机器之心转载
来源：知乎
作者：张皓
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 pythonkill -9 [pid]
或者直接重置没有被清空的 GPU
nvidia-smi --gpu-reset -i [gpu_id]
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 NDH*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 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: (mn) (np) -> (mp).
result = torch.mm(tensor1, tensor2)
Batch matrix multiplication: (bmn) (bnp) -> (bm*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))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
链接：https://ezyang.github.io/convolution-visualizer/index.html
###0GAP（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*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。
链接：https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
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
链接：https://github.com/sksq96/pytorch-summary
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
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 ))
数据准备、特征提取与微调
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)]
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<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
链接：https://github.com/Cadene/pretrained-models.pytorch
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)
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
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 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
链接：https://github.com/szagoruyko/pytorchviz
可视化学习曲线
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)
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)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
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张皓：南京大学计算机系机器学习与数据挖掘所（LAMDA）硕士生，研究方向为计算机视觉和机器学习，特别是视觉识别和深度学习。个人主页：http://lamda.nju.edu.cn/zhangh/
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