PyTorch常用代码块
1. 基本配置
需要导入的包
import collections
import os
import shutil
import tqdm
import numpy as np
import PIL.Image
import torch
import torchvision
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
固定随机种子
在硬件设备(CPU、GPU)不同时,完全的可复现性无法保证,即使随机种子相同。但是,在同一个设备上,应该保证可复现性。具体做法是,在程序开始的时候固定torch的随机种子,同时也把numpy的随机种子固定。
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
import os
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()
2. 张量(Tensor)处理
张量的数据类型
A torch.dtype is an object that represents the data type of a torch.Tensor. PyTorch has twelve different data types:
Data type | dtype | Legacy Constructors |
---|---|---|
32-bit floating point | torch.float32 or torch.float | torch.*.FloatTensor |
64-bit floating point | torch.float64 or torch.double | torch.*.DoubleTensor |
64-bit complex | torch.complex64 or torch.cfloat | |
128-bit complex | torch.complex128 or torch.cdouble | |
**16-bit floating point **[1] | torch.float16 or torch.half | torch.*.HalfTensor |
**16-bit floating point **[2] | torch.bfloat16 | torch.*.BFloat16Tensor |
8-bit integer (unsigned) | torch.uint8 | torch.*.ByteTensor |
8-bit integer (signed) | torch.int8 | torch.*.CharTensor |
16-bit integer (signed) | torch.int16 or torch.short | torch.*.ShortTensor |
32-bit integer (signed) | torch.int32 or torch.int | torch.*.IntTensor |
64-bit integer (signed) | torch.int64 or torch.long | torch.*.LongTensor |
Boolean | torch.bool | torch.*.BoolTensor |
张量基本信息
tensor = torch.randn(3,4,5)
print(tensor.type()) # 数据类型
print(tensor.size()) # 张量的shape,是个元组
print(tensor.dim()) # 维度的数量
命名张量
张量命名是一个非常有用的方法,这样可以方便地使用维度的名字来做索引或其他操作,大大提高了可读性、易用性,防止出错。
# 在PyTorch 1.3之前,需要使用注释
# Tensor[N, C, H, W]
images = torch.randn(32, 3, 56, 56)
images.sum(dim=1)
images.select(dim=1, index=0)
# PyTorch 1.3之后
NCHW = [‘N’, ‘C’, ‘H’, ‘W’]
images = torch.randn(32, 3, 56, 56, names=NCHW)
images.sum('C')
images.select('C', index=0)
# 也可以这么设置
tensor = torch.rand(3,4,1,2,names=('C', 'N', 'H', 'W'))
# 使用align_to可以对维度方便地排序
tensor = tensor.align_to('N', 'C', 'H', 'W')
数据类型转换
# 设置默认类型,pytorch中的FloatTensor远远快于DoubleTensor
torch.set_default_tensor_type(torch.FloatTensor)
# 类型转换
tensor = tensor.cuda()
tensor = tensor.cpu()
tensor = tensor.float()
tensor = tensor.long()
torch.Tensor与np.ndarray转换
除了CharTensor,其他所有CPU上的张量都支持转换为numpy格式然后再转换回来。
ndarray = tensor.cpu().numpy()
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, C, 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
path = r'./figure.jpg'
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的转换
image = PIL.Image.fromarray(ndarray.astype(np.uint8))
ndarray = np.asarray(PIL.Image.open(path))
从只包含一个元素的张量中提取值
value = torch.rand(1).item()
张量形变
# 在将卷积层输入全连接层的情况下通常需要对张量做形变处理,
# 相比torch.view,torch.reshape可以自动处理输入张量不连续的情况。
tensor = torch.rand(2,3,4)
shape = (6, 4)
tensor = torch.reshape(tensor, shape)
打乱顺序
tensor = tensor[torch.randperm(tensor.size(0))] # 打乱第一个维度
水平翻转
>>> x = torch.arange(10).view(2, 5)
>>> x
tensor([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]])
>>> torch.flip(x, dims=[0]) # 对第0维进行反转
tensor([[5, 6, 7, 8, 9],
[0, 1, 2, 3, 4]])
>>> torch.flip(x, dims=[1]) # 对第1维进行反转
tensor([[4, 3, 2, 1, 0],
[9, 8, 7, 6, 5]])
>>> torch.flip(x, dims=[0, 1]) # 对第0、1维进行反转
tensor([[9, 8, 7, 6, 5],
[4, 3, 2, 1, 0]])
>>> x.flip(dims=[0, 1]) # 对第0、1维进行反转,与上一句效果相同
tensor([[9, 8, 7, 6, 5],
[4, 3, 2, 1, 0]])
复制张量
# Operation | New/Shared memory | Still in computation graph |
tensor.clone() # | New | Yes |
tensor.detach() # | Shared | No |
tensor.detach.clone()() # | New | No |
张量拼接
'''
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10x5的张量,torch.cat的结果是30x5的张量,
而torch.stack的结果是3x10x5的张量。
'''
tensor = torch.cat(list_of_tensors, dim=0)
tensor = torch.stack(list_of_tensors, dim=0)
将整数标签转为one-hot编码
# pytorch的标记默认从0开始
tensor = torch.tensor([0, 2, 1, 3])
N = tensor.size(0)
num_classes = 4
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.
tensor = torch.rand(64,512)
torch.reshape(tensor, (64, 512, 1, 1)).expand(64, 512, 7, 7)
矩阵乘法
# Matrix multiplcation: (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
利用broadcast机制
dist = torch.sqrt(torch.sum((X1[:,None,:] - X2) ** 2, dim=2))
GAP(Global average pooling)层
gap = torch.nn.AdaptiveAvgPool2d(output_size=1)
3. 模型定义和操作
一个简单两层卷积网络的示例
# convolutional neural network (2 convolutional layers)
class ConvNet(nn.Module):
def __init__(self, num_classes=10):
super(ConvNet, self).__init__()
self.layer1 = nn.Sequential(
nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2),
nn.BatchNorm2d(16),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2))
self.layer2 = nn.Sequential(
nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2))
self.fc = nn.Linear(7*7*32, num_classes)
def forward(self, x):
out = self.layer1(x)
out = self.layer2(out)
out = out.reshape(out.size(0), -1)
out = self.fc(out)
return out
model = ConvNet(num_classes).to(device)
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。
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())
查看网络中的参数
可以通过model.state_dict()或者model.named_parameters()函数查看现在的全部可训练参数(包括通过继承得到的父类中的参数)
params = list(model.named_parameters())
(name, param) = params[28]
print(name)
print(param.grad)
print('-------------------------------------------------')
(name2, param2) = params[29]
print(name2)
print(param2.grad)
print('----------------------------------------------------')
(name1, param1) = params[30]
print(name1)
print(param1.grad)
模型权重初始化
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)
提取模型中的某一层
modules()会返回模型中所有模块的迭代器,它能够访问到最内层,比如self.layer1.conv1这个模块,还有一个与它们相对应的是name_children()属性以及named_modules(),这两个不仅会返回模块的迭代器,还会返回网络层的名字。
# 取模型中的前两层
new_model = nn.Sequential(*list(model.children())[:2]
# 如果希望提取出模型中的所有卷积层,可以像下面这样操作:
for layer in model.named_modules():
if isinstance(layer[1],nn.Conv2d):
conv_model.add_module(layer[0],layer[1])
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'))
导入另一个模型的相同部分到新的模型
模型导入参数时,如果两个模型结构不一致,则直接导入参数会报错。用下面方法可以把另一个模型的相同的部分导入到新的模型中。
# model_new代表新的模型
# model_saved代表其他模型,比如用torch.load导入的已保存的模型
model_new_dict = model_new.state_dict()
model_common_dict = {k:v for k, v in model_saved.items() if k in model_new_dict.keys()}
model_new_dict.update(model_common_dict)
model_new.load_state_dict(model_new_dict)
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)
# 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)
计算数据集的均值和标准差
import os
import cv2
import numpy as np
from torch.utils.data import Dataset
from PIL import Image
def compute_mean_and_std(dataset):
# 输入PyTorch的dataset,输出均值和标准差
mean_r = 0
mean_g = 0
mean_b = 0
for img, _ in dataset:
img = np.asarray(img) # change PIL Image to numpy array
mean_b += np.mean(img[:, :, 0])
mean_g += np.mean(img[:, :, 1])
mean_r += np.mean(img[:, :, 2])
mean_b /= len(dataset)
mean_g /= len(dataset)
mean_r /= len(dataset)
diff_r = 0
diff_g = 0
diff_b = 0
N = 0
for img, _ in dataset:
img = np.asarray(img)
diff_b += np.sum(np.power(img[:, :, 0] - mean_b, 2))
diff_g += np.sum(np.power(img[:, :, 1] - mean_g, 2))
diff_r += np.sum(np.power(img[:, :, 2] - mean_r, 2))
N += np.prod(img[:, :, 0].shape)
std_b = np.sqrt(diff_b / N)
std_g = np.sqrt(diff_g / N)
std_r = np.sqrt(diff_r / N)
mean = (mean_b.item() / 255.0, mean_g.item() / 255.0, mean_r.item() / 255.0)
std = (std_b.item() / 255.0, std_g.item() / 255.0, std_r.item() / 255.0)
return mean, std
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
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)),
])
训练代码基本框架
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 += 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)
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
# 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