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
1. 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 python
kill -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 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 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() / 255
tensor = 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
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))
3. 模型定义
Global average pooling (GAP)层
gap = torch.nn.AdaptiveAvgPool2d(output_size=1)
多卡同步Batch normalization (BN)
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。
vacancy/Synchronized-BatchNorm-PyTorchgithub.com
图标
计算模型整体参数量
import functools
import operator
num_parameters = sum(functools.reduce(operator.mul, parameter.size(), 1)
for parameter in model.parameters())
类似Keras的model.summary()输出模型信息
sksq96/pytorch-summarygithub.com
图标
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. 特征提取与微调
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
# VGG-16 pool5 feature.
model = torchvision.models.vgg16(pretrained=True)
model = torch.nn.Sequential(model.features, model.avgpool)
# 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
Cadene/pretrained-models.pytorchgithub.com
图标
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 = [{'parameters': conv_parameters, 'lr': 1e-3},
{'parameters': 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(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
for images, labels in train_loader:
images, labels = images.cuda(), labels.cuda()
# Mixup images.
lambda_ = np.random.beta(alpha, alpha)
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))
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
szagoruyko/pytorchvizgithub.com
图标
可视化学习曲线
有Facebook自己开发的Visdom和Tensorboard两个选择。
facebookresearch/visdomgithub.com
图标
lanpa/tensorboardXgithub.com
图标
# 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)
6. 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)
- 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
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
- PyTorch论坛：PyTorch Forums
- PyTorch文档：http://pytorch.org/docs/stable/index.html
- 其他基于PyTorch的公开实现代码
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