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来自 | 知乎

地址 | https://zhuanlan.zhihu.com/p/59205847

作者 | 张皓

编辑 | 机器学习算法与自然语言处理公众号

本文仅作学术分享，若侵权，请联系后台删文处理

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

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 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。

# 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

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.

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, X2 is of shape n*d.

dist = torch.sqrt(torch.sum((X1[:,None,:] - X2) ** 2, dim=2))

3. 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

Convolution Visualizerezyang.github.io

GAP（Global average pooling）层

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

双线性汇合（bilinear pooling）^[1]

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。

vacancy/Synchronized-BatchNorm-PyTorchgithub.com

现在PyTorch官方已经支持同步BN操作

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())

类似Keras的model.summary()输出模型信息

sksq96/pytorch-summarygithub.com

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. 数据准备、特征提取与微调

图像分块打散（image shuffle）/区域混淆机制（region confusion mechanism，RCM）^[2]

# X is torch.Tensor of size N*D*H*W.
# Shuffle rows
Q 
= (torch
.unsqueeze(torch
.arange(num_blocks), dim
=1) 
* torch
.ones(
1, num_blocks)
.long()

+ torch
.randint(low
=-neighbour, high
=neighbour, size
=(num_blocks, num_blocks)))

Q 
= torch
.argsort(Q, dim
=0)

assert Q
.size() 
== (num_blocks, num_blocks)


X 
= [torch
.chunk(row, chunks
=num_blocks, dim
=2)

for row 
in torch
.chunk(X, chunks
=num_blocks, dim
=1)]

X 
= [[X[Q[i, j]
.item()][j] 
for j 
inrange(num_blocks)]

for i 
inrange(num_blocks)]


# Shulle columns.
Q 
= (torch
.ones(num_blocks, 
1)
.long() 
* torch
.unsqueeze(torch
.arange(num_blocks), dim
=0)

+ torch
.randint(low
=-neighbour, high
=neighbour, size
=(num_blocks, num_blocks)))

Q 
= torch
.argsort(Q, dim
=1)

assert Q
.size() 
== (num_blocks, num_blocks)

X 
= [[X[i][Q[i, j]
.item()] 
for j 
inrange(num_blocks)]

for i 
inrange(num_blocks)]


Y 
= torch
.cat([torch
.cat(row, dim
=2) 
for row 
in X], dim
=1)

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^[3]

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

Cadene/pretrained-models.pytorchgithub.com

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)

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(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)),

])

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）^[4]

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^[5]

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 += lambda_ * 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

可视化学习曲线

有Facebook自己开发的Visdom和Tensorboard（仍处于实验阶段）两个选择。

facebookresearch/visdomgithub.com

torch.utils.tensorboard - PyTorch master documentationpytorch.org

# 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

6. 模型测试

计算每个类别的查准率（precision）、查全率（recall）、F1和总体指标

import sklearn.metrics


all_label = []

all_prediction = []

for images, labels in tqdm.tqdm(data_loader):

 # Data.

 images, labels = images.cuda(), labels.cuda()


 # Forward pass.

 score = model(images)


 # Save label and predictions.

 prediction = torch.argmax(score, dim=1)

 all_label.append(labels.cpu().numpy())

 all_prediction.append(prediction.cpu().numpy())


# Compute RP and confusion matrix.

all_label = np.concatenate(all_label)

assert len(all_label.shape) == 1

all_prediction = np.concatenate(all_prediction)

assert all_label.shape == all_prediction.shape

micro_p, micro_r, micro_f1, _ = sklearn.metrics.precision_recall_fscore_support(

 all_label, all_prediction, average='micro', labels=range(num_classes))

class_p, class_r, class_f1, class_occurence = sklearn.metrics.precision_recall_fscore_support(

 all_label, all_prediction, average=None, labels=range(num_classes))

# Ci,j = #{y=i and hat_y=j}

confusion_mat = sklearn.metrics.confusion_matrix(

 all_label, all_prediction, labels=range(num_classes))

assert confusion_mat.shape == (num_classes, num_classes)

将各类结果写入电子表格

import csv


# Write results onto disk.

with open(os.path.join(path, filename), 'wt', encoding='utf-8') as f:

 f = csv.writer(f)

 f.writerow(['Class', 'Label', '# occurence', 'Precision', 'Recall', 'F1',

 'Confused class 1', 'Confused class 2', 'Confused class 3',

 'Confused 4', 'Confused class 5'])

 for c in range(num_classes):

 index = np.argsort(confusion_mat[:, c])[::-1][:5]

 f.writerow([

 label2class[c], c, class_occurence[c], '%4.3f' % class_p[c],

 '%4.3f' % class_r[c], '%4.3f' % class_f1[c],

 '%s:%d' % (label2class[index[0]], confusion_mat[index[0], c]),

 '%s:%d' % (label2class[index[1]], confusion_mat[index[1], c]),

 '%s:%d' % (label2class[index[2]], confusion_mat[index[2], c]),

 '%s:%d' % (label2class[index[3]], confusion_mat[index[3], c]),

 '%s:%d' % (label2class[index[4]], confusion_mat[index[4], c])])

 f.writerow(['All', '', np.sum(class_occurence), micro_p, micro_r, micro_f1,

 '', '', '', '', ''])

7. 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)

此外，还可以通过torch.utils.checkpoint前向传播时只保留一部分中间结果来节约GPU存储使用，在反向传播时需要的内容从最近中间结果中计算得到。

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

感谢 @些许流年、 @El tnoto 、 @FlyCharles 的勘误，感谢 @oatmeal 提供的更简洁的方法。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：pytorch.org/docs/stable
其他基于PyTorch的公开实现代码，无法一一列举

参考

^T.-Y. Lin, A. RoyChowdhury, and S. Maji. Bilinear CNN models for fine-grained visual recognition. In ICCV, 2015.
^Y. Chen, Y. Bai, W. Zhang, and T. Mei. Destruction and construction learning for fine-grained image recognition. In CVPR, 2019.
^L. Wang, Y. Xiong, Z. Wang, Y. Qiao, D. Lin, X. Tang, and L. V. Gool. Temporal segment networks: Towards good practices for deep action recognition. In ECCV, 2016.
^C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna: Rethinking the Inception architecture for computer vision. In CVPR, 2016.
^H. Zhang, M. Cissé, Y. N. Dauphin, and D. Lopez-Paz. mixup: Beyond empirical risk minimization. In ICLR, 2018.

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