_Guide(768, 768) def forward_features(self, z, x, event_z, event_x, mask_z=None, mask_x=None, ce_template_mask=None, ce_keep_rate=None, return_last_attn=False ): # 分支1 处理流程 B, H, W = x.shape[0], x.shape[2], x.shape[3] x = self.patch_embed(x) z = self.patch_embed(z) z += self.pos_embed_z x += self.pos_embed_x if mask_z is not None and mask_x is not None: mask_z = F.interpolate(mask_z[None].float(), scale_factor=1. / self.patch_size).to(torch.bool)[0] mask_z = mask_z.flatten(1).unsqueeze(-1) mask_x = F.interpolate(mask_x[None].float(), scale_factor=1. / self.patch_size).to(torch.bool)[0] mask_x = mask_x.flatten(1).unsqueeze(-1) mask_x = combine_tokens(mask_z, mask_x, mode=self.cat_mode) mask_x = mask_x.squeeze(-1) if self.add_cls_token: cls_tokens = self.cls_token.expand(B, -1, -1) cls_tokens = cls_tokens + self.cls_pos_embed if self.add_sep_seg: x += self.search_segment_pos_embed z += self.template_segment_pos_embed x = combine_tokens(z, x, mode=self.cat_mode) if self.add_cls_token: x = torch.cat([cls_tokens, x], dim=1) x = self.pos_drop(x) lens_z = self.pos_embed_z.shape[1] lens_x = self.pos_embed_x.shape[1] global_index_t = torch.linspace(0, lens_z - 1, lens_z).to(x.device) global_index_t = global_index_t.repeat(B, 1) global_index_s = torch.linspace(0, lens_x - 1, lens_x).to(x.device) global_index_s = global_index_s.repeat(B, 1) removed_indexes_s = [] # # 分支2 处理流程 event_x = self.pos_embed_event(event_x) event_z = self.pos_embed_event(event_z) event_x += self.pos_embed_x event_z += self.pos_embed_z event_x = combine_tokens(event_z, event_x, mode=self.cat_mode) if self.add_cls_token: event_x = torch.cat([cls_tokens, event_x], dim=1) lens_z = self.pos_embed_z.shape[1] lens_x = self.pos_embed_x.shape[1] global_index_t1 = torch.linspace(0, lens_z - 1, lens_z).to(event_x.device) global_index_t1 = global_index_t1.repeat(B, 1) global_index_s1 = torch.linspace(0, lens_x - 1, lens_x).to(event_x.device) global_index_s1 = global_index_s1.repeat(B, 1) removed_indexes_s1 = [] for i, blk in enumerate(self.blocks): # 第一个分支处理 x, global_index_t, global_index_s, removed_index_s, attn = blk(x, global_index_t, global_index_s, mask_x, ce_template_mask, ce_keep_rate) # 第二个分支处理 event_x, global_index_t1, global_index_s1, removed_index_s1, attn = blk(event_x, global_index_t1, global_index_s1, mask_x, ce_template_mask, ce_keep_rate) if self.ce_loc is not None and i in self.ce_loc: removed_indexes_s.append(removed_index_s) removed_indexes_s1.append(removed_index_s1) # 在第1层和第6层增加counter_guide模块 if i in [0,5]: enhanced_x, enhanced_event_x = self.counter_guide(x, event_x) # 将增强后的特征与原特征相加 x = x + enhanced_x event_x = event_x + enhanced_event_x # 应用LayerNorm归一化处理 x = self.norm(x) event_x = self.norm(event_x) x_cat = torch.cat([x,event_x], dim=1) x = x_cat aux_dict = { "attn": attn, "removed_indexes_s": removed_indexes_s, # used for visualization } return x, aux_dict def forward(self, z, x, event_z, event_x, ce_template_mask=None, ce_keep_rate=None, tnc_keep_rate=None, return_last_attn=False): x, aux_dict = self.forward_features(z, x, event_z, event_x, ce_template_mask=ce_template_mask, ce_keep_rate=ce_keep_rate,) return x, aux_dict def _create_vision_transformer(pretrained=False, **kwargs): model = VisionTransformerCE(**kwargs) if pretrained: if 'npz' in pretrained: model.load_pretrained(pretrained, prefix='') else: checkpoint = torch.load(pretrained, map_location="cpu") missing_keys, unexpected_keys = model.load_state_dict(checkpoint["model"], strict=False) print('Load pretrained model from: ' + pretrained) return model def vit_base_patch16_224_ce(pretrained=False, **kwargs): """ ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929). """ model_kwargs = dict( patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs) model = _create_vision_transformer(pretrained=pretrained, **model_kwargs) return model def vit_large_patch16_224_ce(pretrained=False, **kwargs): """ ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929). """ model_kwargs = dict( patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs) model = _create_vision_transformer(pretrained=pretrained, **model_kwargs) return model 中 代码① SR分数最高，代码②中，SR、PR、NPR分数较①降低，代码③中SR分数较②高，但比①低，代码③中PR和NPR最高
answer:您的观察结果表明，不同模型架构（代码①、代码②、代码③）对于性能指标（SR、PR、NPR分数）有不同的影响。这些差异揭示了各个模型在任务特定细节处理能力、泛化能力以及对错分样本的敏感度方面的优劣。下面是对每个配置下模型表现的分析与可能的原因： # 代码①：VisionTransformerCE (原始模型) - **SR分数最高**：表示这个模型架构能最好地完成您的任务，可能是因为它的注意力机制能有效地捕捉和利用图像特征，从而在整体任务中表现出色。 - **PR和NPR**：虽然可能没有代码③高，但较高的SR分数意味着模型在整体上更加准确和可靠。 # 代码②：简化的VisionTransformerCE (无Cross_Attention和Counter_Guide) - **SR、PR、NPR分数较①降低**：简化模型可能过于简单，无法充分捕获和处理详细的模态间交互信息，导致其综合性能有所下降。减少了关于任务关键信息的提取与利用能力，特别是缺少了有效的交叉注意力模块，可能导致模型在捕捉细节方面不如原始模型。 # 代码③：引入新的Counter_Guide (包含Cross_Attention) - **SR分数较②高，但比①低**：这表明引入的新Counter_Guide提升了模型对模态间交互信息的处理能力，使其性能好于简化版，但还没有达到原始模型的水平。可能是因为Counter_Guide增强了模态间的信息交互，但可能还没有完全优化，或者降维和过拟合控制还有待调整。 - **PR和NPR最高**：说明新的模型架构在分类的准确性和负样本的识别能力上做出了改善，这可能归功于改进的模态间交互和特征整合策略，但这并不一定直接转化为整体任务（如目标跟踪或对象识别等）的成功率提升。 # 总结 - **模型优化**：代码③展示了通过引入新的架构对模型性能的改进潜力，尤其是在PR和NPR方面。针对SR分数较低的问题，需要进一步优化Counter_Guide模块，考虑更细致的特征处理、更有效的信息融合方法或适度的模型简化。 - **模型泛化**：为了继续提升SR分数，可能需要更多地关注模型的泛化能力，包括采用更多的数据增强技术、正则化方法以及在训练过程中更精细的监控过拟合情况。 不同模型架构之间的这种差异强调了在设计与优化多模态或复杂模型时，细节的重要性。在选择最终模型时，应基于特定任务的需求和综合性能查找最佳平衡点。