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
torch.__version__ # PyTorch versiontorch.version.cuda # Corresponding CUDA versiontorch.backends.cudnn.version() # Corresponding cuDNN versiontorch.cuda.get_device_name(0) # GPU type
PyTorch将被安装在anaconda3/lib/python3.7/site-packages/torch/目录下。
conda update pytorch torchvision -c pytorch
torch.manual_seed(0)torch.cuda.manual_seed_all(0)
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'
torch.cuda.is_available()
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
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]
tensor.type() # Data typetensor.size() # Shape of the tensor. It is a subclass of Python tupletensor.dim() # Number of dimensions.
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 -> 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
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 -> PIL.Image.
image = PIL.Image.fromarray(ndarray.astypde(np.uint8))
PIL.Image -> np.ndarray.
ndarray = np.asarray(PIL.Image.open(path))
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)
tensor = tensor[torch.randperm(tensor.size(0))] # Shuffle the first dimension
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()]
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 |
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)
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())
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
Expand tensor of shape 64*512
to shape 64*512*7*7
.
torch.reshape(tensor, (64, 512, 1, 1)).expand(64, 512, 7, 7)
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
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))
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
gap = torch.nn.AdaptiveAvgPool2d(output_size=1)
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
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
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)
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
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)
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)
model.load_state_dict(torch.load('model,pth', map_location='cpu'))
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()
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)]
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)
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
链接:https://github.com/Cadene/pretrained-models.pytorch
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)
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)
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)),])
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()
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()
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_regularization = torch.nn.L1Loss(reduction='sum')loss = ... # Standard cross-entropy lossfor param in model.parameters(): loss += torch.sum(torch.abs(param))loss.backward()
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)
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=20)
score = model(images)prediction = torch.argmax(score, dim=1)num_correct = torch.sum(prediction == labels).item()accuruacy = num_correct / labels.size(0)
链接:https://github.com/szagoruyko/pytorchviz
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)
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'])
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(...)
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)
# 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
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
本文转载自:PyTorch Cookbook(常用代码段整理合集)https://zhuanlan.zhihu.com/p/59205847
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