classification-main/block/model_get.py

import os
import torch

choice_dict = {
    'yolov7_cls': 'model_prepare(args).yolov7_cls()',
    'timm_model': 'model_prepare(args).timm_model()',
    'Alexnet': 'model_prepare(args).Alexnet()',
    'badnet': 'model_prepare(args).badnet()',
    'GoogleNet': 'model_prepare(args).GoogleNet()',
    'mobilenetv2': 'model_prepare(args).mobilenetv2()',
    'resnet': 'model_prepare(args).resnet()',
    'VGG19': 'model_prepare(args).VGG19()',
    'efficientnet': 'model_prepare(args).EfficientNetV2_S()',
    'LeNet': 'model_prepare(args).LeNet()'
}


def model_get(args):
    if os.path.exists(args.weight):  # 优先加载已有模型继续训练
        model_dict = torch.load(args.weight, map_location='cpu')
    else:  # 新建模型
        if args.prune:  # 模型剪枝
            model_dict = torch.load(args.prune_weight, map_location='cpu')
            model = model_dict['model']
            model = prune(args, model)
        elif args.timm:
            # model = model_prepare(args).timm_model()
            model = eval(choice_dict['timm_model'])
        else:
            model = eval(choice_dict[args.model])
        model_dict = {}
        model_dict['model'] = model
        model_dict['epoch_finished'] = 0  # 已训练的轮数
        model_dict['optimizer_state_dict'] = None  # 学习率参数
        model_dict['ema_updates'] = 0  # ema参数
        model_dict['standard'] = 0  # 评价指标
    return model_dict


def prune(args, model):
    # 记录BN层权重
    # Debugging output
    BatchNorm2d_weight = []
    for module in model.modules():
        if isinstance(module, torch.nn.BatchNorm2d):
            BatchNorm2d_weight.append(module.weight.data.clone())
    BatchNorm2d_weight_abs = torch.cat(BatchNorm2d_weight, dim=0).abs()
    weight_len = len(BatchNorm2d_weight)
    # 记录权重与BN层编号的关系
    BatchNorm2d_id = []
    for i in range(weight_len):
        BatchNorm2d_id.extend([i for _ in range(len(BatchNorm2d_weight[i]))])
    id_all = torch.tensor(BatchNorm2d_id)
    # 筛选
    value, index = torch.sort(BatchNorm2d_weight_abs, dim=0, descending=True)
    boundary = int(len(index) * args.prune_ratio)
    prune_index = index[0:boundary]  # 保留参数的下标
    prune_index, _ = torch.sort(prune_index, dim=0, descending=False)
    prune_id = id_all[prune_index]
    # 将保留参数的下标放到每层中
    index_list = [[] for _ in range(weight_len)]
    for i in range(len(prune_index)):
        index_list[prune_id[i]].append(prune_index[i])
    # 将每层保留参数的下标换算成相对下标
    record_len = 0
    for i in range(weight_len):
        index_list[i] = torch.tensor(index_list[i])
        index_list[i] -= record_len
        if len(index_list[i]) == 0:  # 存在整层都被减去的情况，至少保留一层
            index_list[i] = torch.argmax(BatchNorm2d_weight[i], dim=0).unsqueeze(0)
        record_len += len(BatchNorm2d_weight[i])
    # 创建剪枝后的模型
    args.prune_num = [len(_) for _ in index_list]
    prune_model = eval(choice_dict[args.model])
    # BN层权重赋值和部分conv权重赋值
    index = 0
    for module, prune_module in zip(model.modules(), prune_model.modules()):
        if isinstance(module, torch.nn.Conv2d):  # 更新部分Conv2d层权重
            print(f"处理 Conv2d 层，索引：{index}，权重形状：{module.weight.data.shape}")
            if index > 0 and index - 1 < len(index_list):
                # 打印 index_list 状态
                print(f"当前层前一层索引列表（index_list[{index - 1}]）：{index_list[index - 1]}")
                # 检查是否索引越界
                if index_list[index - 1].max().item() < module.weight.data.shape[1]:  # 检查最大索引是否小于输入通道数
                    weight = module.weight.data.clone()
                    if index < len(index_list):
                        weight = weight[:, index_list[index - 1], :, :]
                    if prune_module.weight.data.shape == weight.shape:
                        prune_module.weight.data = weight
                else:
                    print("索引越界，跳过当前层的处理")
            elif index == 0:
                weight = module.weight.data.clone()[index_list[index]]
                if prune_module.weight.data.shape == weight.shape:
                    prune_module.weight.data = weight

        if isinstance(module, torch.nn.BatchNorm2d):
            print(f"更新 BatchNorm2d 层，索引：{index}，权重形状：{module.weight.data.shape}")
            if index < len(index_list) and len(index_list[index]) > 0:
                expected_size = module.weight.data.size(0)
                actual_size = len(index_list[index])
                print(f"期望的大小：{expected_size}, 实际保留的大小：{actual_size}")
                if actual_size == expected_size:
                    prune_module.weight.data = module.weight.data.clone()[index_list[index]]
                    prune_module.bias.data = module.bias.data.clone()[index_list[index]]
                    prune_module.running_mean = module.running_mean.clone()[index_list[index]]
                    prune_module.running_var = module.running_var.clone()[index_list[index]]
                else:
                    print("警告: 剪枝后的大小与期望的 BatchNorm2d 层大小不匹配")
            index += 1
    return prune_model


class model_prepare:
    def __init__(self, args):
        self.args = args

    def timm_model(self):
        from model.timm_model import timm_model
        model = timm_model(self.args)
        return model

    def yolov7_cls(self):
        from model.yolov7_cls import yolov7_cls
        model = yolov7_cls(self.args)
        return model
    
    def Alexnet(self):
        from model.Alexnet import Alexnet
        model = Alexnet(self.args.input_channels, self.args.output_num, self.args.input_size)
        return model
    
    def badnet(self):
        from model.badnet import BadNet
        model = BadNet(self.args.input_channels, self.args.output_num)
        return model
    
    def GoogleNet(self):
        from model.GoogleNet import GoogLeNet
        model = GoogLeNet(self.args.input_channels, self.args.output_num)
        return model
    
    def mobilenetv2(self):
        from model.mobilenetv2 import MobileNetV2
        model = MobileNetV2(self.args.input_channels, self.args.output_num)
        return model
    
    def resnet(self):
        from model.resnet import ResNet18
        model = ResNet18(self.args.input_channels, self.args.output_num)
        return model
    
    def VGG19(self):
        from model.VGG19 import VGG19
        model = VGG19()
        return model
    
    def EfficientNetV2_S(self):
        from model.efficientnet import EfficientNetV2_S
        model = EfficientNetV2_S(self.args.input_channels, self.args.output_num)
        return model

    def LeNet(self):
        from model.LeNet import LeNet
        model = LeNet(self.args.input_channels, self.args.output_num, self.args.input_size)
        return model