（给机器学习算法与Python学习加星标，提升AI技能）
张皓：南京大学计算机系机器学习与数据挖掘所（LAMDA）硕士生，研究方向为计算机视觉和机器学习，特别是视觉识别和深度学习。个人主页：
http://lamda.nju.edu.cn/zhangh/
原知乎链接：
https://zhuanlan.zhihu.com/p/59205847
本文代码基于 PyTorch 1.0 版本，需要用到以下包
import collections import os import shutil import tqdm import numpy as np import PIL.Image import torch import torchvision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 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 stridetorch.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 waynp.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_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
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 * 100PyTorch 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
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