Respan Dataset Explorer

总结流程：import math
import os
from typing import List
import torch
from torch import nn
from torch.nn.modules.transformer import _get_clones
from lib.models.layers.head import build_box_head
from lib.models.ceutrack.vit import vit_base_patch16_224
from lib.models.ceutrack.vit_ce import vit_large_patch16_224_ce, vit_base_patch16_224_ce
from lib.utils.box_ops import box_xyxy_to_cxcywh

class CEUTrack(nn.Module):
    """ This is the base class for hdetrack """

    def __init__(self, transformer, box_head, aux_loss=False, head_type="CORNER"):
        """ Initializes the model.
        Parameters:
            transformer: torch module of the transformer architecture.
            aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used.
        """
        super().__init__()
        self.backbone = transformer
        self.box_head = box_head
        
        self.aux_loss = aux_loss
        self.head_type = head_type
        if head_type == "CORNER" or head_type == "CENTER":
            self.feat_sz_s = int(box_head.feat_sz)
            self.feat_len_s = int(box_head.feat_sz ** 2)

        if self.aux_loss:
            self.box_head = _get_clones(self.box_head, 6)
    
    def forward(self, 
                event_template_img: torch.Tensor,                # torch.Size([bs, 3, 128, 128])
                event_search_img: torch.Tensor,                  # torch.Size([bs, 3, 256, 256])
                # event_template: torch.Tensor,                  # torch.Size([bs, 1, 19, 1024])
                # event_search: torch.Tensor,                    # torch.Size([bs, 1, 19, 4096])
                x_template_img: torch.Tensor, 
                x_search_img: torch.Tensor,
                ce_template_mask=None,
                ce_keep_rate=None,
                return_last_attn=False,
                ):

        # before feeding into backbone, we need to concat four vectors, or two two concat
        x, attn = self.backbone(z=x_template_img, x=x_search_img,
                                    ce_template_mask=ce_template_mask,
                                    ce_keep_rate=ce_keep_rate,
                                    return_last_attn=return_last_attn)
        event_x,event_attn = self.backbone(z=event_template_img, x=event_search_img,
                                    ce_template_mask=ce_template_mask,
                                    ce_keep_rate=ce_keep_rate,
                                    return_last_attn=return_last_attn)
        
        
        # Forward head
        feat_last = x
        if isinstance(x, list):
            feat_last = x[-1]
        out_x = self.forward_head(feat_last,attn,None)

        # 新增Forward头
        event_feaat_last = event_x
        if isinstance(event_x,list):
            event_feat_last = event_x[-1]
        out_event = self.forward_head(event_feaat_last,event_attn, None)
        
        return out_x,out_event # 双分支输出头

    def forward_head(self, cat_feature, cat_attn, gt_score_map=None):
        """
        cat_feature: output embeddings of the backbone, it can be (HW1+HW2, B, C) or (HW2, B, C)
        """
        # 首先执行cat_feature[:, -self.feat_len_s:]，提取event_search,这里是event还是RGB模态，取决于输入的cat_feature

        cat_search = cat_feature[:, -self.feat_len_s:]       # [bs, 256, 768]  cat_search 这里的cat_search仅仅表示是search模态的特征,与模态无关
        opt = (cat_search.unsqueeze(-1)).permute((0, 3, 2, 1)).contiguous()  # cat_search.unsqueeze(-1) 表示增加一个维度：原始维度是[bs, 256, 768]，增加一个维度后变成[bs, 256, 768, 1] 然后permute((0, 3, 2, 1)) 表示将维度调整为[bs, 1, 768, 256]
        bs, Nq, C, HW = opt.size() # 取出opt的size，分别是bs, Nq, C, HW，分别表示batch size, query number, channel, height*width
        opt_feat = opt.view(-1, C, self.feat_sz_s, self.feat_sz_s) # 将opt的维度调整为[-1, C, self.feat_sz_s, self.feat_sz_s]，这里的self.feat_sz_s是256，表示特征图的大小

        if self.head_type == "CORNER":
            # run the corner head
            pred_box, score_map = self.box_head(opt_feat, True)
            outputs_coord = box_xyxy_to_cxcywh(pred_box)
            outputs_coord_new = outputs_coord.view(bs, Nq, 4)
            out = {'pred_boxes': outputs_coord_new,
                   'score_map': score_map,
                   }
            return out
        # head_type == "CENTER"
        elif self.head_type == "CENTER":
            # run the center head
            score_map_ctr, bbox, size_map, offset_map = self.box_head(opt_feat, gt_score_map)
            # outputs_coord = box_xyxy_to_cxcywh(bbox)
            outputs_coord = bbox
            outputs_coord_new = outputs_coord.view(bs, Nq, 4)
            out = {
                   'pred_boxes': outputs_coord_new,
                   'score_map': score_map_ctr,
                   'size_map': size_map,
                   'offset_map': offset_map,
                   'cat_attn': cat_attn
                   }
            return out
        else:
            raise NotImplementedError

def build_ceutrack(cfg, training=True):
    current_dir = os.path.dirname(os.path.abspath(__file__))  # This is your Project Root
    pretrained_path = os.path.join(current_dir, 'pretrained_models')
    if cfg.MODEL.PRETRAIN_FILE and ('CEUTrack' not in cfg.MODEL.PRETRAIN_FILE) and training:
        pretrained = os.path.join(pretrained_path, cfg.MODEL.PRETRAIN_FILE)
    else:
        pretrained = ''
        
    if cfg.MODEL.BACKBONE.TYPE == 'vit_base_patch16_224':
        backbone_s = vit_base_patch16_224(pretrained, drop_path_rate=cfg.TRAIN.DROP_PATH_RATE)
        hidden_dim = backbone_s.embed_dim
        patch_start_index = 1

    elif cfg.MODEL.BACKBONE.TYPE == 'vit_base_patch16_224_ce':
        backbone = vit_base_patch16_224_ce(pretrained, drop_path_rate=cfg.TRAIN.DROP_PATH_RATE,
                                           ce_loc=cfg.MODEL.BACKBONE.CE_LOC,
                                           ce_keep_ratio=cfg.MODEL.BACKBONE.CE_KEEP_RATIO,
                                           )
        # hidden_dim = backbone_s.embed_dim*2
        hidden_dim = backbone.embed_dim
        patch_start_index = 1

    elif cfg.MODEL.BACKBONE.TYPE == 'vit_large_patch16_224_ce':
        backbone = vit_large_patch16_224_ce(pretrained, drop_path_rate=cfg.TRAIN.DROP_PATH_RATE,
                                            ce_loc=cfg.MODEL.BACKBONE.CE_LOC,
                                            ce_keep_ratio=cfg.MODEL.BACKBONE.CE_KEEP_RATIO,
                                            )
        hidden_dim = backbone.embed_dim
        patch_start_index = 1
    else:
        raise NotImplementedError

    backbone.finetune_track(cfg=cfg, patch_start_index=patch_start_index)
    box_head = build_box_head(cfg, hidden_dim)

    model = CEUTrack(
        backbone,
        box_head,
        aux_loss=False,
        head_type=cfg.MODEL.HEAD.TYPE,
    )

    return model

    if 'CEUTrack' in cfg.MODEL.PRETRAIN_FILE and training:
        checkpoint = torch.load(cfg.MODEL.PRETRAIN_FILE, map_location="cpu")
        missing_keys, unexpected_keys = model.load_state_dict(checkpoint["net"], strict=False)
        print('Load pretrained model from: ' + cfg.MODEL.PRETRAIN_FILE)

    return model

这段代码实现了一个名为 `CEUTrack` 的多模态追踪模型，它基于Vision Transformer（ViT）架构，并且为处理多模态数据（比如标准图像数据和事件相机数据）提供了支持。以下是这段代码的详细流程解析：

### 模型架构

1. **模型初始化**：`CEUTrack` 类在初始化时接收Transformer模型（如ViT）作为其主干网络（backbone），同时接受一个用于预测边界框的头部网络（box_head）。此外，还可以指定是否使用辅助损失（aux_loss）和头部网络的类型（head_type，比如“CORNER”或“CENTER”）。

2. **前向传播**：模型通过 `forward` 方法支持同时处理两对输入图像：标准RGB图像（`x_template_img` 和 `x_search_img`）与事件相机图像（`event_template_img` 和 `event_search_img`）。这意味着模型是为了能同时考虑来自不同传感器的信息，并处理这些不同类型的数据。

    - 首先，这两对图像分别通过相同的Transformer主干网络（backbone）进行特征提取，生成相应的特征 `x` 和 `event_x`，以及各自的注意力权重 `attn` 和 `event_attn`。
    
    - 然后，使用单独定义的 `forward_head` 方法，基于提取的特征和注意力权重来生成最终的输出。这个过程分别对两种模态的数据执行，分别得到RGB图像和事件相机图像的预测输出。

### 模型细节

- `forward_head` 方法负责将特征映射到具体的预测目标上（例如边界框的预测）。根据头部的类型（例如，CORNER或CENTER），这个过程可能包括将特征张量重新排列为一个或多个输出图，然后通过box head进行进一步的处理来预测边界框。

- `build_ceutrack` 函数负责构建并配置 `CEUTrack` 模型实例。它支持加载预训练的ViT模型作为主干网络，并根据配置参数初始化box头部网络。

### 关键概念

- **多模态处理**：这个实现强调了对来自不同传感器源的数据进行同步处理的能力。这对于需要综合多种类型信息的任务非常重要，比如在真实环境中的对象追踪，其中RGB图像和事件相机数据可以提供互补的信息。

- **模块化设计**：`CEUTrack` 将特征提取和预测任务解耦成独立模块（分别是主干网络和头部网络），这提高了模型的灵活性和扩展性。

- **迁移学习**：通过在 `build_ceutrack` 函数中指定不同的预训练模型和参数，易于在不同条件和任务上适用该模型架构。

总体而言，`CEUTrack` 作为一个模型实例展示了现代深度学习中常用的一些设计模式，包括对多模态输入的处理、基于Transformer的特征提取，以及模块化设计来适配不同的预测任务。

那么在这段代码中，包含几个分支？输出是几个分支？得到几个响应？

在提供的代码段中，`CEUTrack` 模型包含了两个主要的处理分支，分别用于处理两对不同类型的输入数据：一对是标准RGB图像（由 `x_template_img` 和 `x_search_img` 表示），另一对是事件相机数据（由 `event_template_img` 和 `event_search_img` 表示）。因此，这构成了两个独立但结构相似的数据处理流程或分支。

### 输出分支：

1. **RGB图像处理分支**：这个分支处理标准RGB图像对，提取其特征和注意力信息，最终通过 `forward_head` 方法得到RGB图像对应的输出。这个输出包括边界框预测和可能的其他与任务相关的预测。

2. **事件相机数据处理分支**：这个分支以相同的方式处理事件相机数据，提取特征和注意力信息，并同样通过 `forward_head` 方法生成对应的输出结果，包括边界框和其他可能的预测。

### 响应数量：

因为这两个分支都独立地生成各自的输出，模型最终会得到**两组响应**（输出），每组响应对应于处理的一对输入数据。更具体地说：

- 来自 **RGB图像处理分支** 的输出，可能包含物体的边界框位置、得分映射等，并将这些信息封装在输出对象或字典中（由 `out_x` 表示）。
  
- 来自 **事件相机数据处理分支** 的输出，同样包含物体的边界框位置、得分映射等，对应于事件相机数据的处理结果，并封装在另一个输出对象或字典中（由 `out_event` 表示）。

因此，在这个模型的结构和实现中，它是为了并行处理两种不同类型的输入数据而设计的，每种类型的数据都通过自己的分支进行处理，并最终生成各自的输出响应。这种设计允许模型在同一预测框架中整合和利用来自不同数据源的信息，从而提高对特定任务（如目标跟踪或检测）的整体性能。

相较于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  ，我们的模型：# 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_S(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 = x.shape[0]
        x = self.patch_embed(x)
        z = self.patch_embed(z)
        x += self.pos_embed_x
        z += self.pos_embed_z
        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_combined = torch.cat([z, x], dim=1)    # student  64+256
        if self.add_cls_token:
            x_combined = torch.cat([cls_tokens, x_combined], dim=1)
        x_combined = self.pos_drop(x_combined)
        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_combined, global_index_t, global_index_s, removed_index_s, attn = blk(x_combined, global_index_t, global_index_s, mask_x, ce_template_mask, ce_keep_rate)
            
            if self.ce_loc is not None and i in self.ce_loc:
                removed_indexes_s.append(removed_index_s)
        x_combined = self.norm(x_combined)
        x = x_combined  # [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
        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:
            event_x += self.search_segment_pos_embed
            event_z += self.template_segment_pos_embed
        event_combined = torch.cat([event_z, event_x], dim=1)    # student  64+256
        if self.add_cls_token:
            event_combined = torch.cat([cls_tokens, event_combined], dim=1)
        event_combined = self.pos_drop(event_combined)
        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_combined, global_index_t1, global_index_s1, removed_index_s1, event_attn = blk(event_combined, global_index_t1, global_index_s1, mask_x, ce_template_mask, ce_keep_rate)
            if self.ce_loc is not None and i in self.ce_loc:
                removed_indexes_s1.append(removed_index_s1)
        event_combined = self.norm(event_combined)
        event_x = event_combined

        return x, event_x, attn, event_attn

    def forward(self,z,x,event_z,event_x,
                ce_template_mask=None, 
                ce_keep_rate=None,
                return_last_attn=False):
        
        x, event_x, attn, event_attn = self.forward_features(z, x,event_z,event_x,
                                                             ce_template_mask, ce_keep_rate,
                                                             return_last_attn)
        return x, event_x, attn, event_attn


def _create_vision_transformer(pretrained=False, **kwargs):
    model = VisionTransformerCE_S(**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_s(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_s(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 有什么优势和劣势，或者我们的修改模型需要一个合理的动机

对比原始的Vision Transformer (ViT) CE模型和修改后的Vision Transformer CE_S模型，可以概括各自的优势、劣势，并从中得出修改模型的动机。

### 原始ViT CE模型的优势和劣势：

**优势**:
1. **多模态处理能力**：原始模型设计用于处理同时包含标准RGB图像和事件相机数据的输入，这增加了模型处理不同数据源的灵活性和适用性。
2. **数据融合**：通过将不同模态的输入合并为一个统一的特征表示，模型在概念上能够更好地融合来自不同传感器的信息，可能提高对复杂环境的理解。
3. **候选消除（CE）**：利用CE模块可以在保持模型性能的同时减少计算量，提高模型效率。

**劣势**:
1. **复杂性较高**：处理多模态数据并执行候选消除增加了模型的复杂性，对于理解、实现和调优都带来了挑战。
2. **计算成本**：尽管有CE模块的优化，但同时处理两种类型的数据仍可能导致较高的计算成本。
3. **特定情形下的适用性有限**：如果某些任务或场景中只能获得一种类型的输入数据，那么原始模型中用于处理另一种模态的部分则可能变得冗余。

### 修改后ViT CE_S模型的优势和劣势：

**优势**:
1. **特定模态焦点**：专注于优化和改进对单一数据模态（像是仅RGB图像或事件相机数据）的处理能力，使得模型在这个领域可能具有更优的性能。
2. **简化模型结构**：减少了对第二模态数据处理的逻辑，可以降低模型复杂性，简化训练和推理过程。
3. **计算效率**：专注于单一数据源的处理，缩小模型规模和计算量，有助于提高计算效率和加速模型推理。

**劣势**:
1. **信息利用有限**：忽略了利用额外模态数据（如事件相机数据）带来的互补信息，可能在一定程度上限制了模型处理复杂场景的能力。
2. **适用性窄**：修改后的模型专注于处理特定类型的输入数据，其适用性相比原有设计更为有限。

### 修改动机：

考虑到上述因素，修改模型的合理动机可能包括：

- **针对应用专优**：如果目标应用场景中只有单一类型的数据源可用，或者已知某一数据模态明显比另外的数据模态更为重要和有效，那么专注优化那一模态的处理能力便是合理的策略。
- **资源限制考虑**：在计算资源有限的环境下，为了提高模型的部署效率和实时性能，简化模型并减少其对计算资源的需求是一种重要的修改动机。
- **研究目的**：为了探索单一模态数据（如仅RGB图像或事件数据）在特定任务上的潜力和局限性，专门设计和优化针对该模态的模型有其研究价值。

总之，修改模型的设计应基于具体应用场景的需求、计算资源的可用性以及研究目标的明确，以确保模型修改后能更好地满足特定的需求。

我们将不同模态的数据单独处理，仅仅是第一步，接着我们将增加不同模态之间的信息交互，那么这样的设计较原始vit_ce的优势和劣势是什么？

将不同模态数据单独处理并在后续步骤中增加模态间的信息交互，是一种在保持模态特异性处理的同时促进模态间融合的策略。这种设计与原始Vision Transformer (ViT) CE模型相比，具有其独特的优势和劣势：

### 优势

1. **专注的模态特定处理**：通过单独处理每种模态的数据，可以针对每种数据的特有特征和性质设计最合适的处理流程，从而提高模型对于每种模态信息的把握能力。

2. **灵活的模态融合**：在模态特定处理之后引入信息交互步骤，使得模型在保留了对单一模态深入理解的同时，还能灵活地探索和利用模态间的互补信息。这样的融合策略可能更加有针对性，从而提高最终任务的性能。

3. **可解释性提升**：分阶段处理和融合不同模态数据可能增强模型的可解释性，因为模型的每一部分都有明确的职责，如模态特定特征提取、模态间的信息交换等。

4. **调优灵活性**：这种设计提供了额外的调优点，比如在模态特定处理和模态间交互之间的平衡，可以根据不同任务的需求进行更细致的优化。

### 劣势

1. **设计和实现复杂度增加**：相比直接处理所有输入数据，分步骤处理不同模态及其间的信息交互增加了模型的设计和实现复杂度。

2. **训练难度可能增加**：由于引入了模态间信息交互机制，可能需要更精细的训练策略来确保这些交互能够有效发挥作用，这可能增加训练难度。

3. **计算开销增加**：分别处理每种模态数据之后再进行信息交互，可能会增加一些计算开销，特别是当模态间交互机制较为复杂时。

4. **模态间依赖性风险**：过度依赖模态间的信息交互可能导致在某些模态数据缺失或质量较差时，模型性能大幅下降。

### 设计改进的动机

采取这样的策略设计，反映了试图在维持模态独立性处理的基础上，探索模态间的互补性，以实现更全面的任务理解和性能提升的动机。它基于的假设是，通过首先提取每种模态的深层特征，然后在一个或多个后续步骤中促进这些特征的融合，可以更有效地利用多模态数据的潜力。这样的设计思路有望在多模态学习任务中，如视觉问答（VQA）和机器人感知等复杂场景下，提供更优的性能。

那么就先分析一下：原始的vit_ce中4个输入经过backbone得到一个统一的features，我们将2种模态分为2个分支，执行相同的vit模型

将原有的Vision Transformer with Candidate Elimination (ViT-CE) 模型，从处理四个融合输入到两个独立分支处理不同模态的转变，代表了一种从集中式处理到模块化、分布式处理的策略。以下是这种改变可能带来的影响和分析：

### 原始ViT-CE的处理方式：

在原始的ViT-CE模型中，四个输入（假设为两种模态的一对模板图像和搜索图像）被合并或以某种方式融合后通过一个统一的Transformer backbone进行特征提取。这种方式致力于直接在原始数据级别利用这些不同模态的合并特征，从而进行下游任务的处理。

### 改变为两个独立分支：

在修改后的结构中，两种模态的数据分别通过两个相同的ViT模型（或分支）进行处理，每个分支独立地处理一种类型的模态数据。

### 分析：

**优势**

1. **模态特定的处理**：每个分支可以专注于对特定模态的优化处理，允许模型更深入地学习每种模态的独特特性和模式。

2. **灵活度与可扩展性**：这种分支式架构的设计提供了更高的灵活度和可扩展性。例如，可以针对特定模态引入更适合的前处理步骤或特定的网络架构改进。

3. **并行处理**：两个分支可以并行处理，提升计算效率，尤其是在多GPU环境下。

4. **误差隔离**：当某一模态的数据质量较低或在特定情况下无法获取时，该模态的处理分支可能对整体性能影响较小，这有助于改善模型的鲁棒性。

**劣势**

1. **模态间互补信息的初期缺失**：早期阶段缺乏不同模态之间的信息交互，可能未能充分利用模态间的互补性，这可能在某种程度上限制了模型初期特征提取的表现。

2. **增加了融合设计的复杂性**：虽然分支独立处理简化了模态内的处理，但增加了后续如何有效整合和利用这些独立特征的设计难度和复杂性。

3. **资源消耗**：两个独立分支处理相同的ViT模型可能会比单一融合输入的模型消耗更多计算资源。

### 结论

采取将不同模态数据分为两个独立分支的设计，引入了在特定模态处理优化与模态间信息交互平衡之间的新挑战。随后的模型设计需要着重考虑如何有效地在这些独立处理的特征上实施交互机制，以确保模型能够充分利用不同模态间的互补信息，从而实现更好的总体性能。这种设计策略的实际表现将高度依赖于特定任务的需求以及如何实现高效而有意义的模态间信息融合。

                            As a prompt generator for a generative AI called "Midjourney", you will create image prompts for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image.
                            
                            Please adhere to the structure and formatting below, and follow these guidelines:
                            
                            Do not use the words "description" or ":" in any form.
                            Do not place a comma between [ar] and [v].
                            Write each prompt in one line without using return.
                            Structure:
                            [1] = 图片1: 帮我画一个封面图 从中间分开里面有两对情侣，左边是一对亚洲情侣，男生牵着女生在电梯步梯上，女生手上有鲜花，和非常多的礼物；右边是一对亚洲情侣，女生牵着男生在电梯步梯，男生手里有很多电子产品。逼真形象，背景丰富，远景，3:4
                            [2] = a detailed description of [1] with specific imagery details.
                            [3] = a detailed description of the scene's environment.
                            [4] = a detailed description of the compositions.
                            [5] = a detailed description of the scene's mood, feelings, and atmosphere.
                            [6] = A style (e.g. photography, painting, illustration, sculpture, artwork, paperwork, 3D, etc.) for [1].
                            [7] =  a detailed description of the scene's mood, feelings, and atmosphere.
                            [ar] = Use "--ar 16:9" for horizontal images, "--ar 9:16" for vertical images, or "--ar 1:1" for square images.
                            [v] = Use "--niji" for Japanese art style, or "--v 5" for other styles.
                            
                            
                            Formatting:
                            Follow this prompt structure: "/imagine prompt: [1], [2], [3], [4], [5], [6], [7], [ar] [v]".
                            
                            Your task: Create 4 distinct prompts for each concept [1], varying in details description, environment,compositions,atmosphere, and realization.
                            
                            Write your prompts in english.
                            Do not describe unreal concepts as "real" or "photographic".
                            Include one realistic photographic style prompt with lens type and size.
                            Separate different prompts with two new lines.
                            Example Prompts:
                            
                            /imagine prompt: cute dog, fluffy fur, wagging tail, playful expression, sitting on a grassy field, under a clear blue sky, with a colorful collar, in a natural and vibrant setting, by a lake, captured with a Nikon D750 camera, 50mm lens, shallow depth of field, composition focused on the dog's face, capturing its joyful spirit, in a style reminiscent of William Wegman's iconic dog portraits. --ar 1:1 --v 5.2
                            /imagine prompt: beautiful women in the coffee shop, elegant and sophisticated, sipping a cup of steaming coffee, natural sunlight streaming through the window, soft and warm color tones, vintage decor with cozy armchairs and wooden tables, a bookshelf filled with classic novels, delicate porcelain teacups, a hint of aromatic coffee beans in the air, captured by a Leica M10 camera, 35mm lens, capturing the essence of timeless beauty, composition focused on the woman's face and hands, reminiscent of a painting by Leonardo da Vinci. --ar 1:1 --v 5.2
                            /imagine prompt: A captivating Halo Reach landscape with a Spartan amidst a battlefield, fallen enemies around, smoke and fire in the background, emphasizing the Spartan's determination and bravery, detailed environment blending chaos and beauty, Illustration, digital art, --ar 16:9 --v 5
                                                        

/imagine prompt: Illustrate a cover image split in the middle portraying two couples; on the left, an Asian couple with the man leading the woman up an escalator, the woman holding flowers and numerous gifts, on the right, another Asian couple with the woman leading the man up the escalator, the man holding various electronic gadgets. The image should be realistic with a rich background, distant view, in a 3:4 aspect ratio. --ar 3:4 --v 5

/imagine prompt: Create an image showcasing two sets of Asian couples on an escalator; the left couple consists of a man guiding a woman with flowers and gifts, while the right couple depicts the woman leading a man with electronic gadgets. The scene should have a detailed background, a distant perspective, and a 3:4 aspect ratio. --ar 3:4 --v 5

/imagine prompt: Visualize a split cover image depicting two pairs of Asian couples on an escalator; the left couple shows a man guiding a woman holding flowers and gifts, while the right couple portrays a woman leading a man with electronic gadgets. The setting should be realistic with a detailed background, distant view, in a 3:4 aspect ratio. --ar 3:4 --v 5

/imagine prompt: Design an image with a split cover format featuring two Asian couples on an escalator; one pair shows a man leading a woman with flowers and gifts, the other depicts a woman leading a man with electronic gadgets. The image should be realistic, with a rich background, distant perspective, and a 3:4 aspect ratio. --ar 3:4 --v 5

proposer moi les items d'examens d'état de l'informatique avec des réponses  pour les élèves de la commerciale gestion 

1. Expliquer la différence entre un système d'exploitation Windows et un système d'exploitation Linux.

Réponse: Un système d'exploitation Windows est un système d'exploitation propriétaire développé par Microsoft, tandis qu'un système d'exploitation Linux est un système d'exploitation libre et open source basé sur le noyau Linux.

2. Qu'est-ce qu'un navigateur web et donnez des exemples de navigateurs populaires.

Réponse: Un navigateur web est un logiciel utilisé pour accéder à des pages web sur Internet. Exemples de navigateurs populaires : Google Chrome, Mozilla Firefox, Safari, Microsoft Edge.

3. Qu'est-ce qu'un algorithme et pourquoi est-il important en informatique ?

Réponse: Un algorithme est une séquence d'instructions précises pour résoudre un problème ou effectuer une tâche. Il est important en informatique car il permet de structurer et d'optimiser les processus de traitement des données.

4. Quelle est la différence entre un logiciel de gestion de base de données et un tableur ?

Réponse: Un logiciel de gestion de base de données est utilisé pour stocker, organiser et manipuler des données structurées, tandis qu'un tableur est un outil permettant de manipuler des données de manière tabulaire.

5. Expliquer ce qu'est un réseau informatique et donner des exemples de types de réseaux.

Réponse: Un réseau informatique est un ensemble de dispositifs interconnectés permettant le partage de ressources et d'informations. Exemples de types de réseaux : LAN (réseau local), WAN (réseau étendu), WLAN (réseau local sans fil).

items d'informatique  avec des accessions pour les élèves de 6 iem commerciale et gestion 

1. Qu'est-ce qu'un ordinateur et quelles sont ses principales composantes ?

Réponse: Un ordinateur est une machine électronique capable d'exécuter des instructions pour traiter des données. Ses principales composantes sont le processeur, la mémoire (RAM), le disque dur, la carte mère et les périphériques d'entrée/sortie.

2. Quelle est la différence entre un logiciel et un matériel informatique ?

Réponse: Le matériel informatique désigne les composantes physiques d'un ordinateur, tandis que le logiciel est un programme informatique qui permet d'exécuter des tâches spécifiques sur l'ordinateur.

3. Quels sont les principaux systèmes d'exploitation utilisés sur les ordinateurs et donnez un exemple de chacun ?

Réponse: Les principaux systèmes d'exploitation sont Windows (exemple: Windows 10), macOS (exemple: macOS Mojave) et Linux (exemple: Ubuntu).

4. Qu'est-ce qu'un navigateur web et comment utilise-t-on un moteur de recherche ?

Réponse: Un navigateur web est un logiciel permettant d'accéder à des pages web sur Internet. Pour utiliser un moteur de recherche, il suffit de saisir des mots-clés dans la barre de recherche du moteur pour obtenir des résultats pertinents.

5. Expliquer ce qu'est un virus informatique et comment se protéger contre les menaces informatiques.

Réponse: Un virus informatique est un programme malveillant conçu pour endommager ou voler des données sur un ordinateur. Pour se protéger contre les menaces informatiques, il est important d'installer un antivirus, de ne pas ouvrir des pièces jointes suspectes et de ne pas télécharger des logiciels provenant de sources non fiables.

items informatique de la rdc pour les eleves de 6 iem humanites commerciale de2020,2021, 2022,2023,

Voici quelques thèmes d'informatique spécifiquement pour les élèves de 6ème humanités commerciale en RDC pour les années 2020, 2021, 2022 et 2023:

1. Les principaux composants d'un ordinateur et leur rôle.
2. Les différents types de logiciels et leurs utilisations.
3. Les principaux systèmes d'exploitation utilisés sur les ordinateurs.
4. Les bases de la sécurité informatique et les bonnes pratiques pour se protéger contre les cyberattaques.
5. Les différentes étapes de la création d'un site web basique.
6. La distinction entre un réseau local et un réseau étendu.
7. Les principes de base de la programmation et les langages de programmation populaires.
8. L'importance de la sauvegarde des données et les différents moyens de sauvegarde disponibles.
9. Les fonctions principales d'un navigateur web et la navigation sur Internet.
10. L'impact des nouvelles technologies sur le monde du travail et de l'éducation.

Ces thèmes peuvent servir de base pour l'enseignement de l'informatique aux élèves de 6ème humanités commerciale en RDC dans les années 2020, 2021, 2022 et 2023.

                            As a prompt generator for a generative AI called "Midjourney", you will create image prompts for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image.
                            
                            Please adhere to the structure and formatting below, and follow these guidelines:
                            
                            Do not use the words "description" or ":" in any form.
                            Do not place a comma between [ar] and [v].
                            Write each prompt in one line without using return.
                            Structure:
                            [1] = 写实风格的2024年中国成都市街头，未来科技与传统文化融合的景观
                            [2] = a detailed description of [1] with specific imagery details.
                            [3] = a detailed description of the scene's environment.
                            [4] = a detailed description of the compositions.
                            [5] = a detailed description of the scene's mood, feelings, and atmosphere.
                            [6] = A style (e.g. photography, painting, illustration, sculpture, artwork, paperwork, 3D, etc.) for [1].
                            [7] =  a detailed description of the scene's mood, feelings, and atmosphere.
                            [ar] = Use "--ar 16:9" for horizontal images, "--ar 9:16" for vertical images, or "--ar 1:1" for square images.
                            [v] = Use "--niji" for Japanese art style, or "--v 5" for other styles.
                            
                            
                            Formatting:
                            Follow this prompt structure: "/imagine prompt: [1], [2], [3], [4], [5], [6], [7], [ar] [v]".
                            
                            Your task: Create 4 distinct prompts for each concept [1], varying in details description, environment,compositions,atmosphere, and realization.
                            
                            Write your prompts in english.
                            Do not describe unreal concepts as "real" or "photographic".
                            Include one realistic photographic style prompt with lens type and size.
                            Separate different prompts with two new lines.
                            Example Prompts:
                            
                            /imagine prompt: cute dog, fluffy fur, wagging tail, playful expression, sitting on a grassy field, under a clear blue sky, with a colorful collar, in a natural and vibrant setting, by a lake, captured with a Nikon D750 camera, 50mm lens, shallow depth of field, composition focused on the dog's face, capturing its joyful spirit, in a style reminiscent of William Wegman's iconic dog portraits. --ar 1:1 --v 5.2
                            /imagine prompt: beautiful women in the coffee shop, elegant and sophisticated, sipping a cup of steaming coffee, natural sunlight streaming through the window, soft and warm color tones, vintage decor with cozy armchairs and wooden tables, a bookshelf filled with classic novels, delicate porcelain teacups, a hint of aromatic coffee beans in the air, captured by a Leica M10 camera, 35mm lens, capturing the essence of timeless beauty, composition focused on the woman's face and hands, reminiscent of a painting by Leonardo da Vinci. --ar 1:1 --v 5.2
                            /imagine prompt: A captivating Halo Reach landscape with a Spartan amidst a battlefield, fallen enemies around, smoke and fire in the background, emphasizing the Spartan's determination and bravery, detailed environment blending chaos and beauty, Illustration, digital art, --ar 16:9 --v 5
                                                        

/imagine prompt: Realistic style depiction of a futuristic Chengdu cityscape in 2024, blending advanced technology with traditional Chinese culture, bustling streets filled with neon lights and flying vehicles, towering skyscrapers juxtaposed with ancient temples, vibrant night market stalls selling a fusion of street food, surrounded by a misty mountain backdrop, captured with a Canon EOS R5 camera, 24-70mm lens, medium depth of field, composition highlighting the contrast between old and new, in a cyberpunk art style. --ar 16:9 --v 5

/imagine prompt: Streets of Chengdu in 2024 transformed into a colorful fusion of modern and traditional elements, traditional Sichuan architecture interspersed with sleek glass structures, people in futuristic attire mingling with locals in traditional clothing, street vendors selling high-tech gadgets alongside classic street foods, a harmonious blend of the past and future, captured in a vibrant and lively atmosphere, rendered in a digital painting style. --ar 16:9 --v 5

/imagine prompt: A realistic portrayal of a future Chengdu scene in 2024, with a mix of modern technology and traditional Chinese elements, teahouses with holographic tea ceremonies, streets lined with ancient trees and automated vehicles, a serene blend of old and new in a tranquil setting, captured with a Sony A7III camera, 35mm lens, wide depth of field, composition focusing on the harmony of elements, in a high-definition photography style. --ar 16:9

/imagine prompt: A vision of Chengdu city streets in 2024, a seamless integration of futuristic tech and ancient culture, bustling markets brimming with augmented reality displays and traditional crafts, a unique blend of innovation and heritage, captured in a vibrant and energetic atmosphere, depicted in a modern illustration style with bold colors and dynamic compositions. --ar 16:9 --v 5