PyTorch 常用代码段整理合集
发布日期:2022-03-11 10:18:48 浏览次数:42 分类:技术文章

本文共 23728 字,大约阅读时间需要 79 分钟。

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

1. 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]

2. 张量处理

张量基本信息

tensor.type()   # Data typetensor.size()   # Shape of the tensor. It is a subclass of Python tupletensor.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() / 255tensor = 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 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.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

 

GAP(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*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。

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

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)

# X is torch.Tensor of size N*D*H*W.# Shuffle rowsQ = (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 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 // 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

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)

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)

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 lossfor 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

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。

 

# 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_accbest_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.metricsall_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) == 1all_prediction = np.concatenate(all_prediction)assert all_label.shape == all_prediction.shapemicro_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

致谢

感谢 

、 

 、 

 的勘误,感谢 

 提供的更简洁的方法。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论坛:
  • PyTorch文档:
  • 其他基于PyTorch的公开实现代码,无法一一列举

参考

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

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