更改import torch import torchvision.models as models import torch.nn as nn import torch.nn.functional as F class eca_Resnet50(nn.Module): def init(self): super().init() self.model = models.resnet50(pretrained=True) self.model.avgpool = nn.AdaptiveAvgPool2d((1,1)) self.model.fc = nn.Linear(2048, 1000) self.eca = ECA_Module(2048, 8) def forward(self, x): x = self.model.conv1(x) x = self.model.bn1(x) x = self.model.relu(x) x = self.model.maxpool(x) x = self.model.layer1(x) x = self.model.layer2(x) x = self.model.layer3(x) x = self.model.layer4(x) x = self.eca(x) x = self.model.avgpool(x) x = torch.flatten(x, 1) x = self.model.fc(x) return x class ECA_Module(nn.Module): def init(self, channel, k_size=3): super(ECA_Module, self).init() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False) self.sigmoid = nn.Sigmoid() def forward(self, x): b, c, _, _ = x.size() y = self.avg_pool(x) y = self.conv(y.squeeze(-1).transpose(-1,-2)).transpose(-1,-2).unsqueeze(-1) y = self.sigmoid(y) return x * y.expand_as(x) class ImageDenoising(nn.Module): def init(self): super().init() self.model = eca_Resnet50() self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1) self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1) self.conv3 = nn.Conv2d(64, 3, kernel_size=3, stride=1, padding=1) def forward(self, x): x = self.conv1(x) x = F.relu(x) x = self.conv2(x) x = F.relu(x) x = self.conv3(x) x = F.relu(x) return x，使最后输出为[16,1,50,50,]。

pytorch1.0中torch.nn.Conv2d用法详解

import torch.nn as nn # 假设我们有一个输入张量x，形状为(batch_size, 1, Height, Width) # 我们想创建一个卷积层，将输入的单通道图像转换为6个通道的输出 self.conv1 = nn.Conv2d(in_channels=1, out_channels=...

fb.resnet.torch-master.zip_ResNet_ResNet深度学习_facebook_torch resn

1. models：存放不同变体的ResNet模型结构定义，如ResNet-18、ResNet-34、ResNet-50、ResNet-101和ResNet-152。 2. train.lua和test.lua：训练和测试模型的脚本，定义了优化器、损失函数、训练和验证循环等。 ...

更改import torch import torchvision.models as models import torch.nn as nn import torch.nn.functional as F class eca_Resnet50(nn.Module): def init(self): super().init() self.model = models.resnet50(pretrained=True) self.model.avgpool = nn.AdaptiveAvgPool2d((1,1)) self.model.fc = nn.Linear(2048, 1000) self.eca = ECA_Module(2048, 8) def forward(self, x): x = self.model.conv1(x) x = self.model.bn1(x) x = self.model.relu(x) x = self.model.maxpool(x) x = self.model.layer1(x) x = self.model.layer2(x) x = self.model.layer3(x) x = self.model.layer4(x) x = self.eca(x) x = self.model.avgpool(x) x = torch.flatten(x, 1) x = self.model.fc(x) return x class ECA_Module(nn.Module): def init(self, channel, k_size=3): super(ECA_Module, self).init() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False) self.sigmoid = nn.Sigmoid() def forward(self, x): b, c, _, _ = x.size() y = self.avg_pool(x) y = self.conv(y.squeeze(-1).transpose(-1,-2)).transpose(-1,-2).unsqueeze(-1) y = self.sigmoid(y) return x * y.expand_as(x) class ImageDenoising(nn.Module): def init(self): super().init() self.model = eca_Resnet50() self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1) self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1) self.conv3 = nn.Conv2d(64, 3, kernel_size=3, stride=1, padding=1) def forward(self, x): x = self.conv1(x) x = F.relu(x) x = self.conv2(x) x = F.relu(x) x = self.conv3(x) x = F.relu(x) return x输出为[16,1,50,50]

很好，你已经成功地定义了一个带有 ECA 模块的 ResNet50，以及一个图像去噪模型 ImageDenoising，输出大小为 [16, 1, 50, 50]。在 ImageDenoising 的 forward 函数中，你首先将输入 x 传递给 conv1，然后对其进行 ...

import os import datetime import argparse import torch import torch.nn as nn import torch.nn.functional as F import torchvision from torchvision.models.feature_extraction import create_feature_extractor import copy from collections import OrderedDict import transforms from network_files import FasterRCNN, AnchorsGenerator from my_dataset import VOCDataSet from train_utils import GroupedBatchSampler, create_aspect_ratio_groups from train_utils import train_eval_utils as utils # ---------------------------- ECA注意力模块 ---------------------------- class ECAAttention(nn.Module): def init(self, channels, kernel_size=3): super(ECAAttention, self).init() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.conv = nn.Conv1d(1, 1, kernel_size=kernel_size, padding=(kernel_size - 1) // 2, bias=False) self.sigmoid = nn.Sigmoid() def forward(self, x): # x: [B, C, H, W] b, c, h, w = x.size() # 全局平均池化 y = self.avg_pool(x).view(b, c, 1) # 一维卷积实现跨通道交互 y = self.conv(y.transpose(1, 2)).transpose(1, 2).view(b, c, 1, 1) # 生成注意力权重 y = self.sigmoid(y) return x * y.expand_as(x) # ---------------------------- 特征融合模块 ---------------------------- class FeatureFusionModule(nn.Module): def init(self, student_channels, teacher_channels): super().init() # 1x1卷积用于通道对齐 self.teacher_proj = nn.Conv2d(teacher_channels, student_channels, kernel_size=1) # 注意力机制 self.attention = nn.Sequential( nn.Conv2d(student_channels * 2, student_channels // 8, kernel_size=1), nn.ReLU(), nn.Conv2d(student_channels // 8, 2, kernel_size=1), nn.Softmax(dim=1) ) # ECA注意力模块 self.eca = ECAAttention(student_channels, kernel_size=3) def forward(self, student_feat, teacher_feat): # 调整教师特征的空间尺寸以匹配学生特征 if student_feat.shape[2:] != teacher_feat.shape[2:]: teacher_feat = F.interpolate(teacher_feat, size=student_feat.shape[2:], mode='bilinear', align_corners=False) # 通道投影 teacher_feat_proj = self.teacher_proj(teacher_feat) # 特征拼接 concat_feat = torch.cat([student_feat, teacher_feat_proj], dim=1) # 计算注意力权重 attn_weights = self.attention(concat_feat) # 加权融合 fused_feat = attn_weights[:, 0:1, :, :] * student_feat + attn_weights[:, 1:2, :, :] * teacher_feat_proj # 应用ECA注意力 fused_feat = self.eca(fused_feat) return fused_feat # ---------------------------- 简化版FPN实现 ---------------------------- class SimpleFPN(nn.Module): def init(self, in_channels_list, out_channels): super().init() self.inner_blocks = nn.ModuleList() self.layer_blocks = nn.ModuleList() # FPN输出层的ECA模块 self.eca_blocks = nn.ModuleList() # 为每个输入特征创建内部卷积和输出卷积 for in_channels in in_channels_list: inner_block = nn.Conv2d(in_channels, out_channels, 1) layer_block = nn.Conv2d(out_channels, out_channels, 3, padding=1) self.inner_blocks.append(inner_block) self.layer_blocks.append(layer_block) # 为每个FPN输出添加ECA模块 self.eca_blocks.append(ECAAttention(out_channels, kernel_size=3)) # 初始化权重 for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_uniform_(m.weight, a=1) if m.bias is not None: nn.init.constant_(m.bias, 0) def forward(self, x): # 假设x是一个有序的字典，包含'c2', 'c3', 'c4', 'c5'特征 c2, c3, c4, c5 = x['c2'], x['c3'], x['c4'], x['c5'] # 处理最顶层特征 last_inner = self.inner_blocks[3](c5) results = [] # 处理P5 p5 = self.layer_blocks[3](last_inner) p5 = self.eca_blocks[3](p5) # 应用ECA注意力 results.append(p5) # 自顶向下路径 for i in range(2, -1, -1): inner_lateral = self.inner_blocks[i](x[f'c{i + 2}']) feat_shape = inner_lateral.shape[-2:] last_inner = F.interpolate(last_inner, size=feat_shape, mode="nearest") last_inner = last_inner + inner_lateral # 应用卷积和ECA注意力 layer_out = self.layer_blocks[i](last_inner) layer_out = self.eca_blocks[i](layer_out) # 应用ECA注意力 results.insert(0, layer_out) # 返回有序的特征字典 return { 'p2': results[0], 'p3': results[1], 'p4': results[2], 'p5': results[3] } # ---------------------------- 通道剪枝器类 ---------------------------- class ChannelPruner: def init(self, model, input_size, device='cpu'): self.model = model self.input_size = input_size # (B, C, H, W) self.device = device self.model.to(device) self.prunable_layers = self._identify_prunable_layers() # 识别可剪枝层 self.channel_importance = {} # 存储各层通道重要性 self.mask = {} # 剪枝掩码 self.reset() def reset(self): """重置剪枝状态""" self.pruned_model = copy.deepcopy(self.model) self.pruned_model.to(self.device) for name, module in self.prunable_layers.items(): self.mask[name] = torch.ones(module.out_channels, dtype=torch.bool, device=self.device) def _identify_prunable_layers(self): """识别可剪枝的卷积层（优先中间层，如layer2、layer3）""" prunable_layers = OrderedDict() for name, module in self.model.named_modules(): # 主干网络层（layer1-layer4） if "backbone.backbone." in name: # layer1（底层，少剪或不剪） if "layer1" in name: if isinstance(module, nn.Conv2d) and module.out_channels > 1: prunable_layers[name] = module # layer2和layer3（中间层，优先剪枝） elif "layer2" in name or "layer3" in name: if isinstance(module, nn.Conv2d) and module.out_channels > 1: prunable_layers[name] = module # layer4（高层，少剪） elif "layer4" in name: if isinstance(module, nn.Conv2d) and module.out_channels > 1: prunable_layers[name] = module # FPN层（inner_blocks和layer_blocks） elif "fpn.inner_blocks" in name or "fpn.layer_blocks" in name: if isinstance(module, nn.Conv2d) and module.out_channels > 1: prunable_layers[name] = module # FeatureFusionModule的teacher_proj层 elif "feature_fusion." in name and "teacher_proj" in name: if isinstance(module, nn.Conv2d) and module.out_channels > 1: prunable_layers[name] = module return prunable_layers def compute_channel_importance(self, dataloader=None, num_batches=10): """基于激活值计算通道重要性""" self.pruned_model.eval() for name in self.prunable_layers.keys(): self.channel_importance[name] = torch.zeros( self.prunable_layers[name].out_channels, device=self.device) with torch.no_grad(): if dataloader is None: # 使用随机数据计算 for _ in range(num_batches): inputs = torch.randn(self.input_size, device=self.device) self._forward_once([inputs]) # 包装为列表以匹配数据加载器格式 else: # 使用验证集计算 for inputs, _ in dataloader: if num_batches <= 0: break # 将图像列表移至设备 inputs = [img.to(self.device) for img in inputs] self._forward_once(inputs) num_batches -= 1 def _forward_once(self, inputs): """前向传播并记录各层激活值""" def hook(module, input, output, name): # 计算通道重要性（绝对值均值） channel_impact = torch.mean(torch.abs(output), dim=(0, 2, 3)) self.channel_importance[name] += channel_impact hooks = [] for name, module in self.pruned_model.named_modules(): if name in self.prunable_layers: hooks.append(module.register_forward_hook(lambda m, i, o, n=name: hook(m, i, o, n))) # 修改这里以正确处理目标检测模型的输入格式 self.pruned_model(inputs) for hook in hooks: hook.remove() def prune_channels(self, layer_prune_ratios): """按层剪枝（支持不同层不同比例）""" for name, ratio in layer_prune_ratios.items(): if name not in self.prunable_layers: continue module = self.prunable_layers[name] num_channels = module.out_channels num_prune = int(num_channels * ratio) if num_prune <= 0: continue # 获取最不重要的通道索引（按重要性从小到大排序） importance = self.channel_importance[name] _, indices = torch.sort(importance) prune_indices = indices[:num_prune] # 更新掩码 self.mask[name][prune_indices] = False def apply_pruning(self): """应用剪枝掩码到模型""" pruned_model = copy.deepcopy(self.model) pruned_model.to(self.device) # 存储每层的输入通道掩码（用于处理非连续层的依赖关系） output_masks = {} # 第一遍：处理所有可剪枝层，记录输出通道掩码 for name, module in pruned_model.named_modules(): if name in self.mask: curr_mask = self.mask[name] # 处理卷积层 if isinstance(module, nn.Conv2d): # 剪枝输出通道 module.weight.data = module.weight.data[curr_mask] if module.bias is not None: module.bias.data = module.bias.data[curr_mask] module.out_channels = curr_mask.sum().item() # 记录该层的输出通道掩码 output_masks[name] = curr_mask print(f"处理层: {name}, 原始输出通道: {len(curr_mask)}, 剪枝后: {curr_mask.sum().item()}") # 处理ECA注意力模块 elif isinstance(module, ECAAttention): # ECA模块不需要修改，因为它不改变通道数 pass # 处理FeatureFusionModule的teacher_proj elif "teacher_proj" in name: # 输入通道来自教师特征，输出通道由curr_mask决定 module.weight.data = module.weight.data[curr_mask] module.out_channels = curr_mask.sum().item() # 记录该层的输出通道掩码 output_masks[name] = curr_mask # 第二遍：处理非剪枝层的输入通道 for name, module in pruned_model.named_modules(): if name in output_masks: # 已在第一遍处理过，跳过 continue # 对于卷积层，查找其输入来源的层的掩码 if isinstance(module, nn.Conv2d): # 尝试查找前一层的输出掩码 prev_mask = None # 简化的查找逻辑，实际情况可能需要更复杂的实现 # 这里假设命名约定能帮助我们找到前一层 for possible_prev_name in reversed(list(output_masks.keys())): if possible_prev_name in name or ( "backbone" in name and "backbone" in possible_prev_name): prev_mask = output_masks[possible_prev_name] break # 应用输入通道掩码 if prev_mask is not None and prev_mask.shape[0] == module.weight.shape[1]: print( f"应用输入掩码到层: {name}, 原始输入通道: {module.weight.shape[1]}, 剪枝后: {prev_mask.sum().item()}") module.weight.data = module.weight.data[:, prev_mask] module.in_channels = prev_mask.sum().item() else: print(f"警告: 无法为层 {name} 找到匹配的输入掩码，保持原始通道数") return pruned_model def evaluate_model(self, dataloader, model=None): """评估模型性能（mAP）""" if model is None: model = self.pruned_model model.eval() # 调用评估函数 coco_info = utils.evaluate(model, dataloader, device=self.device) return coco_info[1] # 返回mAP值 def get_model_size(self, model=None): """获取模型大小（MB）""" if model is None: model = self.pruned_model torch.save(model.state_dict(), "temp.pth") size = os.path.getsize("temp.pth") / (1024 * 1024) os.remove("temp.pth") return size def create_model(num_classes): # ---------------------------- 学生模型定义 ---------------------------- try: # 尝试使用新版本API backbone = torchvision.models.resnet18(weights=torchvision.models.ResNet18_Weights.IMAGENET1K_V1) except AttributeError: # 旧版本API backbone = torchvision.models.resnet18(pretrained=True) # 提取多个层作为特征融合点 return_nodes = { "layer1": "c2", # 对应FPN的P2 (1/4) "layer2": "c3", # 对应FPN的P3 (1/8) "layer3": "c4", # 对应FPN的P4 (1/16) "layer4": "c5", # 对应FPN的P5 (1/32) } backbone = create_feature_extractor(backbone, return_nodes=return_nodes) # 添加简化版FPN fpn = SimpleFPN([64, 128, 256, 512], 256) # 创建一个包装模块，将backbone和FPN组合在一起 class BackboneWithFPN(nn.Module): def init(self, backbone, fpn): super().init() self.backbone = backbone self.fpn = fpn self.out_channels = 256 # FPN输出通道数 def forward(self, x): x = self.backbone(x) x = self.fpn(x) return x # 替换原始backbone为带FPN的backbone backbone_with_fpn = BackboneWithFPN(backbone, fpn) # 增加更多anchor尺度和宽高比 anchor_sizes = ((16, 32, 48), (32, 64, 96), (64, 128, 192), (128, 256, 384), (256, 512, 768)) aspect_ratios = ((0.33, 0.5, 1.0, 2.0, 3.0),) * len(anchor_sizes) anchor_generator = AnchorsGenerator( sizes=anchor_sizes, aspect_ratios=aspect_ratios ) roi_pooler = torchvision.ops.MultiScaleRoIAlign( featmap_names=['p2', 'p3', 'p4', 'p5'], output_size=[7, 7], sampling_ratio=2 ) model = FasterRCNN( backbone=backbone_with_fpn, num_classes=num_classes, rpn_anchor_generator=anchor_generator, box_roi_pool=roi_pooler ) # 添加多尺度特征融合模块 model.feature_fusion = nn.ModuleDict({ 'p2': FeatureFusionModule(256, 256), 'p3': FeatureFusionModule(256, 256), 'p4': FeatureFusionModule(256, 256), 'p5': FeatureFusionModule(256, 256), }) return model def main(args): # 确保输出目录存在 if args.output_dir: os.makedirs(args.output_dir, exist_ok=True) # ---------------------------- 模型剪枝流程 ---------------------------- print("开始模型剪枝...") device = torch.device(args.device if torch.cuda.is_available() else "cpu") # 加载已训练的模型 model = create_model(num_classes=args.num_classes + 1) checkpoint = torch.load(args.resume, map_location=device) model.load_state_dict(checkpoint["model"]) model.to(device) # 输入尺寸（需与训练时一致） input_size = (1, 3, 800, 600) # (B, C, H, W) # 加载验证集 val_dataset = VOCDataSet(args.data_path, "2012", transforms.Compose([transforms.ToTensor()]), "val.txt") val_data_loader = torch.utils.data.DataLoader( val_dataset, batch_size=1, shuffle=False, pin_memory=True, num_workers=4, collate_fn=val_dataset.collate_fn ) # 初始化剪枝器 pruner = ChannelPruner(model, input_size, device=device) # 计算通道重要性 print("计算通道重要性...") pruner.compute_channel_importance(dataloader=val_data_loader, num_batches=10) # 定义分层剪枝比例（中间层多剪，底层和高层少剪） layer_prune_ratios = { # 主干网络 "backbone.backbone.layer1": args.layer1_ratio, # 底层：剪10% "backbone.backbone.layer2": args.layer2_ratio, # 中间层：剪30% "backbone.backbone.layer3": args.layer3_ratio, # 中间层：剪30% "backbone.backbone.layer4": args.layer4_ratio, # 高层：剪10% # FPN层 "fpn.inner_blocks": args.fpn_inner_ratio, # FPN内部卷积：剪20% "fpn.layer_blocks": args.fpn_layer_ratio, # FPN输出卷积：剪20% # FeatureFusionModule的teacher_proj "feature_fusion.p2.teacher_proj": args.ff_p2_ratio, "feature_fusion.p3.teacher_proj": args.ff_p3_ratio, "feature_fusion.p4.teacher_proj": args.ff_p4_ratio, "feature_fusion.p5.teacher_proj": args.ff_p5_ratio, } # 执行剪枝 print("执行通道剪枝...") pruner.prune_channels(layer_prune_ratios) # 应用剪枝并获取新模型 pruned_model = pruner.apply_pruning() # 评估剪枝前后的性能和模型大小 original_size = pruner.get_model_size(model) pruned_size = pruner.get_model_size(pruned_model) original_map = pruner.evaluate_model(val_data_loader, model) pruned_map = pruner.evaluate_model(val_data_loader, pruned_model) print(f"原始模型大小: {original_size:.2f} MB") print(f"剪枝后模型大小: {pruned_size:.2f} MB") print(f"模型压缩率: {100 * (1 - pruned_size / original_size):.2f}%") print(f"原始mAP: {original_map:.4f}, 剪枝后mAP: {pruned_map:.4f}") # 保存剪枝后的模型 pruned_model_path = os.path.join(args.output_dir, f"pruned_resNetFpn_{args.num_classes}classes.pth") torch.save(pruned_model.state_dict(), pruned_model_path) print(f"剪枝后的模型已保存至: {pruned_model_path}") # 对剪枝后的模型进行微调（默认启用） print("准备对剪枝后的模型进行微调...") # 加载训练集 train_dataset = VOCDataSet(args.data_path, "2012", transforms.Compose([ transforms.ToTensor(), transforms.RandomHorizontalFlip(0.5) ]), "train.txt") # 创建训练数据加载器 train_sampler = torch.utils.data.RandomSampler(train_dataset) train_data_loader = torch.utils.data.DataLoader( train_dataset, batch_size=args.batch_size, sampler=train_sampler, num_workers=4, collate_fn=train_dataset.collate_fn ) # 定义优化器 params = [p for p in pruned_model.parameters() if p.requires_grad] optimizer = torch.optim.SGD( params, lr=args.lr, momentum=0.9, weight_decay=0.0005 ) # 定义学习率调度器 lr_scheduler = torch.optim.lr_scheduler.StepLR( optimizer, step_size=args.lr_step_size, gamma=args.lr_gamma ) # 微调训练 print(f"开始微调: 批次大小={args.batch_size}, 学习率={args.lr}, 轮数={args.epochs}") best_map = 0.0 for epoch in range(args.epochs): # 训练一个epoch utils.train_one_epoch(pruned_model, optimizer, train_data_loader, device, epoch, print_freq=50) # 更新学习率 lr_scheduler.step() # 评估模型 coco_info = utils.evaluate(pruned_model, val_data_loader, device=device) # 保存当前最佳模型 map_50 = coco_info[1] # COCO评估指标中的IoU=0.50时的mAP if map_50 > best_map: best_map = map_50 torch.save({ 'model': pruned_model.state_dict(), 'optimizer': optimizer.state_dict(), 'lr_scheduler': lr_scheduler.state_dict(), 'epoch': epoch, 'args': args }, os.path.join(args.output_dir, f"finetuned_pruned_best.pth")) print(f"Epoch {epoch + 1}/{args.epochs}, [email protected]: {map_50:.4f}") print(f"微调完成，最佳[email protected]: {best_map:.4f}") if name == "main": parser = argparse.ArgumentParser(description=doc) # ---------------------------- 剪枝参数 ---------------------------- parser.add_argument('--device', default='cuda:0', help='device') parser.add_argument('--data-path', default='./', help='dataset') parser.add_argument('--num-classes', default=6, type=int, help='num_classes') parser.add_argument('--output-dir', default='./save_weights', help='path where to save') parser.add_argument('--resume', default='./save_weights/resNetFpn-zuizhong.pth', type=str, help='resume from checkpoint') # 分层剪枝比例参数 parser.add_argument('--layer1-ratio', default=0.1, type=float, help='layer1 pruning ratio') parser.add_argument('--layer2-ratio', default=0.5, type=float, help='layer2 pruning ratio') parser.add_argument('--layer3-ratio', default=0.5, type=float, help='layer3 pruning ratio') parser.add_argument('--layer4-ratio', default=0.1, type=float, help='layer4 pruning ratio') parser.add_argument('--fpn-inner-ratio', default=0.2, type=float, help='FPN inner blocks pruning ratio') parser.add_argument('--fpn-layer-ratio', default=0.2, type=float, help='FPN layer blocks pruning ratio') parser.add_argument('--ff-p2-ratio', default=0.2, type=float, help='Feature fusion P2 pruning ratio') parser.add_argument('--ff-p3-ratio', default=0.2, type=float, help='Feature fusion P3 pruning ratio') parser.add_argument('--ff-p4-ratio', default=0.2, type=float, help='Feature fusion P4 pruning ratio') parser.add_argument('--ff-p5-ratio', default=0.2, type=float, help='Feature fusion P5 pruning ratio') # ---------------------------- 微调参数 ---------------------------- parser.add_argument('--epochs', default=10, type=int, help='number of total epochs to run') parser.add_argument('--batch-size', default=8, type=int, help='batch size') parser.add_argument('--lr', default=0.05, type=float, help='initial learning rate') parser.add_argument('--lr-step-size', default=3, type=int, help='decrease lr every step-size epochs') parser.add_argument('--lr-gamma', default=0.1, type=float, help='decrease lr by a factor of lr-gamma') args = parser.parse_args() main(args)以上代码没有实现成功的剪枝，请你仔细检查并修改，帮助我实现成功的剪枝。

例如，剪枝50%的通道prune_rate=0.5#遍历模型中所有卷积层forname,moduleinmodel.named_modules():ifisinstance(module,torch.nn.Conv2d):#获取该卷积层的通道数num_channels=module.weight.data.shape[0]#计算要...

为什么我看到的版本跟你的不太一样？ import torch import torch.nn as nn class ChannelAttention(nn.Module): def init(self, in_planes, ratio=16): super(ChannelAttention, self).init() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.max_pool = nn.AdaptiveMaxPool2d(1) # 两个全连接层用于特征压缩和恢复 self.fc1 = nn.Conv2d(in_planes, in_planes // ratio, 1, bias=False) self.relu = nn.ReLU() self.fc2 = nn.Conv2d(in_planes // ratio, in_planes, 1, bias=False) self.sigmoid = nn.Sigmoid() def forward(self, x): avg_out = self.fc2(self.relu(self.fc1(self.avg_pool(x)))) max_out = self.fc2(self.relu(self.fc1(self.max_pool(x)))) return self.sigmoid(avg_out + max_out) class SpatialAttention(nn.Module): def init(self, kernel_size=7): super(SpatialAttention, self).init() assert kernel_size in (3, 7), 'kernel size must be 3 or 7' padding = 3 if kernel_size == 7 else 1 self.conv = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False) self.sigmoid = nn.Sigmoid() def forward(self, x): avg_out = torch.mean(x, dim=1, keepdim=True) max_out, _ = torch.max(x, dim=1, keepdim=True) x = torch.cat([avg_out, max_out], dim=1) return self.sigmoid(self.conv(x)) class CBAM(nn.Module): def init(self, in_planes, ratio=16, kernel_size=7): super(CBAM, self).init() self.channel_att = ChannelAttention(in_planes, ratio) self.spatial_att = SpatialAttention(kernel_size) def forward(self, x): # 通道注意力 x = x * self.channel_att(x) # 空间注意力 x = x * self.spatial_att(x) return x # 测试代码 if name == 'main': x = torch.randn(2, 64, 32, 32) # 输入特征图 (batch, channels, height, width) cbam = CBAM(64) out = cbam(x) print(f"Input shape: {x.shape}, Output shape: {out.shape}") # 应保持相同形状

class ChannelAttention(nn.Module): def __init__(self, in_planes, ratio=16): super().__init__() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.max_pool = nn.AdaptiveMaxPool2d(1) self.fc = nn....

更改eca_Resnet50来进行图像去噪的pytorch代码

import torch.nn.functional as F def denoise_loss(input_image, target_image): mse_loss = F.mse_loss(input_image, target_image) return mse_loss 最后，我们可以使用以下代码来定义训练循环： ...

Python基于Resnet50等模型结合Attention的多模型消融实验的人脸表情识别项目源码

基于多模型消融实验的人脸表情识别，本实验主要是通过使用主流的卷积神经网络模型（vgg16、resnet50、inceptionv3）及自定义卷积神经网络，结合注意力机制，包括(cbam、se、eca)三个神经网络嵌入到卷积神经网络中，...

你觉得all = ['Threshold', 'ReLU', 'RReLU', 'Hardtanh', 'ReLU6', 'Sigmoid', 'Hardsigmoid', 'Tanh', 'SiLU', 'Mish', 'Hardswish', 'ELU', 'CELU', 'SELU', 'GLU', 'GELU', 'Hardshrink', 'LeakyReLU', 'LogSigmoid', 'Softplus', 'Softshrink', 'MultiheadAttention', 'PReLU', 'Softsign', 'Tanhshrink', 'Softmin', 'Softmax', 'Softmax2d', 'LogSoftmax'] 这些激活函数哪些更适合下面的代码呢？或者你从知网万维等等论文网络中搜索查看代码如下： class Swish(nn.Module): def forward(self, x): return x * torch.sigmoid(x) class DynamicChannelAttention(nn.Module): def init(self, channels, reduction=16): super().init() mid = max(channels // reduction, 4) self.avg_pool = nn.AdaptiveAvgPool2d(1) self.max_pool = nn.AdaptiveMaxPool2d(1) self.fc = nn.Sequential( nn.Conv2d(channels, mid, 1, bias=False), Swish(), nn.Conv2d(mid, channels, 1, bias=False), nn.Sigmoid() ) self.alpha = nn.Parameter(torch.tensor([0.5]), requires_grad=True) def forward(self, x): avg_out = self.fc(self.avg_pool(x)) max_out = self.fc(self.max_pool(x)) return x * (self.alpha * avg_out + (1 - self.alpha) * max_out) class DeformableSpatialAttention(nn.Module): def init(self, channels, kernel_size=7): super().init() self.conv = nn.Sequential( nn.Conv2d(channels, channels // 2, 3, padding=1, groups=channels // 4), Swish(), nn.Conv2d(channels // 2, 1, kernel_size, padding=kernel_size // 2, groups=1), nn.Sigmoid() ) self.offset_conv = nn.Conv2d(channels, 2 * kernel_size ** 2, kernel_size, padding=kernel_size // 2) def forward(self, x): offset = self.offset_conv(x) weight_map = self.conv(x) return x * weight_map

import torch.nn.functional as F # Swish模块（固定使用Swish激活） class Swish(nn.Module): def __init__(self, beta=1.0): super().__init__() self.beta = beta def forward(self, x): return x * ...

用eca_resnet50进行图像去噪，包含train.py、val,py、test.py，并在test.py中导出去噪后的图片

import torch.nn.functional as F class ECABlock(nn.Module): def __init__(self, channels, kernel_size, gamma=2, b=1): super(ECABlock, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d(1) self...

class LocalBottleNeck(nn.Module): def init(self): super(LocalBottleNeck, self).init() self.conv1 = self._make_branch(1024) self.conv2 = self._make_branch(1024) self.conv3 = self._make_branch(1024) self.down1 = nn.Sequential( nn.Conv2d(3072, 1024, 1, bias=False), nn.BatchNorm2d(1024), nn.Conv2d(1024, 1024, 3, 2, 1, bias=False), nn.BatchNorm2d(1024), nn.ReLU(), nn.Conv2d(1024, 2048, 1, bias=False), nn.BatchNorm2d(2048) ) self.down2 = nn.Sequential( nn.Conv2d(3072, 2048, 3, 2, 1, bias=False), nn.BatchNorm2d(2048), ) self.final = nn.Sequential( ResidualBlock(2048, 2048), ResidualBlock(2048, 2048), ) def _make_branch(self, out_ch): return nn.Sequential( nn.Conv2d(768, out_ch, 1), Conv_LayerNorm(out_ch), nn.Conv2d(out_ch, out_ch, 3, 1, 1, bias=False), Conv_LayerNorm(out_ch), ) def forward(self, feats): feats1 = self.conv1(feats[0]) feats2 = self.conv2(feats[1]) feats3 = self.conv3(feats[2]) fused = torch.concat([feats1, feats2, feats3], dim=1) fused = F.relu(self.down1(fused) + self.down2(fused)) return self.final(fused)这段代码用于将vitbase输入的第2、6、10层[1, 768, 14, 14]特征融合输出[1, 2048, 7, 7]最终用来还原widresnet50的特征，请你改写这段代码，提升还原精度，要求给出完整的代码实现，不要自己提出模块，尽量使用新颖有效经过检验的高效模块，方便论文书写，给出使用理由

import torch.nn.functional as F class SEBottleneck(nn.Module): """结合通道注意力与残差连接，CVPR 2018""" def __init__(self, in_ch, out_ch, reduction=16): super().__init__() self.conv = nn....

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