pytorch list转tensor_PyTorch 常用代码段整理合集，建议收藏！

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转自：张皓

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 collectionsimport osimport shutilimport tqdmimport numpy as npimport PIL.Imageimport torchimport torchvision

基础配置

检查 PyTorch 版本

torch.__version__ # PyTorch versiontorch.version.cuda # Corresponding CUDA versiontorch.backends.cudnn.version() # Corresponding cuDNN versiontorch.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 pythonkill -9 [pid]

或者直接重置没有被清空的 GPU

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

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

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.X1 = torch.unsqueeze(X1, dim=1).expand(m, n, d)# X2 is of shape n*d.X2 = torch.unsqueeze(X2, dim=0).expand(m, n, d)# dist is of shape m*n, where dist[i][j] = sqrt(|X1[i, :] - X[j, :]|^2)dist = torch.sqrt(torch.sum((X1 - X2) ** 2, dim=2))

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

链接：https://ezyang.github.io/convolution-visualizer/index.html

0GAP(Global average pooling)层

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

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。

链接：https://github.com/vacancy/Synchronized-BatchNorm-PyTorch

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

链接：https://github.com/sksq96/pytorch-summary

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

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 ))

数据准备、特征提取与微调

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_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 // 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 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

其他预训练模型

链接：https://github.com/Cadene/pretrained-models.pytorch

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)

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(224), 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)

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

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 += 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

链接：https://github.com/szagoruyko/pytorchviz

可视化学习曲线

有 Facebook 自己开发的 Visdom 和 Tensorboard 两个选择。

https://github.com/facebookresearch/visdom

https://github.com/lanpa/tensorboardX

# 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

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)

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 @些许流年和@El tnoto的勘误。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 (https://link.zhihu.com/?target=https%3A//github.com/pytorch/examples)
PyTorch 论坛：PyTorch Forums (https://link.zhihu.com/?target=https%3A//discuss.pytorch.org/latest%3Forder%3Dviews)
PyTorch 文档：http://pytorch.org/docs/stable/index.html (https://link.zhihu.com/?target=http%3A//pytorch.org/docs/stable/index.html)
其他基于 PyTorch 的公开实现代码，无法一一列举

– EOF –

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