概述
本文是PyTorch常用代码段合集，涵盖基本配置、张量处理、模型定义与操作、数据处理、模型训练与测试等5个方面，还给出了多个值得注意的Tips，内容非常全面。
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 torchimport torch.nn as nnimport torchvisionprint(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 = Truetorch.backends.cudnn.benchmark = False
显卡设置
如果只需要一张显卡
# Device configurationdevice = 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 osos.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DoubleTensortorch.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.Imageimage = 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.Tensorpath = r'./figure.jpg'tensor = torch.from_numpy(np.asarray(PIL.Image.open(path))).permute(2,0,1).float() / 255tensor = torchvision.transforms.functional.to_tensor(PIL.Image.open(path)) # Equivalently way
np.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 = 4one_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())
得到非零元素
torch.nonzero(tensor) # index of non-zero elementstorch.nonzero(tensor==0) # index of zero elementstorch.nonzero(tensor).size(0) # number of non-zero elementstorch.nonzero(tensor == 0).size(0) # number of zero elements
判断两个张量相等
torch.allclose(tensor1, tensor2) # float tensortorch.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 outmodel = 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*WX = torch.bmm(X, torch.transpose(X, 1, 2)) / (H * W) # Bilinear poolingassert 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 normalizationX = 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_meansync_bn.running_var = module.running_varif module.affine:sync_bn.weight = module.weight.clone().detach()sync_bn.bias = module.bias.clone().detach()return sync_bnelse: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 CPU
model.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 osimport cv2import numpy as npfrom torch.utils.data import Datasetfrom PIL import Imagedef compute_mean_and_std(dataset):# 输入PyTorch的dataset，输出均值和标准差mean_r = 0mean_g = 0mean_b = 0for img, _ in dataset:img = np.asarray(img) # change PIL Image to numpy arraymean_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 = 0diff_g = 0diff_b = 0N = 0for 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 cv2video = 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_segmentsif 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 // 2frame_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 optimizercriterion = nn.CrossEntropyLoss()optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)# Train the modeltotal_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 passoutputs = model(images)loss = criterion(outputs, labels)# Backward and optimizeroptimizer.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 modelmodel.eval() # eval mode(batch norm uses moving mean/variance#instead of mini-batch mean/variance)with torch.no_grad():correct = 0total = 0for 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 torchimport torch.nn as nnclass LSR(nn.Module):def __init__(self, e=0.1, reduction='mean'):super().__init__()self.log_softmax = nn.LogSoftmax(dim=1)self.e = eself.reduction = reductiondef _one_hot(self, labels, classes, value=1):"""Convert labels to one hot vectorsArgs:labels: torch tensor in format [label1, label2, label3, ...]classes: int, number of classesvalue: label value in one hot vector, default to 1Returns:return one hot format labels in shape [batchsize, classes]"""one_hot = torch.zeros(labels.size(0), classes)#labels and value_added size must matchlabels = 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_hotdef _smooth_label(self, target, length, smooth_factor):"""convert targets to one-hot format, and smooththem.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 smoothReturns: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 losselif 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) / Noptimizer.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 lossfor 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 torchfrom torch.optim import SGDfrom torch.optim.lr_scheduler import ExponentialLR, StepLRmodel = [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 tensorboardtensorboard --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 SummaryWriterimport numpy as npwriter = 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1model_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 modelfor epoch in range(start_epoch, num_epochs):...# Test the model...# save checkpointis_best = current_acc > best_accbest_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 givenpre-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_modelself._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 = Falsemodel.fc = nn.Linear(512, 100) # Replace the last fc layeroptimizer = 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
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张皓：PyTorch Cookbook（常用代码段整理合集），https://zhuanlan.zhihu.com/p/59205847?
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PyTorch官方文档和示例
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https://pytorch.org/docs/stable/notes/faq.html
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https://github.com/szagoruyko/pytorchviz
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https://github.com/sksq96/pytorch-summary
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其他
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link
最后
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