点赞收藏:PyTorch常用代码段整理合集

转自机器之心:https://www.jiqizhixin.com/articles/2019-04-25-8

众所周知,程序猿在写代码时通常会在网上搜索大量资料,其中大部分是代码段。然而,这项工作常常令人心累身疲,耗费大量时间。所以,今天小编转载了知乎上的一篇文章,介绍了一些常用PyTorch代码段,希望能够为奋战在电脑桌前的众多程序猿们提供帮助!

本文代码基于 PyTorch 1.0 版本,需要用到以下包

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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

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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。

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conda update pytorch torchvision -c pytorch

固定随机种子

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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

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CUDA_VISIBLE_DEVICES=0,1 python train.py

或在代码中指定

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os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'

判断是否有 CUDA 支持

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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。

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torch.backends.cudnn.benchmark = True

如果想要避免这种结果波动,设置

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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

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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

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ps aux | grep pythonkill -9 [pid]

或者直接重置没有被清空的 GPU

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nvidia-smi --gpu-reset -i [gpu_id]

张量处理

张量基本信息

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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

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# 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

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# 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。

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# 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

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# 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。

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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。

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tensor = torch.reshape(tensor, shape)

打乱顺序

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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。

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# 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。

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# 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。

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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。

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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

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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

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# 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

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# 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

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# 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

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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

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gap = torch.nn.AdaptiveAvgPool2d(output_size=1)

双线性汇合(bilinear pooling)

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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。

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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

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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。

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# 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

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model.load_state_dict(torch.load('model,pth'), strict=False)

将在 GPU 保存的模型加载到 CPU

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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

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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

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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

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# 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

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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

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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

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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。

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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

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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)

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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

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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

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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)

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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)

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torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=20)

计算 Softmax 输出的准确率

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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

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# 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

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# 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

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# 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。

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# 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)

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# 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
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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
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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
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with torch.autograd.profiler.profile(enabled=True, use_cuda=False) as profile:
...
print(profile)

或者在命令行运行

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python -m torch.utils.bottleneck main.py

致谢

感谢 @些许流年和@El tnoto的勘误。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

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/

原知乎链接:https://zhuanlan.zhihu.com/p/59205847?