PyTorch 常用代码段整理合集

小灰灰 2023-10-12 07:57 10阅读 0赞

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

## **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 1]T.-Y. Lin, A. RoyChowdhury, and S. Maji. Bilinear CNN models for fine-grained visual recognition. In ICCV, 2015.
2.  [^][Link 2]Y. Chen, Y. Bai, W. Zhang, and T. Mei. Destruction and construction learning for fine-grained image recognition. In CVPR, 2019.
3.  [^][Link 3]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 4]C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna: Rethinking the Inception architecture for computer vision. In CVPR, 2016.
5.  [^][Link 5]H. Zhang, M. Cissé, Y. N. Dauphin, and D. Lopez-Paz. mixup: Beyond empirical risk minimization. In ICLR, 2018.

[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
[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 1]: https://zhuanlan.zhihu.com/p/59205847#ref_1_0
[Link 2]: https://zhuanlan.zhihu.com/p/59205847#ref_2_0
[Link 3]: https://zhuanlan.zhihu.com/p/59205847#ref_3_0
[Link 4]: https://zhuanlan.zhihu.com/p/59205847#ref_4_0
[Link 5]: https://zhuanlan.zhihu.com/p/59205847#ref_5_0