PyTorch常用代码段合集_pytorch例程

2023-03-26 08:00:52

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DoubleTensor torch.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.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 path = r./figure.jpg 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 = 4 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

判断两个张量相等torch.allclose(tensor1, tensor2) # float tensor torch.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 out model = 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*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。

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())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/szagoruyko/pytorchvizgithub.com/szagoruyko/pytorchviz

类似 Keras 的 model.summary() 输出模型信息（使用pytorch-summary ）sksq96/pytorch-summarygithub.com/sksq96/pytorch-summary

github.com/sksq96/pytorch-summary

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 CPUmodel.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 os import cv2 import numpy as np from torch.utils.data import Dataset from PIL import Image def compute_mean_and_std(dataset): # 输入PyTorch的dataset，输出均值和标准差 mean_r = 0 mean_g = 0 mean_b = 0 for img, _ in dataset: img = np.asarray(img) # change PIL Image to numpy array mean_r += np.mean(img[:, :, 0]) mean_g += np.mean(img[:, :, 1]) mean_b += np.mean(img[:, :, 2]) mean_r /= len(dataset) mean_g /= len(dataset) mean_b /= len(dataset) diff_r = 0 diff_g = 0 diff_b = 0 N = 0 for img, _ in dataset: img = np.asarray(img) diff_r += np.sum(np.power(img[:, :, 0] - mean_r, 2)) diff_g += np.sum(np.power(img[:, :, 1] - mean_g, 2)) diff_b += np.sum(np.power(img[:, :, 2] - mean_b, 2)) N += np.prod(img[:, :, 0].shape) std_r = np.sqrt(diff_r / N) std_g = np.sqrt(diff_g / N) std_b = np.sqrt(diff_b / N) mean = (mean_r.item() / 255.0, mean_g.item() / 255.0, mean_b.item() / 255.0) std = (std_r.item() / 255.0, std_g.item() / 255.0, std_b.item() / 255.0) return mean, std

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)]

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 optimizer criterion = nn.CrossEntropyLoss() optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) # Train the model total_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 pass outputs = model(images) loss = criterion(outputs, labels) # Backward and optimizer optimizer.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 model model.eval() # eval mode(batch norm uses moving mean/variance #instead of mini-batch mean/variance) with torch.no_grad(): correct = 0 total = 0 for 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lossclass 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 torch import torch.nn as nn class LSR(nn.Module): def __init__(self, e=0.1, reduction=mean): super().__init__() self.log_softmax = nn.LogSoftmax(dim=1) self.e = e self.reduction = reduction def _one_hot(self, labels, classes, value=1): """ Convert labels to one hot vectors Args: labels: torch tensor in format [label1, label2, label3, ...] classes: int, number of classes value: label value in one hot vector, default to 1 Returns: return one hot format labels in shape [batchsize, classes] """ one_hot = torch.zeros(labels.size(0), classes) #labels and value_added size must match labels = 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_hot def _smooth_label(self, target, length, smooth_factor): """convert targets to one-hot format, and smooth them. 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 smooth Returns: 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 loss elif 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 smoothingfor 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 loss for 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 torch from torch.optim import SGD from torch.optim.lr_scheduler import ExponentialLR, StepLR model = [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TensorBoardpip install tensorboard tensorboard --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/testfrom torch.utils.tensorboard import SummaryWriter import numpy as np writer = 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1 model_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 model for epoch in range(start_epoch, num_epochs): ... # Test the model ... # save checkpoint is_best = current_acc > best_acc best_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 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

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)

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 O(mn) 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 Softmaxtorch.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 torchsnooper import 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(): 原本的代码模型可解释性，使用captum库参考资料：张皓：PyTorch Cookbook（常用代码段整理合集）PyTorch官方文档和示例

https://pytorch.org/docs/stable/notes/faq.htmlhttps://github.com/szagoruyko/pytorchvizhttps://github.com/sksq96/pytor

ch-summary其他

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PyTorch常用代码段合集_pytorch例程

目录：

1.pytorch代码在哪里运行

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4.pytorch详解

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