PyTorch 常用代码段整理合集

╰+哭是因爲堅強的太久メ 2024-04-17 06:04 30阅读 0赞

[https://zhuanlan.zhihu.com/p/59205847][https_zhuanlan.zhihu.com_p_59205847]

本文代码基于PyTorch 1.0版本，需要用到以下包

import collections
    import os
    import shutil
    import tqdm
    
    import numpy as np
    import PIL.Image
    import torch
    import torchvision

## **1. 基础配置** ##

**检查PyTorch版本**

torch.__version__               # PyTorch version
    torch.version.cuda              # Corresponding CUDA version
    torch.backends.cudnn.version()  # Corresponding cuDNN version
    torch.cuda.get_device_name(0)   # GPU type

**更新PyTorch**

PyTorch将被安装在anaconda3/lib/python3.7/site-packages/torch/目录下。

conda update pytorch torchvision -c pytorch

**固定随机种子**

torch.manual_seed(0)
    torch.cuda.manual_seed_all(0)

**指定程序运行在特定GPU卡上**

在命令行指定环境变量

CUDA_VISIBLE_DEVICES=0,1 python train.py

或在代码中指定

os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'

**判断是否有CUDA支持**

torch.cuda.is_available()

**设置为cuDNN benchmark模式**

Benchmark模式会提升计算速度，但是由于计算中有随机性，每次网络前馈结果略有差异。

torch.backends.cudnn.benchmark = True

如果想要避免这种结果波动，设置

torch.backends.cudnn.deterministic = True

**清除GPU存储**

有时Control-C中止运行后GPU存储没有及时释放，需要手动清空。在PyTorch内部可以

torch.cuda.empty_cache()

或在命令行可以先使用ps找到程序的PID，再使用kill结束该进程

ps aux | grep python
    kill -9 [pid]

或者直接重置没有被清空的GPU

nvidia-smi --gpu-reset -i [gpu_id]

## **2. 张量处理** ##

**张量基本信息**

tensor.type()   # Data type
    tensor.size()   # Shape of the tensor. It is a subclass of Python tuple
    tensor.dim()    # Number of dimensions.

**数据类型转换**

# Set default tensor type. Float in PyTorch is much faster than double.
    torch.set_default_tensor_type(torch.FloatTensor)
    
    # Type convertions.
    tensor = tensor.cuda()
    tensor = tensor.cpu()
    tensor = tensor.float()
    tensor = tensor.long()

**torch.Tensor与np.ndarray转换**

# torch.Tensor -> np.ndarray.
    ndarray = tensor.cpu().numpy()
    
    # np.ndarray -> torch.Tensor.
    tensor = torch.from_numpy(ndarray).float()
    tensor = torch.from_numpy(ndarray.copy()).float()  # If ndarray has negative stride

**torch.Tensor与PIL.Image转换**

PyTorch中的张量默认采用N×D×H×W的顺序，并且数据范围在\[0, 1\]，需要进行转置和规范化。

# torch.Tensor -> PIL.Image.
    image = PIL.Image.fromarray(torch.clamp(tensor * 255, min=0, max=255
        ).byte().permute(1, 2, 0).cpu().numpy())
    image = torchvision.transforms.functional.to_pil_image(tensor)  # Equivalently way
    
    # PIL.Image -> torch.Tensor.
    tensor = torch.from_numpy(np.asarray(PIL.Image.open(path))
        ).permute(2, 0, 1).float() / 255
    tensor = torchvision.transforms.functional.to_tensor(PIL.Image.open(path))  # Equivalently way

**np.ndarray与PIL.Image转换**

# np.ndarray -> PIL.Image.
    image = PIL.Image.fromarray(ndarray.astypde(np.uint8))
    
    # PIL.Image -> np.ndarray.
    ndarray = np.asarray(PIL.Image.open(path))

**从只包含一个元素的张量中提取值**

这在训练时统计loss的变化过程中特别有用。否则这将累积计算图，使GPU存储占用量越来越大。

value = tensor.item()

**张量形变**

张量形变常常需要用于将卷积层特征输入全连接层的情形。相比torch.view，torch.reshape可以自动处理输入张量不连续的情况。

tensor = torch.reshape(tensor, shape)

**打乱顺序**

tensor = tensor[torch.randperm(tensor.size(0))]  # Shuffle the first dimension

**水平翻转**

PyTorch不支持tensor\[::-1\]这样的负步长操作，水平翻转可以用张量索引实现。

# Assume tensor has shape N*D*H*W.
    tensor = tensor[:, :, :, torch.arange(tensor.size(3) - 1, -1, -1).long()]

**复制张量**

有三种复制的方式，对应不同的需求。

# Operation                 |  New/Shared memory | Still in computation graph |
    tensor.clone()            # |        New         |          Yes               |
    tensor.detach()           # |      Shared        |          No                |
    tensor.detach.clone()()   # |        New         |          No                |

**拼接张量**

注意torch.cat和torch.stack的区别在于torch.cat沿着给定的维度拼接，而torch.stack会新增一维。例如当参数是3个10×5的张量，torch.cat的结果是30×5的张量，而torch.stack的结果是3×10×5的张量。

tensor = torch.cat(list_of_tensors, dim=0)
    tensor = torch.stack(list_of_tensors, dim=0)

**将整数标记转换成独热（one-hot）编码**

PyTorch中的标记默认从0开始。

N = tensor.size(0)
    one_hot = torch.zeros(N, num_classes).long()
    one_hot.scatter_(dim=1, index=torch.unsqueeze(tensor, dim=1), src=torch.ones(N, num_classes).long())

**得到非零/零元素**

torch.nonzero(tensor)               # Index of non-zero elements
    torch.nonzero(tensor == 0)          # Index of zero elements
    torch.nonzero(tensor).size(0)       # Number of non-zero elements
    torch.nonzero(tensor == 0).size(0)  # Number of zero elements

**判断两个张量相等**

torch.allclose(tensor1, tensor2)  # float tensor
    torch.equal(tensor1, tensor2)     # int tensor

**张量扩展**

# Expand tensor of shape 64*512 to shape 64*512*7*7.
    torch.reshape(tensor, (64, 512, 1, 1)).expand(64, 512, 7, 7)

**矩阵乘法**

# Matrix multiplication: (m*n) * (n*p) -> (m*p).
    result = torch.mm(tensor1, tensor2)
    
    # Batch matrix multiplication: (b*m*n) * (b*n*p) -> (b*m*p).
    result = torch.bmm(tensor1, tensor2)
    
    # Element-wise multiplication.
    result = tensor1 * tensor2

**计算两组数据之间的两两欧式距离**

# X1 is of shape m*d, X2 is of shape n*d.
    dist = torch.sqrt(torch.sum((X1[:,None,:] - X2) ** 2, dim=2))

## **3. 模型定义** ##

**卷积层**

最常用的卷积层配置是

conv = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=True)
    conv = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=True)

如果卷积层配置比较复杂，不方便计算输出大小时，可以利用如下可视化工具辅助

[Convolution Visualizerezyang.github.io][Convolution Visualizer_ezyang.github.io]

**GAP（Global average pooling）层**

gap = torch.nn.AdaptiveAvgPool2d(output_size=1)

**双线性汇合（bilinear pooling）**[\[1\]][1]

X = torch.reshape(N, D, H * W)                        # Assume X has shape N*D*H*W
    X = torch.bmm(X, torch.transpose(X, 1, 2)) / (H * W)  # Bilinear pooling
    assert X.size() == (N, D, D)
    X = torch.reshape(X, (N, D * D))
    X = torch.sign(X) * torch.sqrt(torch.abs(X) + 1e-5)   # Signed-sqrt normalization
    X = torch.nn.functional.normalize(X)                  # L2 normalization

**多卡同步BN（Batch normalization）**

当使用torch.nn.DataParallel将代码运行在多张GPU卡上时，PyTorch的BN层默认操作是各卡上数据独立地计算均值和标准差，同步BN使用所有卡上的数据一起计算BN层的均值和标准差，缓解了当批量大小（batch size）比较小时对均值和标准差估计不准的情况，是在目标检测等任务中一个有效的提升性能的技巧。

[vacancy/Synchronized-BatchNorm-PyTorchgithub.com![图标][format_png]][vacancy_Synchronized-BatchNorm-PyTorch_github.com_format_png]

现在PyTorch官方已经支持同步BN操作

sync_bn = torch.nn.SyncBatchNorm(num_features, eps=1e-05, momentum=0.1, affine=True, 
                                     track_running_stats=True)

将已有网络的所有BN层改为同步BN层

def convertBNtoSyncBN(module, process_group=None):
        '''Recursively replace all BN layers to SyncBN layer.
    
        Args:
            module[torch.nn.Module]. Network
        '''
        if isinstance(module, torch.nn.modules.batchnorm._BatchNorm):
            sync_bn = torch.nn.SyncBatchNorm(module.num_features, module.eps, module.momentum, 
                                             module.affine, module.track_running_stats, process_group)
            sync_bn.running_mean = module.running_mean
            sync_bn.running_var = module.running_var
            if module.affine:
                sync_bn.weight = module.weight.clone().detach()
                sync_bn.bias = module.bias.clone().detach()
            return sync_bn
        else:
            for name, child_module in module.named_children():
                setattr(module, name) = convert_syncbn_model(child_module, process_group=process_group))
            return module

**类似BN滑动平均**

如果要实现类似BN滑动平均的操作，在forward函数中要使用原地（inplace）操作给滑动平均赋值。

class BN(torch.nn.Module)
        def __init__(self):
            ...
            self.register_buffer('running_mean', torch.zeros(num_features))
    
        def forward(self, X):
            ...
            self.running_mean += momentum * (current - self.running_mean)

**计算模型整体参数量**

num_parameters = sum(torch.numel(parameter) for parameter in model.parameters())

**类似Keras的model.summary()输出模型信息**

[sksq96/pytorch-summarygithub.com![图标][format_png 1]][sksq96_pytorch-summary_github.com_format_png 1]

**模型权值初始化**

注意model.modules()和model.children()的区别：model.modules()会迭代地遍历模型的所有子层，而model.children()只会遍历模型下的一层。

# Common practise for initialization.
    for layer in model.modules():
        if isinstance(layer, torch.nn.Conv2d):
            torch.nn.init.kaiming_normal_(layer.weight, mode='fan_out',
                                          nonlinearity='relu')
            if layer.bias is not None:
                torch.nn.init.constant_(layer.bias, val=0.0)
        elif isinstance(layer, torch.nn.BatchNorm2d):
            torch.nn.init.constant_(layer.weight, val=1.0)
            torch.nn.init.constant_(layer.bias, val=0.0)
        elif isinstance(layer, torch.nn.Linear):
            torch.nn.init.xavier_normal_(layer.weight)
            if layer.bias is not None:
                torch.nn.init.constant_(layer.bias, val=0.0)
    
    # Initialization with given tensor.
    layer.weight = torch.nn.Parameter(tensor)

**部分层使用预训练模型**

注意如果保存的模型是torch.nn.DataParallel，则当前的模型也需要是torch.nn.DataParallel。torch.nn.DataParallel(model).module == model。

model.load_state_dict(torch.load('model,pth'), strict=False)

**将在GPU保存的模型加载到CPU**

model.load_state_dict(torch.load('model,pth', map_location='cpu'))

## **4. 数据准备、特征提取与微调** ##

**图像分块打散（image shuffle）/区域混淆机制（region confusion mechanism，RCM）**[\[2\]][2]

# X is torch.Tensor of size N*D*H*W.
    # Shuffle rows
    Q = (torch.unsqueeze(torch.arange(num_blocks), dim=1) * torch.ones(1, num_blocks).long()
         + torch.randint(low=-neighbour, high=neighbour, size=(num_blocks, num_blocks)))
    Q = torch.argsort(Q, dim=0)
    assert Q.size() == (num_blocks, num_blocks)
    
    X = [torch.chunk(row, chunks=num_blocks, dim=2)
         for row in torch.chunk(X, chunks=num_blocks, dim=1)]
    X = [[X[Q[i, j].item()][j] for j in range(num_blocks)]
         for i in range(num_blocks)]
    
    # Shulle columns.
    Q = (torch.ones(num_blocks, 1).long() * torch.unsqueeze(torch.arange(num_blocks), dim=0)
         + torch.randint(low=-neighbour, high=neighbour, size=(num_blocks, num_blocks)))
    Q = torch.argsort(Q, dim=1)
    assert Q.size() == (num_blocks, num_blocks)
    X = [[X[i][Q[i, j].item()] for j in range(num_blocks)]
         for i in range(num_blocks)]
    
    Y = torch.cat([torch.cat(row, dim=2) for row in X], dim=1)

**得到视频数据基本信息**

import cv2
    video = cv2.VideoCapture(mp4_path)
    height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
    width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
    num_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
    fps = int(video.get(cv2.CAP_PROP_FPS))
    video.release()

**TSN每段（segment）采样一帧视频**[\[3\]][3]

K = self._num_segments
    if is_train:
        if num_frames > K:
            # Random index for each segment.
            frame_indices = torch.randint(
                high=num_frames // K, size=(K,), dtype=torch.long)
            frame_indices += num_frames // K * torch.arange(K)
        else:
            frame_indices = torch.randint(
                high=num_frames, size=(K - num_frames,), dtype=torch.long)
            frame_indices = torch.sort(torch.cat((
                torch.arange(num_frames), frame_indices)))[0]
    else:
        if num_frames > K:
            # Middle index for each segment.
            frame_indices = num_frames / K // 2
            frame_indices += num_frames // K * torch.arange(K)
        else:
            frame_indices = torch.sort(torch.cat((                              
                torch.arange(num_frames), torch.arange(K - num_frames))))[0]
    assert frame_indices.size() == (K,)
    return [frame_indices[i] for i in range(K)]

**提取ImageNet预训练模型某层的卷积特征**

# VGG-16 relu5-3 feature.
    model = torchvision.models.vgg16(pretrained=True).features[:-1]
    # VGG-16 pool5 feature.
    model = torchvision.models.vgg16(pretrained=True).features
    # VGG-16 fc7 feature.
    model = torchvision.models.vgg16(pretrained=True)
    model.classifier = torch.nn.Sequential(*list(model.classifier.children())[:-3])
    # ResNet GAP feature.
    model = torchvision.models.resnet18(pretrained=True)
    model = torch.nn.Sequential(collections.OrderedDict(
        list(model.named_children())[:-1]))
    
    with torch.no_grad():
        model.eval()
        conv_representation = model(image)

**提取ImageNet预训练模型多层的卷积特征**

class FeatureExtractor(torch.nn.Module):
        """Helper class to extract several convolution features from the given
        pre-trained model.
    
        Attributes:
            _model, torch.nn.Module.
            _layers_to_extract, list<str> or set<str>
    
        Example:
            >>> model = torchvision.models.resnet152(pretrained=True)
            >>> model = torch.nn.Sequential(collections.OrderedDict(
                    list(model.named_children())[:-1]))
            >>> conv_representation = FeatureExtractor(
                    pretrained_model=model,
                    layers_to_extract={'layer1', 'layer2', 'layer3', 'layer4'})(image)
        """
        def __init__(self, pretrained_model, layers_to_extract):
            torch.nn.Module.__init__(self)
            self._model = pretrained_model
            self._model.eval()
            self._layers_to_extract = set(layers_to_extract)
        
        def forward(self, x):
            with torch.no_grad():
                conv_representation = []
                for name, layer in self._model.named_children():
                    x = layer(x)
                    if name in self._layers_to_extract:
                        conv_representation.append(x)
                return conv_representation

**其他预训练模型**

[Cadene/pretrained-models.pytorchgithub.com![图标][format_png 2]][Cadene_pretrained-models.pytorch_github.com_format_png 2]

**微调全连接层**

model = torchvision.models.resnet18(pretrained=True)
    for param in model.parameters():
        param.requires_grad = False
    model.fc = nn.Linear(512, 100)  # Replace the last fc layer
    optimizer = torch.optim.SGD(model.fc.parameters(), lr=1e-2, momentum=0.9, weight_decay=1e-4)

**以较大学习率微调全连接层，较小学习率微调卷积层**

model = torchvision.models.resnet18(pretrained=True)
    finetuned_parameters = list(map(id, model.fc.parameters()))
    conv_parameters = (p for p in model.parameters() if id(p) not in finetuned_parameters)
    parameters = [{'params': conv_parameters, 'lr': 1e-3}, 
                  {'params': model.fc.parameters()}]
    optimizer = torch.optim.SGD(parameters, lr=1e-2, momentum=0.9, weight_decay=1e-4)

## **5. 模型训练** ##

**常用训练和验证数据预处理**

其中ToTensor操作会将PIL.Image或形状为H×W×D，数值范围为\[0, 255\]的np.ndarray转换为形状为D×H×W，数值范围为\[0.0, 1.0\]的torch.Tensor。

train_transform = torchvision.transforms.Compose([
        torchvision.transforms.RandomResizedCrop(size=224,
                                                 scale=(0.08, 1.0)),
        torchvision.transforms.RandomHorizontalFlip(),
        torchvision.transforms.ToTensor(),
        torchvision.transforms.Normalize(mean=(0.485, 0.456, 0.406),
                                         std=(0.229, 0.224, 0.225)),
     ])
     val_transform = torchvision.transforms.Compose([
        torchvision.transforms.Resize(256),
        torchvision.transforms.CenterCrop(224),
        torchvision.transforms.ToTensor(),
        torchvision.transforms.Normalize(mean=(0.485, 0.456, 0.406),
                                         std=(0.229, 0.224, 0.225)),
    ])

**训练基本代码框架**

for t in epoch(80):
        for images, labels in tqdm.tqdm(train_loader, desc='Epoch %3d' % (t + 1)):
            images, labels = images.cuda(), labels.cuda()
            scores = model(images)
            loss = loss_function(scores, labels)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

**标记平滑（label smoothing）**[\[4\]][4]

for images, labels in train_loader:
        images, labels = images.cuda(), labels.cuda()
        N = labels.size(0)
        # C is the number of classes.
        smoothed_labels = torch.full(size=(N, C), fill_value=0.1 / (C - 1)).cuda()
        smoothed_labels.scatter_(dim=1, index=torch.unsqueeze(labels, dim=1), value=0.9)
    
        score = model(images)
        log_prob = torch.nn.functional.log_softmax(score, dim=1)
        loss = -torch.sum(log_prob * smoothed_labels) / N
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

**Mixup**[\[5\]][5]

beta_distribution = torch.distributions.beta.Beta(alpha, alpha)
    for images, labels in train_loader:
        images, labels = images.cuda(), labels.cuda()
    
        # Mixup images.
        lambda_ = beta_distribution.sample([]).item()
        index = torch.randperm(images.size(0)).cuda()
        mixed_images = lambda_ * images + (1 - lambda_) * images[index, :]
    
        # Mixup loss.    
        scores = model(mixed_images)
        loss = (lambda_ * loss_function(scores, labels) 
                + (1 - lambda_) * loss_function(scores, labels[index]))
    
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

**L1正则化**

l1_regularization = torch.nn.L1Loss(reduction='sum')
    loss = ...  # Standard cross-entropy loss
    for param in model.parameters():
        loss += lambda_ * torch.sum(torch.abs(param))
    loss.backward()

**不对偏置项进行L2正则化/权值衰减（weight decay）**

bias_list = (param for name, param in model.named_parameters() if name[-4:] == 'bias')
    others_list = (param for name, param in model.named_parameters() if name[-4:] != 'bias')
    parameters = [{'parameters': bias_list, 'weight_decay': 0},                
                  {'parameters': others_list}]
    optimizer = torch.optim.SGD(parameters, lr=1e-2, momentum=0.9, weight_decay=1e-4)

**梯度裁剪（gradient clipping）**

torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=20)

**计算Softmax输出的准确率**

score = model(images)
    prediction = torch.argmax(score, dim=1)
    num_correct = torch.sum(prediction == labels).item()
    accuruacy = num_correct / labels.size(0)

**可视化模型前馈的计算图**

[szagoruyko/pytorchvizgithub.com![图标][format_png 3]][szagoruyko_pytorchviz_github.com_format_png 3]

**可视化学习曲线**

有Facebook自己开发的Visdom和Tensorboard（仍处于实验阶段）两个选择。

[facebookresearch/visdomgithub.com![图标][format_png 4]][facebookresearch_visdom_github.com_format_png 4][torch.utils.tensorboard - PyTorch master documentationpytorch.org][torch.utils.tensorboard - PyTorch master documentation_pytorch.org]

# Example using Visdom.
    vis = visdom.Visdom(env='Learning curve', use_incoming_socket=False)
    assert self._visdom.check_connection()
    self._visdom.close()
    options = collections.namedtuple('Options', ['loss', 'acc', 'lr'])(
        loss={'xlabel': 'Epoch', 'ylabel': 'Loss', 'showlegend': True},
        acc={'xlabel': 'Epoch', 'ylabel': 'Accuracy', 'showlegend': True},
        lr={'xlabel': 'Epoch', 'ylabel': 'Learning rate', 'showlegend': True})
    
    for t in epoch(80):
        tran(...)
        val(...)
        vis.line(X=torch.Tensor([t + 1]), Y=torch.Tensor([train_loss]),
                 name='train', win='Loss', update='append', opts=options.loss)
        vis.line(X=torch.Tensor([t + 1]), Y=torch.Tensor([val_loss]),
                 name='val', win='Loss', update='append', opts=options.loss)
        vis.line(X=torch.Tensor([t + 1]), Y=torch.Tensor([train_acc]),
                 name='train', win='Accuracy', update='append', opts=options.acc)
        vis.line(X=torch.Tensor([t + 1]), Y=torch.Tensor([val_acc]),
                 name='val', win='Accuracy', update='append', opts=options.acc)
        vis.line(X=torch.Tensor([t + 1]), Y=torch.Tensor([lr]),
                 win='Learning rate', update='append', opts=options.lr)

**得到当前学习率**

# If there is one global learning rate (which is the common case).
    lr = next(iter(optimizer.param_groups))['lr']
    
    # If there are multiple learning rates for different layers.
    all_lr = []
    for param_group in optimizer.param_groups:
        all_lr.append(param_group['lr'])

**学习率衰减**

# Reduce learning rate when validation accuarcy plateau.
    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='max', patience=5, verbose=True)
    for t in range(0, 80):
        train(...); val(...)
        scheduler.step(val_acc)
    
    # Cosine annealing learning rate.
    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=80)
    # Reduce learning rate by 10 at given epochs.
    scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[50, 70], gamma=0.1)
    for t in range(0, 80):
        scheduler.step()    
        train(...); val(...)
    
    # Learning rate warmup by 10 epochs.
    scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda t: t / 10)
    for t in range(0, 10):
        scheduler.step()
        train(...); val(...)

**保存与加载断点**

注意为了能够恢复训练，我们需要同时保存模型和优化器的状态，以及当前的训练轮数。

# Save checkpoint.
    is_best = current_acc > best_acc
    best_acc = max(best_acc, current_acc)
    checkpoint = {
        'best_acc': best_acc,    
        'epoch': t + 1,
        'model': model.state_dict(),
        'optimizer': optimizer.state_dict(),
    }
    model_path = os.path.join('model', 'checkpoint.pth.tar')
    torch.save(checkpoint, model_path)
    if is_best:
        shutil.copy('checkpoint.pth.tar', model_path)
    
    # Load checkpoint.
    if resume:
        model_path = os.path.join('model', 'checkpoint.pth.tar')
        assert os.path.isfile(model_path)
        checkpoint = torch.load(model_path)
        best_acc = checkpoint['best_acc']
        start_epoch = checkpoint['epoch']
        model.load_state_dict(checkpoint['model'])
        optimizer.load_state_dict(checkpoint['optimizer'])
        print('Load checkpoint at epoch %d.' % start_epoch)

**计算准确率、查准率（precision）、查全率（recall）**

# data['label'] and data['prediction'] are groundtruth label and prediction 
    # for each image, respectively.
    accuracy = np.mean(data['label'] == data['prediction']) * 100
    
    # Compute recision and recall for each class.
    for c in range(len(num_classes)):
        tp = np.dot((data['label'] == c).astype(int),
                    (data['prediction'] == c).astype(int))
        tp_fp = np.sum(data['prediction'] == c)
        tp_fn = np.sum(data['label'] == c)
        precision = tp / tp_fp * 100
        recall = tp / tp_fn * 100

## 6. 模型测试 ##

**计算每个类别的查准率（precision）、查全率（recall）、F1和总体指标**

import sklearn.metrics
    
    all_label = []
    all_prediction = []
    for images, labels in tqdm.tqdm(data_loader):
         # Data.
         images, labels = images.cuda(), labels.cuda()
         
         # Forward pass.
         score = model(images)
         
         # Save label and predictions.
         prediction = torch.argmax(score, dim=1)
         all_label.append(labels.cpu().numpy())
         all_prediction.append(prediction.cpu().numpy())
    
    # Compute RP and confusion matrix.
    all_label = np.concatenate(all_label)
    assert len(all_label.shape) == 1
    all_prediction = np.concatenate(all_prediction)
    assert all_label.shape == all_prediction.shape
    micro_p, micro_r, micro_f1, _ = sklearn.metrics.precision_recall_fscore_support(
         all_label, all_prediction, average='micro', labels=range(num_classes))
    class_p, class_r, class_f1, class_occurence = sklearn.metrics.precision_recall_fscore_support(
         all_label, all_prediction, average=None, labels=range(num_classes))
    # Ci,j = #{y=i and hat_y=j}
    confusion_mat = sklearn.metrics.confusion_matrix(
         all_label, all_prediction, labels=range(num_classes))
    assert confusion_mat.shape == (num_classes, num_classes)

**将各类结果写入电子表格**

import csv
    
    # Write results onto disk.
    with open(os.path.join(path, filename), 'wt', encoding='utf-8') as f:
         f = csv.writer(f)
         f.writerow(['Class', 'Label', '# occurence', 'Precision', 'Recall', 'F1',
                     'Confused class 1', 'Confused class 2', 'Confused class 3',
                     'Confused 4', 'Confused class 5'])
         for c in range(num_classes):
             index = np.argsort(confusion_mat[:, c])[::-1][:5]
             f.writerow([
                 label2class[c], c, class_occurence[c], '%4.3f' % class_p[c],
                     '%4.3f' % class_r[c], '%4.3f' % class_f1[c],
                     '%s:%d' % (label2class[index[0]], confusion_mat[index[0], c]),
                     '%s:%d' % (label2class[index[1]], confusion_mat[index[1], c]),
                     '%s:%d' % (label2class[index[2]], confusion_mat[index[2], c]),
                     '%s:%d' % (label2class[index[3]], confusion_mat[index[3], c]),
                     '%s:%d' % (label2class[index[4]], confusion_mat[index[4], c])])
             f.writerow(['All', '', np.sum(class_occurence), micro_p, micro_r, micro_f1, 
                         '', '', '', '', ''])

## **7. PyTorch其他注意事项** ##

模型定义

*  建议有参数的层和汇合（pooling）层使用torch.nn模块定义，激活函数直接使用torch.nn.functional。torch.nn模块和torch.nn.functional的区别在于，torch.nn模块在计算时底层调用了torch.nn.functional，但torch.nn模块包括该层参数，还可以应对训练和测试两种网络状态。使用torch.nn.functional时要注意网络状态，如

def forward(self, x):
        ...
        x = torch.nn.functional.dropout(x, p=0.5, training=self.training)

*  model(x)前用model.train()和model.eval()切换网络状态。
 *  不需要计算梯度的代码块用with torch.no\_grad()包含起来。model.eval()和torch.no*\_*grad()的区别在于，model.eval()是将网络切换为测试状态，例如BN和随机失活（dropout）在训练和测试阶段使用不同的计算方法。torch.no\_grad()是关闭PyTorch张量的自动求导机制，以减少存储使用和加速计算，得到的结果无法进行loss.backward()。
 *  torch.nn.CrossEntropyLoss的输入不需要经过Softmax。torch.nn.CrossEntropyLoss等价于torch.nn.functional.log\_softmax + torch.nn.NLLLoss。
 *  loss.backward()前用optimizer.zero\_grad()清除累积梯度。optimizer.zero\_grad()和model.zero\_grad()效果一样。

PyTorch性能与调试

*  torch.utils.data.DataLoader中尽量设置pin\_memory=True，对特别小的数据集如MNIST设置pin\_memory=False反而更快一些。num\_workers的设置需要在实验中找到最快的取值。
 *  用del及时删除不用的中间变量，节约GPU存储。
 *  使用inplace操作可节约GPU存储，如

x = torch.nn.functional.relu(x, inplace=True)

此外，还可以通过torch.utils.checkpoint前向传播时只保留一部分中间结果来节约GPU存储使用，在反向传播时需要的内容从最近中间结果中计算得到。

*  减少CPU和GPU之间的数据传输。例如如果你想知道一个epoch中每个mini-batch的loss和准确率，先将它们累积在GPU中等一个epoch结束之后一起传输回CPU会比每个mini-batch都进行一次GPU到CPU的传输更快。
 *  使用半精度浮点数half()会有一定的速度提升，具体效率依赖于GPU型号。需要小心数值精度过低带来的稳定性问题。
 *  时常使用assert tensor.size() == (N, D, H, W)作为调试手段，确保张量维度和你设想中一致。
 *  除了标记y外，尽量少使用一维张量，使用n\*1的二维张量代替，可以避免一些意想不到的一维张量计算结果。
 *  统计代码各部分耗时

with torch.autograd.profiler.profile(enabled=True, use_cuda=False) as profile:
        ...
    print(profile)

或者在命令行运行

python -m torch.utils.bottleneck main.py

## **致谢** ##

感谢

[些许流年][Link 1]

、

[El tnoto][]

、

[FlyCharles][]

的勘误，感谢

[oatmeal][]

提供的更简洁的方法。由于作者才疏学浅，更兼时间和精力所限，代码中错误之处在所难免，敬请读者批评指正。

## **参考资料** ##

*  PyTorch官方代码：[pytorch/examples][pytorch_examples]
 *  PyTorch论坛：[PyTorch Forums][]
 *  PyTorch文档：[http://pytorch.org/docs/stable/index.html][http_pytorch.org_docs_stable_index.html]
 *  其他基于PyTorch的公开实现代码，无法一一列举

## 参考 ##

1.  [^][Link 2]T.-Y. Lin, A. RoyChowdhury, and S. Maji. Bilinear CNN models for fine-grained visual recognition. In ICCV, 2015.
2.  [^][Link 3]Y. Chen, Y. Bai, W. Zhang, and T. Mei. Destruction and construction learning for fine-grained image recognition. In CVPR, 2019.
3.  [^][Link 4]L. Wang, Y. Xiong, Z. Wang, Y. Qiao, D. Lin, X. Tang, and L. V. Gool. Temporal segment networks: Towards good practices for deep action recognition. In ECCV, 2016.
4.  [^][Link 5]C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna: Rethinking the Inception architecture for computer vision. In CVPR, 2016.
5.  [^][Link 6]H. Zhang, M. Cissé, Y. N. Dauphin, and D. Lopez-Paz. mixup: Beyond empirical risk minimization. In ICLR, 2018.

[https_zhuanlan.zhihu.com_p_59205847]: https://zhuanlan.zhihu.com/p/59205847
[Convolution Visualizer_ezyang.github.io]: https://link.zhihu.com/?target=https%3A//ezyang.github.io/convolution-visualizer/index.html
[1]: https://zhuanlan.zhihu.com/p/59205847#ref_1
[format_png]: https://imgconvert.csdnimg.cn/aHR0cHM6Ly9waWMyLnpoaW1nLmNvbS92Mi02YThmNjBmMDZjYzFjYThkMWJkYmEwZTIzZmVjNjJiZF9pcGljby5qcGc?x-oss-process=image/format,png
[vacancy_Synchronized-BatchNorm-PyTorch_github.com_format_png]: https://link.zhihu.com/?target=https%3A//github.com/vacancy/Synchronized-BatchNorm-PyTorch
[format_png 1]: https://imgconvert.csdnimg.cn/aHR0cHM6Ly9waWM0LnpoaW1nLmNvbS92Mi1lYjc5NTA5MDQ2N2FhMjU1ODUyOTQ1OWZkZWQxNGQwM19pcGljby5qcGc?x-oss-process=image/format,png
[sksq96_pytorch-summary_github.com_format_png 1]: https://link.zhihu.com/?target=https%3A//github.com/sksq96/pytorch-summary
[2]: https://zhuanlan.zhihu.com/p/59205847#ref_2
[3]: https://zhuanlan.zhihu.com/p/59205847#ref_3
[format_png 2]: https://imgconvert.csdnimg.cn/aHR0cHM6Ly9waWMxLnpoaW1nLmNvbS92Mi00MTczM2I5ZTg0MWRiM2I5MDkyNzQyYmJhYmNiMDg5Y19pcGljby5qcGc?x-oss-process=image/format,png
[Cadene_pretrained-models.pytorch_github.com_format_png 2]: https://link.zhihu.com/?target=https%3A//github.com/Cadene/pretrained-models.pytorch
[4]: https://zhuanlan.zhihu.com/p/59205847#ref_4
[5]: https://zhuanlan.zhihu.com/p/59205847#ref_5
[format_png 3]: https://imgconvert.csdnimg.cn/aHR0cHM6Ly9waWMxLnpoaW1nLmNvbS92Mi0zZWU1ZWViZWM4NzZhMWMxZmU5NmVkMzM4M2ZjYTJhMF9pcGljby5qcGc?x-oss-process=image/format,png
[szagoruyko_pytorchviz_github.com_format_png 3]: https://link.zhihu.com/?target=https%3A//github.com/szagoruyko/pytorchviz
[format_png 4]: https://imgconvert.csdnimg.cn/aHR0cHM6Ly9waWMxLnpoaW1nLmNvbS92Mi04YTJiZTk5YTY3ZDgwMzZkZjg0NmQ3MjQ2Njc4M2I0MF9pcGljby5qcGc?x-oss-process=image/format,png
[facebookresearch_visdom_github.com_format_png 4]: https://link.zhihu.com/?target=https%3A//github.com/facebookresearch/visdom
[torch.utils.tensorboard - PyTorch master documentation_pytorch.org]: https://link.zhihu.com/?target=https%3A//pytorch.org/docs/stable/tensorboard.html
[Link 1]: https://www.zhihu.com/people/57cabe6d8a13c5fa82cf94df53c6ea76
[El tnoto]: https://www.zhihu.com/people/43893bc9471ec2e531b43c6a1858e5ee
[FlyCharles]: https://www.zhihu.com/people/3951450445ed16090bf3d8bc38472cd2
[oatmeal]: https://www.zhihu.com/people/63ea897503affb2d5552a13498fd6015
[pytorch_examples]: https://link.zhihu.com/?target=https%3A//github.com/pytorch/examples
[PyTorch Forums]: https://link.zhihu.com/?target=https%3A//discuss.pytorch.org/latest%3Forder%3Dviews
[http_pytorch.org_docs_stable_index.html]: https://link.zhihu.com/?target=http%3A//pytorch.org/docs/stable/index.html
[Link 2]: https://zhuanlan.zhihu.com/p/59205847#ref_1_0
[Link 3]: https://zhuanlan.zhihu.com/p/59205847#ref_2_0
[Link 4]: https://zhuanlan.zhihu.com/p/59205847#ref_3_0
[Link 5]: https://zhuanlan.zhihu.com/p/59205847#ref_4_0
[Link 6]: https://zhuanlan.zhihu.com/p/59205847#ref_5_0