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