PyTorch 常用代码段整理合集

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 Visualizer​ezyang.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-PyTorch​github.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-summary​github.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 in range(num_blocks)]
for i in range(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 in range(num_blocks)]
for i in range(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.pytorch​github.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/pytorchviz​github.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/visdom​github.comtorch.utils.tensorboard - PyTorch master documentation​pytorch.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
  
  

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，无法一一列举

参考

1. ^T.-Y. Lin, A. RoyChowdhury, and S. Maji. Bilinear CNN models for fine-grained visual recognition. In ICCV, 2015.
2. ^Y. Chen, Y. Bai, W. Zhang, and T. Mei. Destruction and construction learning for fine-grained image recognition. In CVPR, 2019.
3. ^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.
4. ^C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna: Rethinking the Inception architecture for computer vision. In CVPR, 2016.
5. ^H. Zhang, M. Cissé, Y. N. Dauphin, and D. Lopez-Paz. mixup: Beyond empirical risk minimization. In ICLR, 2018.