PyTorch常用代码段合集

　　作者丨Jack Stark@知乎
　　来源丨https://zhuanlan.zhihu.com/p/104019160
　　PyTorch最好的资料是官方文档。本文是PyTorch常用代码段，在参考资料[1](张皓：PyTorch Cookbook)的基础上做了一些修补，方便使用时查阅。  1. 基本配置导入包和版本查询import torch import torch.nn as nn import torchvision print(torch.__version__) print(torch.version.cuda) print(torch.backends.cudnn.version()) print(torch.cuda.get_device_name(0))可复现性
　　在硬件设备（CPU、GPU）不同时，完全的可复现性无法保证，即使随机种子相同。但是，在同一个设备上，应该保证可复现性。具体做法是，在程序开始的时候固定torch的随机种子，同时也把numpy的随机种子固定。  np.random.seed(0) torch.manual_seed(0) torch.cuda.manual_seed_all(0)   torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False显卡设置
　　如果只需要一张显卡  # Device configuration device = torch.device(＂cuda＂ if torch.cuda.is_available() else ＂cpu＂)
　　如果需要指定多张显卡，比如0，1号显卡。  import os os.environ[＂CUDA_VISIBLE_DEVICES＂] = ＂0,1＂
　　也可以在命令行运行代码时设置显卡：  CUDA_VISIBLE_DEVICES=0,1 python train.py
　　清除显存  torch.cuda.empty_cache()
　　也可以使用在命令行重置GPU的指令  nvidia-smi --gpu-reset -i [gpu_id]2. 张量(Tensor)处理张量的数据类型
　　PyTorch有9种CPU张量类型和9种GPU张量类型。
　　张量基本信息tensor = torch.randn(3,4,5) print(tensor.type())  # 数据类型 print(tensor.size())  # 张量的shape，是个元组 print(tensor.dim())   # 维度的数量命名张量
　　张量命名是一个非常有用的方法，这样可以方便地使用维度的名字来做索引或其他操作，大大提高了可读性、易用性，防止出错。  # 在PyTorch 1.3之前，需要使用注释 # Tensor[N, C, H, W] images = torch.randn(32, 3, 56, 56) images.sum(dim=1) images.select(dim=1, index=0)   # PyTorch 1.3之后 NCHW = [‘N’, ‘C’, ‘H’, ‘W’] images = torch.randn(32, 3, 56, 56, names=NCHW) images.sum(＂C＂) images.select(＂C＂, index=0) # 也可以这么设置 tensor = torch.rand(3,4,1,2,names=(＂C＂, ＂N＂, ＂H＂, ＂W＂)) # 使用align_to可以对维度方便地排序 tensor = tensor.align_to(＂N＂, ＂C＂, ＂H＂, ＂W＂)数据类型转换# 设置默认类型，pytorch中的FloatTensor远远快于DoubleTensor torch.set_default_tensor_type(torch.FloatTensor)   # 类型转换 tensor = tensor.cuda() tensor = tensor.cpu() tensor = tensor.float() tensor = tensor.long()torch.Tensor与np.ndarray转换
　　除了CharTensor，其他所有CPU上的张量都支持转换为numpy格式然后再转换回来。  ndarray = tensor.cpu().numpy() 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, C, 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 path = r＂./figure.jpg＂ 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 waynp.ndarray与PIL.Image的转换image = PIL.Image.fromarray(ndarray.astype(np.uint8))   ndarray = np.asarray(PIL.Image.open(path))从只包含一个元素的张量中提取值value = torch.rand(1).item()张量形变# 在将卷积层输入全连接层的情况下通常需要对张量做形变处理， # 相比torch.view，torch.reshape可以自动处理输入张量不连续的情况。 tensor = torch.rand(2,3,4) shape = (6, 4) tensor = torch.reshape(tensor, shape)打乱顺序tensor = tensor[torch.randperm(tensor.size(0))]  # 打乱第一个维度水平翻转# pytorch不支持tensor[::-1]这样的负步长操作，水平翻转可以通过张量索引实现 # 假设张量的维度为[N, D, H, W]. tensor = tensor[:,:,:,torch.arange(tensor.size(3) - 1, -1, -1).long()]复制张量# Operation                 |  New/Shared memory | Still in computation graph | tensor.clone()            # |        New         |          Yes               | tensor.detach()           # |      Shared        |          No                | tensor.detach.clone()()   # |        New         |          No                |张量拼接＂＂＂ 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10x5的张量，torch.cat的结果是30x5的张量， 而torch.stack的结果是3x10x5的张量。 ＂＂＂ tensor = torch.cat(list_of_tensors, dim=0) tensor = torch.stack(list_of_tensors, dim=0)将整数标签转为one-hot编码# pytorch的标记默认从0开始 tensor = torch.tensor([0, 2, 1, 3]) N = tensor.size(0) num_classes = 4 one_hot = torch.zeros(N, num_classes).long() one_hot.scatter_(dim=1, index=torch.unsqueeze(tensor, dim=1), src=＂/a2020/img/data-img.jpg＂ data-src=torch.ones(N, num_classes).long())得到非零元素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. tensor = torch.rand(64,512) torch.reshape(tensor, (64, 512, 1, 1)).expand(64, 512, 7, 7)矩阵乘法# Matrix multiplcation: (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
　　利用broadcast机制  dist = torch.sqrt(torch.sum((X1[:,None,:] - X2) ** 2, dim=2))3. 模型定义和操作一个简单两层卷积网络的示例# convolutional neural network (2 convolutional layers) class ConvNet(nn.Module):     def __init__(self, num_classes=10):         super(ConvNet, self).__init__()         self.layer1 = nn.Sequential(             nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2),             nn.BatchNorm2d(16),             nn.ReLU(),             nn.MaxPool2d(kernel_size=2, stride=2))         self.layer2 = nn.Sequential(             nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2),             nn.BatchNorm2d(32),             nn.ReLU(),             nn.MaxPool2d(kernel_size=2, stride=2))         self.fc = nn.Linear(7*7*32, num_classes)       def forward(self, x):         out = self.layer1(x)         out = self.layer2(out)         out = out.reshape(out.size(0), -1)         out = self.fc(out)         return out     model = ConvNet(num_classes).to(device)
　　卷积层的计算和展示可以用这个网站辅助。  双线性汇合（bilinear pooling）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。  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())查看网络中的参数
　　可以通过model.state_dict()或者model.named_parameters()函数查看现在的全部可训练参数（包括通过继承得到的父类中的参数）  params = list(model.named_parameters()) (name, param) = params[28] print(name) print(param.grad) print(＂-------------------------------------------------＂) (name2, param2) = params[29] print(name2) print(param2.grad) print(＂----------------------------------------------------＂) (name1, param1) = params[30] print(name1) print(param1.grad)模型可视化（使用pytorchviz）
　　szagoruyko/pytorchvizgithub.com  类似 Keras 的 model.summary() 输出模型信息，使用pytorch-summary
　　sksq96/pytorch-summarygithub.com
　　模型权重初始化
　　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)提取模型中的某一层
　　modules()会返回模型中所有模块的迭代器，它能够访问到最内层，比如self.layer1.conv1这个模块，还有一个与它们相对应的是name_children()属性以及named_modules(),这两个不仅会返回模块的迭代器，还会返回网络层的名字。  # 取模型中的前两层 new_model = nn.Sequential(*list(model.children())[:2]  # 如果希望提取出模型中的所有卷积层，可以像下面这样操作： for layer in model.named_modules():     if isinstance(layer[1],nn.Conv2d):          conv_model.add_module(layer[0],layer[1])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  model.load_state_dict(torch.load(＂model.pth＂), strict=False)将在 GPU 保存的模型加载到 CPUmodel.load_state_dict(torch.load(＂model.pth＂, map_location=＂cpu＂))导入另一个模型的相同部分到新的模型
　　模型导入参数时，如果两个模型结构不一致，则直接导入参数会报错。用下面方法可以把另一个模型的相同的部分导入到新的模型中。  # model_new代表新的模型 # model_saved代表其他模型，比如用torch.load导入的已保存的模型 model_new_dict = model_new.state_dict() model_common_dict = {k:v for k, v in model_saved.items() if k in model_new_dict.keys()} model_new_dict.update(model_common_dict) model_new.load_state_dict(model_new_dict)4. 数据处理计算数据集的均值和标准差import os import cv2 import numpy as np from torch.utils.data import Dataset from PIL import Image     def compute_mean_and_std(dataset):     # 输入PyTorch的dataset，输出均值和标准差     mean_r = 0     mean_g = 0     mean_b = 0       for img, _ in dataset:         img = np.asarray(img) # change PIL Image to numpy array         mean_b += np.mean(img[:, :, 0])         mean_g += np.mean(img[:, :, 1])         mean_r += np.mean(img[:, :, 2])       mean_b /= len(dataset)     mean_g /= len(dataset)     mean_r /= len(dataset)       diff_r = 0     diff_g = 0     diff_b = 0       N = 0       for img, _ in dataset:         img = np.asarray(img)           diff_b += np.sum(np.power(img[:, :, 0] - mean_b, 2))         diff_g += np.sum(np.power(img[:, :, 1] - mean_g, 2))         diff_r += np.sum(np.power(img[:, :, 2] - mean_r, 2))           N += np.prod(img[:, :, 0].shape)       std_b = np.sqrt(diff_b / N)     std_g = np.sqrt(diff_g / N)     std_r = np.sqrt(diff_r / N)       mean = (mean_b.item() / 255.0, mean_g.item() / 255.0, mean_r.item() / 255.0)     std = (std_b.item() / 255.0, std_g.item() / 255.0, std_r.item() / 255.0)     return mean, std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)]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)), ])5. 模型训练和测试分类模型训练代码# Loss and optimizer criterion = nn.CrossEntropyLoss() optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)   # Train the model total_step = len(train_loader) for epoch in range(num_epochs):     for i ,(images, labels) in enumerate(train_loader):         images = images.to(device)         labels = labels.to(device)           # Forward pass         outputs = model(images)         loss = criterion(outputs, labels)           # Backward and optimizer         optimizer.zero_grad()         loss.backward()         optimizer.step()           if (i+1) % 100 == 0:             print(＂Epoch: [{}/{}], Step: [{}/{}], Loss: {}＂                   .format(epoch+1, num_epochs, i+1, total_step, loss.item()))分类模型测试代码# Test the model model.eval()  # eval mode(batch norm uses moving mean/variance                #instead of mini-batch mean/variance) with torch.no_grad():     correct = 0     total = 0     for images, labels in test_loader:         images = images.to(device)         labels = labels.to(device)         outputs = model(images)         _, predicted = torch.max(outputs.data, 1)         total += labels.size(0)         correct += (predicted == labels).sum().item()       print(＂Test accuracy of the model on the 10000 test images: {} %＂           .format(100 * correct / total))自定义loss
　　继承torch.nn.Module类写自己的loss。  class MyLoss(torch.nn.Moudle):     def __init__(self):         super(MyLoss, self).__init__()       def forward(self, x, y):         loss = torch.mean((x - y) ** 2)         return loss标签平滑（label smoothing）
　　写一个label_smoothing.py的文件，然后在训练代码里引用，用LSR代替交叉熵损失即可。label_smoothing.py内容如下：  import torch import torch.nn as nn     class LSR(nn.Module):       def __init__(self, e=0.1, reduction=＂mean＂):         super().__init__()           self.log_softmax = nn.LogSoftmax(dim=1)         self.e = e         self.reduction = reduction       def _one_hot(self, labels, classes, value=1):         ＂＂＂             Convert labels to one hot vectors           Args:             labels: torch tensor in format [label1, label2, label3, ...]             classes: int, number of classes             value: label value in one hot vector, default to 1           Returns:             return one hot format labels in shape [batchsize, classes]         ＂＂＂           one_hot = torch.zeros(labels.size(0), classes)           #labels and value_added  size must match         labels = labels.view(labels.size(0), -1)         value_added = torch.Tensor(labels.size(0), 1).fill_(value)           value_added = value_added.to(labels.device)         one_hot = one_hot.to(labels.device)           one_hot.scatter_add_(1, labels, value_added)           return one_hot       def _smooth_label(self, target, length, smooth_factor):         ＂＂＂convert targets to one-hot format, and smooth         them.         Args:             target: target in form with [label1, label2, label_batchsize]             length: length of one-hot format(number of classes)             smooth_factor: smooth factor for label smooth           Returns:             smoothed labels in one hot format         ＂＂＂         one_hot = self._one_hot(target, length, value=1 - smooth_factor)         one_hot += smooth_factor / (length - 1)           return one_hot.to(target.device)       def forward(self, x, target):           if x.size(0) != target.size(0):             raise ValueError(＂Expected input batchsize ({}) to match target batch_size({})＂                     .format(x.size(0), target.size(0)))           if x.dim() < 2:             raise ValueError(＂Expected input tensor to have least 2 dimensions(got {})＂                     .format(x.size(0)))           if x.dim() != 2:             raise ValueError(＂Only 2 dimension tensor are implemented, (got {})＂                     .format(x.size()))             smoothed_target = self._smooth_label(target, x.size(1), self.e)         x = self.log_softmax(x)         loss = torch.sum(- x * smoothed_target, dim=1)           if self.reduction == ＂none＂:             return loss           elif self.reduction == ＂sum＂:             return torch.sum(loss)           elif self.reduction == ＂mean＂:             return torch.mean(loss)           else:             raise ValueError(＂unrecognized option, expect reduction to be one of none, mean, sum＂)
　　或者直接在训练文件里做label smoothing  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训练beta_distribution = torch.distributions.beta.Beta(alpha, alpha) for images, labels in train_loader:     images, labels = images.cuda(), labels.cuda()       # Mixup images and labels.     lambda_ = beta_distribution.sample([]).item()     index = torch.randperm(images.size(0)).cuda()     mixed_images = lambda_ * images + (1 - lambda_) * images[index, :]     label_a, label_b = labels, labels[index]       # Mixup loss.     scores = model(mixed_images)     loss = (lambda_ * loss_function(scores, label_a)             + (1 - lambda_) * loss_function(scores, label_b))     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 += torch.sum(torch.abs(param)) loss.backward()不对偏置项进行权重衰减（weight decay）
　　pytorch里的weight decay相当于l2正则  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)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＂])
　　另一种方法，在一个batch训练代码里，当前的lr是optimizer.param_groups[0][＂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(...)优化器链式更新
　　从1.4版本开始，torch.optim.lr_scheduler 支持链式更新（chaining），即用户可以定义两个 schedulers，并交替在训练中使用。  import torch from torch.optim import SGD from torch.optim.lr_scheduler import ExponentialLR, StepLR model = [torch.nn.Parameter(torch.randn(2, 2, requires_grad=True))] optimizer = SGD(model, 0.1) scheduler1 = ExponentialLR(optimizer, gamma=0.9) scheduler2 = StepLR(optimizer, step_size=3, gamma=0.1) for epoch in range(4):     print(epoch, scheduler2.get_last_lr()[0])     optimizer.step()     scheduler1.step()     scheduler2.step()模型训练可视化
　　PyTorch可以使用tensorboard来可视化训练过程。
　　安装和运行TensorBoard。  pip install tensorboard tensorboard --logdir=runs
　　使用SummaryWriter类来收集和可视化相应的数据，放了方便查看，可以使用不同的文件夹，比如＂Loss/train＂和＂Loss/test＂。  from torch.utils.tensorboard import SummaryWriter import numpy as np   writer = SummaryWriter()   for n_iter in range(100):     writer.add_scalar(＂Loss/train＂, np.random.random(), n_iter)     writer.add_scalar(＂Loss/test＂, np.random.random(), n_iter)     writer.add_scalar(＂Accuracy/train＂, np.random.random(), n_iter)     writer.add_scalar(＂Accuracy/test＂, np.random.random(), n_iter)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。  start_epoch = 0 # Load checkpoint. if resume: # resume为参数，第一次训练时设为0，中断再训练时设为1     model_path = os.path.join(＂model＂, ＂best_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 {}.＂.format(start_epoch))     print(＂Best accuracy so far {}.＂.format(best_acc))   # Train the model for epoch in range(start_epoch, num_epochs):      ...        # Test the model     ...       # save checkpoint     is_best = current_acc > best_acc     best_acc = max(current_acc, best_acc)     checkpoint = {         ＂best_acc＂: best_acc,         ＂epoch＂: epoch + 1,         ＂model＂: model.state_dict(),         ＂optimizer＂: optimizer.state_dict(),     }     model_path = os.path.join(＂model＂, ＂checkpoint.pth.tar＂)     best_model_path = os.path.join(＂model＂, ＂best_checkpoint.pth.tar＂)     torch.save(checkpoint, model_path)     if is_best:         shutil.copy(model_path, best_model_path)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 or set       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)6. 其他注意事项
　　不要使用太大的线性层。因为nn.Linear(m,n)使用的是的内存，线性层太大很容易超出现有显存。
　　不要在太长的序列上使用RNN。因为RNN反向传播使用的是BPTT算法，其需要的内存和输入序列的长度呈线性关系。
　　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()。
　　model.zero_grad()会把整个模型的参数的梯度都归零, 而optimizer.zero_grad()只会把传入其中的参数的梯度归零.
　　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。
　　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)
　　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
　　使用TorchSnooper来调试PyTorch代码，程序在执行的时候，就会自动 print 出来每一行的执行结果的 tensor 的形状、数据类型、设备、是否需要梯度的信息。  # pip install torchsnooperimport torchsnooper# 对于函数，使用修饰器@torchsnooper.snoop()# 如果不是函数，使用 with 语句来激活 TorchSnooper，把训练的那个循环装进 with 语句中去。with torchsnooper.snoop():    原本的代码
　　https://github.com/zasdfgbnm/TorchSnoopergithub.com
　　模型可解释性，使用captum库：https://captum.ai/captum.ai
　　参考资料张皓：PyTorch Cookbook（常用代码段整理合集），https://zhuanlan.zhihu.com/p/59205847?  PyTorch官方文档和示例  https://pytorch.org/docs/stable/notes/faq.html  https://github.com/szagoruyko/pytorchviz  https://github.com/sksq96/pytorch-summary  其他