解释代码：class Reshape(nn.Module): def __init__(self, *args): super(Reshape, self).__init__() self.shape = args def forward(self, x): return x.view((x.size(0),) + self.shape)

这段代码定义了一个名为 Reshape 的类，它是 nn.Module 的子类。该类用于将输入张量 x 进行形状重塑操作。

在类的构造函数中，使用 super() 函数调用父类的构造函数来初始化 Reshape 类。同时，使用 self.shape 存储传入构造函数的参数 args，以便在前向传播函数中使用。

在前向传播函数 forward 中，调用了 x.view() 方法来对输入张量进行形状重塑操作。x.size(0) 表示输入张量的批量大小，而 self.shape 则表示要重塑的目标形状。通过将这两个形状信息进行拼接，可以得到最终的目标形状。最后，返回重塑后的张量。

这个类的作用是让用户能够通过指定目标形状来对输入张量进行形状重塑操作。

相关问题

以CWRU数据集编写基于python的mmd损失训练DCNN代码

好的，下面是基于Python的MMD损失训练DCNN代码，使用CWRU数据集：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import scipy.io as sio
import argparse

# 设置参数
parser = argparse.ArgumentParser(description='MMD损失训练DCNN')
parser.add_argument('--batch_size', type=int, default=64, help='输入批次大小')
parser.add_argument('--lr', type=float, default=1e-3, help='学习率')
parser.add_argument('--num_epochs', type=int, default=100, help='训练轮次')
parser.add_argument('--num_classes', type=int, default=10, help='分类数')
parser.add_argument('--num_channels', type=int, default=2, help='通道数')
parser.add_argument('--num_filters', type=int, default=32, help='卷积核数')
parser.add_argument('--num_blocks', type=int, default=3, help='残差块数')
parser.add_argument('--num_features', type=int, default=128, help='特征数')
parser.add_argument('--mmd_sigma', type=float, default=1, help='MMD损失函数中的sigma')
args = parser.parse_args()

# 加载数据
data = sio.loadmat('CWRU.mat')
X_train = data['X_train']
y_train = data['y_train']
X_test = data['X_test']
y_test = data['y_test']

# 将数据转换为张量
X_train = torch.from_numpy(X_train).float()
y_train = torch.from_numpy(y_train).long()
X_test = torch.from_numpy(X_test).float()
y_test = torch.from_numpy(y_test).long()

# 创建模型
class ResNet(nn.Module):
    def __init__(self, num_channels, num_filters, num_blocks, num_classes, num_features):
        super(ResNet, self).__init__()

        self.conv1 = nn.Conv1d(num_channels, num_filters, kernel_size=5, stride=2, padding=2)
        self.bn1 = nn.BatchNorm1d(num_filters)
        self.relu = nn.ReLU(inplace=True)

        self.layer1 = self._make_layer(num_filters, num_filters, num_blocks-1)
        self.layer2 = self._make_layer(num_filters, num_filters*2, num_blocks)
        self.layer3 = self._make_layer(num_filters*2, num_filters*4, num_blocks)
        self.layer4 = self._make_layer(num_filters*4, num_filters*8, num_blocks)

        self.avgpool = nn.AdaptiveAvgPool1d(1)
        self.fc1 = nn.Linear(num_filters*8, num_features)
        self.fc2 = nn.Linear(num_features, num_classes)

    def _make_layer(self, in_channels, out_channels, num_blocks):
        layers = []
        layers.append(nn.Conv1d(in_channels, out_channels, kernel_size=3, stride=2, padding=1))
        layers.append(nn.BatchNorm1d(out_channels))
        layers.append(nn.ReLU(inplace=True))

        for i in range(num_blocks):
            layers.append(nn.Conv1d(out_channels, out_channels, kernel_size=3, stride=1, padding=1))
            layers.append(nn.BatchNorm1d(out_channels))
            layers.append(nn.ReLU(inplace=True))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)

        return x

# 定义损失函数和优化器
model = ResNet(args.num_channels, args.num_filters, args.num_blocks, args.num_classes, args.num_features)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=args.lr)

# 训练模型
for epoch in range(args.num_epochs):
    running_loss = 0.0
    for i in range(0, len(X_train), args.batch_size):
        # 取得当前批次的数据和标签
        inputs = X_train[i:i+args.batch_size]
        labels = y_train[i:i+args.batch_size]

        # 清零梯度
        optimizer.zero_grad()

        # 前向传播
        outputs = model(inputs)

        # 计算损失函数
        loss = criterion(outputs, labels)

        # 计算MMD损失函数
        X_data = inputs.numpy()
        X_data = X_data.reshape(-1, X_data.shape[1]*X_data.shape[2])
        X_data = X_data / np.max(X_data, axis=1, keepdims=True)
        X_data = torch.from_numpy(X_data).float()
        X_data = X_data.cuda()

        mmd_loss = 0.0
        for j in range(args.num_classes):
            X_class = X_data[labels == j]
            X_class_mean = torch.mean(X_class, dim=0, keepdim=True)
            X_class_diff = X_class - X_class_mean

            mmd_loss += torch.mean(torch.exp(-torch.sum(X_class_diff**2, dim=1)/(2*args.mmd_sigma**2)))

        loss += mmd_loss

        # 反向传播和优化
        loss.backward()
        optimizer.step()

        running_loss += loss.item()

    # 打印每轮的损失函数值
    print('Epoch [%d/%d], Loss: %.4f' % (epoch+1, args.num_epochs, running_loss))

# 测试模型
with torch.no_grad():
    correct = 0
    total = 0
    for i in range(0, len(X_test), args.batch_size):
        inputs = X_test[i:i+args.batch_size]
        labels = y_test[i:i+args.batch_size]
        outputs = model(inputs)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

    print('测试集准确率: %d %%' % (100 * correct / total))

在命令行中运行以下命令使用上面的代码：

python train.py --batch_size 64 --lr 1e-3 --num_epochs 100 --num_classes 10 --num_channels 2 --num_filters 32 --num_blocks 3 --num_features 128 --mmd_sigma 1

其中，`--batch_size` 表示每个批次的大小，`--lr` 表示学习率，`--num_epochs` 表示训练轮数，`--num_classes` 表示分类数，`--num_channels` 表示通道数，`--num_filters` 表示卷积核数，`--num_blocks` 表示残差块数，`--num_features` 表示特征数，`--mmd_sigma` 表示MMD损失函数中的sigma。

ResRep 剪枝方法 YOLOv5 6.1中的应用，完整详细代码复现

ResRep 剪枝方法是一种基于残差网络的剪枝方法，可以有效地减少神经网络模型的参数和计算量，同时保持模型的精度。

在 YOLOv5 6.1 版本中，已经应用了 ResRep 剪枝方法，可以通过以下步骤进行完整详细代码复现：

1. 下载 YOLOv5 6.1 版本的代码，并安装相关依赖。

git clone https://github.com/ultralytics/yolov5.git
cd yolov5/
pip install -r requirements.txt

2. 下载 COCO 数据集，并将其放在 `data/` 文件夹中。

mkdir data/
cd data/
wget https://github.com/ultralytics/yolov5/releases/download/v6.0/coco128.zip
unzip coco128.zip
cd ..

3. 在 `models/yolov5s.yaml` 文件中，修改模型的 `anchors`、`nc` 和 `depth_multiple` 参数。

anchors:
  - [10,13, 16,30, 33,23]  # P3/8
  - [30,61, 62,45, 59,119]  # P4/16
  - [116,90, 156,198, 373,326]  # P5/32
nc: 80
depth_multiple: 0.33

4. 在 `train.py` 文件中，修改训练参数，包括 `epochs`、`batch-size` 和 `img-size` 等。

python train.py --img 640 --batch 16 --epochs 300 --data coco.yaml --cfg models/yolov5s.yaml --weights '' --name yolov5s_resrep

5. 在 `models/yolo.py` 文件中，添加 ResRep 剪枝方法的相关代码，包括 `resrep_prune()` 和 `forward` 函数的修改。

import torch.nn as nn
import torch.nn.functional as F
from models.common import Conv, Bottleneck, SPP, DWConv, Focus, Concat
from utils.torch_utils import time_synchronized

class ResRep(nn.Module):
    def __init__(self, model, prune_idx):
        super(ResRep, self).__init__()
        self.model = model
        self.prune_idx = prune_idx

    def forward(self, x):
        # Forward pass through the pruned model
        for i, m in enumerate(self.model):
            x = m(x)
            if i == self.prune_idx:
                break
        return x

    def resrep_prune(self, threshold):
        # Prune the model based on the threshold
        pruned_idx = []
        for i, m in enumerate(self.model):
            if isinstance(m, Bottleneck):
                if m.bn3.weight is not None:
                    mask = m.bn3.weight.data.abs().ge(threshold).float().cuda()
                    m.bn3.weight.data.mul_(mask)
                    m.bn3.bias.data.mul_(mask)
                    m.conv3.weight.data.mul_(mask.view(-1, 1, 1, 1))
                    pruned_idx.append(i)
            elif isinstance(m, Conv):
                if m.bn.weight is not None:
                    mask = m.bn.weight.data.abs().ge(threshold).float().cuda()
                    m.bn.weight.data.mul_(mask)
                    m.bn.bias.data.mul_(mask)
                    m.conv.weight.data.mul_(mask.view(-1, 1, 1, 1))
                    pruned_idx.append(i)
        self.prune_idx = max(pruned_idx)

class YOLOLayer(nn.Module):
    def __init__(self, anchors, nc, img_size, yolo_idx):
        super(YOLOLayer, self).__init__()
        self.anchors = torch.Tensor(anchors)
        self.na = self.anchors.shape[0]  # number of anchors
        self.nc = nc  # number of classes
        self.no = self.nc + 5  # number of outputs per anchor
        self.img_size = img_size
        self.grid_size = 0  # grid size
        self.stride = 0  # stride
        self.grid = self.create_grid(img_size)
        self.scaled_anchors = torch.Tensor([(a_w / self.stride, a_h / self.stride) for a_w, a_h in self.anchors])
        self.anchor_w = self.scaled_anchors[:, 0:1].view((1, self.na, 1, 1))
        self.anchor_h = self.scaled_anchors[:, 1:2].view((1, self.na, 1, 1))
        self.yolo_idx = yolo_idx

    def forward(self, x):
        # Residual block
        x = self.residual(x, 1)
        # Feature extraction
        x = self.extract(x, 2)
        # Pruning
        x = self.prune(x, 3)
        # Detection
        x = self.detect(x, 4)
        return x

    def residual(self, x, n):
        for i in range(n):
            x = self.m[i](x) + x
        return x

    def extract(self, x, n):
        for i in range(n):
            x = self.m[i](x)
        return x

    def prune(self, x, n):
        self.resrep.resrep_prune(threshold=0.1)
        x = self.resrep(x)
        return x

    def detect(self, x, n):
        # Predict on center
        io = x.clone()[:, :, self.grid_size[1] // 2:self.grid_size[1] // 2 + 1,
             self.grid_size[0] // 2:self.grid_size[0] // 2 + 1]
        io[..., 4] += self.grid_x
        io[..., 5] += self.grid_y
        io[..., :2] = self.sigmoid(io[..., :2]) * 2. - 0.5 + self.grid.to(x.device)
        io[..., 2:4] = (self.sigmoid(io[..., 2:4]) * 2) ** 2 * self.anchor_wh[self.anchor_vec == self.yolo_idx]
        io[..., :4] *= self.stride
        return io.view(io.shape[0], -1, self.no), x

class Model(nn.Module):
    def __init__(self, cfg='models/yolov5s.yaml', ch=3, nc=None):
        super(Model, self).__init__()
        self.model, self.save = parse_model(deepcopy(yaml.load(open(cfg, 'r'))))
        self.stride = torch.tensor([32, 16, 8])
        self.ch = ch
        self.nc = nc
        self.hyper_params = self.model.pop('hyper_params')
        self.init_weights()

    def forward(self, x):
        y, dt = [], []
        for i, m in enumerate(self.model):
            x = m(x)
            if i in [2, 4, 6]:
                y.append(x)
                dt.append(None)
        return y, dt

    def init_weights(self):
        # Initialize weights
        for m in self.modules():
            t = type(m)
            if t is Conv:
                pass  # nn.init.kaiming_normal_(m.conv.weight, mode='fan_out', nonlinearity='relu')
            elif t is DWConv:
                pass  # nn.init.kaiming_normal_(m.conv.weight, mode='fan_out', nonlinearity='relu')
                m.bn.weight.data.fill_(1.0)
                m.bn.bias.data.fill_(0)
            elif t is nn.BatchNorm2d:
                m.eps = 1e-3
                m.momentum = 0.03

    def prune(self, threshold):
        # Apply ResRep pruning to the model
        pruned_idx = []
        for i, m in enumerate(self.model):
            if isinstance(m, Bottleneck):
                if m.bn3.weight is not None:
                    mask = m.bn3.weight.data.abs().ge(threshold).float().cuda()
                    m.bn3.weight.data.mul_(mask)
                    m.bn3.bias.data.mul_(mask)
                    m.conv3.weight.data.mul_(mask.view(-1, 1, 1, 1))
                    pruned_idx.append(i)
            elif isinstance(m, Conv):
                if m.bn.weight is not None:
                    mask = m.bn.weight.data.abs().ge(threshold).float().cuda()
                    m.bn.weight.data.mul_(mask)
                    m.bn.bias.data.mul_(mask)
                    m.conv.weight.data.mul_(mask.view(-1, 1, 1, 1))
                    pruned_idx.append(i)
            elif isinstance(m, YOLOLayer):
                m.resrep = ResRep(m.m, self.prune_idx)
                m.resrep.resrep_prune(threshold=0.1)
                pruned_idx.append(i)
        self.prune_idx = max(pruned_idx)

    def fuse(self):
        # Fuse Conv+BN and Conv+ReLU into Conv
        print('Fusing layers...')
        for m in self.modules():
            if type(m) is Conv and type(m.bn) is nn.BatchNorm2d:
                m.conv = fuse_conv_bn(m.conv, m.bn)
                delattr(m, 'bn')
            elif type(m) is nn.Sequential:
                for i, v in enumerate(m):
                    if type(v) is Conv and type(v.bn) is nn.BatchNorm2d:
                        v.conv = fuse_conv_bn(v.conv, v.bn)
                        delattr(v, 'bn')
                    elif type(v) is Conv and hasattr(m[i + 1], 'act'):
                        v.conv = fuse_conv_relu(v.conv, m[i + 1].act)
                        delattr(m[i + 1], 'act')
                    elif type(v) is nn.BatchNorm2d and hasattr(m[i + 1], 'act'):
                        delattr(m[i + 1], 'act')
            elif type(m) is nn.BatchNorm2d:
                if not hasattr(m, 'act'):
                    m.act = nn.ReLU(inplace=True)

    def info(self, verbose=False):
        # Print model information
        model_info(self, verbose)

def parse_model(d, ch=3, nc=None):  # model_dict, input_channels, num_classes
    anchors, nc = d['anchors'], d['nc']
    na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors
    layers, save, c2 = [], [], ch
    for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']):
        m = eval(m) if isinstance(m, str) else m  # eval strings
        for j, a in enumerate(args):
            try:
                args[j] = eval(a) if isinstance(a, str) else a  # eval strings
            except:
                pass
        n = '' if n == 1 else n
        if m in [Conv, DWConv, Focus, Bottleneck, SPP, Concat, Detect]:
            c1, c2 = c2, args[0]
            if isinstance(c2, list):
                c2 = [ch] + c2
            elif c2 == 'same':
                c2 = c1
            elif c2 == -1:
                c2 = [256, 512, 1024, 2048][max(2 + i - len(d['backbone']), 0)]
            elif c2 == -2:
                c2 = c1 // 2
            elif c2 == -3:
                c2 = c1 // 3
            elif c2 == -4:
                c2 = c1 // 4
            else:
                c2 = int(c2)
            args = [c1, c2, *args[1:]]
            if m in [Bottleneck, SPP]:
                args.insert(2, n)
                n = ''
        elif m is nn.BatchNorm2d:
            args = [c2]
        elif m is nn.Upsample:
            if isinstance(args[0], str):
                args = [f'{c2 * int(args[0])}']
            else:
                args *= 2
        elif m is nn.Linear:
            args = [nc, args[0]]
            if n == 'head':
                args[0] = args[0] * na * (nc + 5)
                n = ''
        elif m is Detect:
            args.append([anchors[i] for i in d['anchor_idx'][f]])
            args.append(nc)
            args.append(f)
        else:
            print(f'Warning: Unrecognized layer string: {m}')
        if isinstance(c2, list):
            c2 = c2[-1]
        module = nn.Sequential(*[m(*args) if m is not Detect else Detect(*args[:3]).to(f'cuda:{args[3]}') for m in [m]])
        module.nc = nc  # attach number of classes to Detect()
        module.stride = torch.tensor([2 ** i for i in range(10)])[[f, f - 1, f - 2]]  # strides computed during construction
        module.anchor_vec = d['anchor_idx'][f]
        module.training = False
        layers.append(module)
        if n:
            save.append(n)
    return nn.Sequential(*layers), sorted(save)

def fuse_conv_bn(conv, bn):
    # https://tehnokv.com/posts/fusing-batchnorm-and-conv/
    with torch.no_grad():
        # init
        fusedconv = Conv(
            conv.in_channels,
            conv.out_channels,
            kernel_size=conv.kernel_size,
            stride=conv.stride,
            padding=conv.padding,
            groups=conv.groups,
            bias=True,
            dilation=conv.dilation)
        fusedconv.weight.data = conv.weight.data.clone().reshape(
            fusedconv.weight.data.shape)  # copy conv weights

        # prepare filters
        bnmean = bn.running_mean
        bnstd = torch.sqrt(bn.running_var + bn.eps)
        if conv.groups > 1:
            # reshape (out_c, in_c // groups, kH, kW) -> (out_c * groups, in_c // groups, kH, kW)
            conv_weight_groups = conv.weight.data.reshape(
                conv.out_channels * conv.groups, -1, conv.kernel_size[0], conv.kernel_size[1])
            # reshape bn params (out_c) -> (out_c * groups)
            bnmean = bnmean.repeat(conv.groups)
            bnstd = bnstd.repeat(conv.groups)
        else:
            conv_weight_groups = conv.weight.data

        # fuse
        fusedconv.bias.data = bn.bias.data + (bn.weight.data / bnstd) * (conv.bias.data - bnmean)
        scale = (bn.weight.data / bnstd)
        fusedconv.weight.data *= scale.reshape(-1, 1, 1, 1)

        return fusedconv

def fuse_conv_relu(conv, relu):
    # Fuse Conv+ReLU into Conv
    with torch.no_grad():
        # init
        fusedconv = Conv(
            conv.in_channels,
            conv.out_channels,
            kernel_size=conv.kernel_size,
            stride=conv.stride,
            padding=conv.padding,
            groups=conv.groups,
            bias=True,
            dilation=conv.dilation)
        fusedconv.weight.data = conv.weight.data.clone().reshape(
            fusedconv.weight.data.shape)  # copy conv weights

        # fuse
        fusedconv.bias.data = conv.bias.data
        fusedconv.weight.data *= relu.inplace_slope.reshape(-1, 1, 1, 1)

        return fusedconv

6. 在 `train.py` 文件中，添加 ResRep 剪枝方法的调用。

# ResRep pruning
if epoch == 100:
    model.prune(threshold=0.1)
    print(f'Pruned model to {count_parameters(model)[0] / 1e6:.3g}M parameters')

7. 运行训练命令，开始训练。

python train.py --img 640 --batch 16 --epochs 300 --data coco.yaml --cfg models/yolov5s.yaml --weights '' --name yolov5s_resrep

完成以上步骤后，即可得到应用了 ResRep 剪枝方法的 YOLOv5 6.1 版本的模型，并进行训练。