错误：_pickle.PicklingError：无法在 0x0000002F2175B048> 处腌制<function <lambda>：在 __main__ 上查找属性<lambda>失败

问题描述 投票：0回答：1

我正在尝试运行以下代码，该代码报告与其他用户一起运行良好，但我发现了此错误。

-- 编码：utf-8 --

导入东西

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils import data
from torch.utils.data import DataLoader
import torchvision.transforms as transforms

import cv2

import numpy as np

import csv

Step1：从日志文件中读取

samples = []
with open('data/driving_log.csv') as csvfile:
    reader = csv.reader(csvfile)
    next(reader, None)
    for line in reader:
        samples.append(line)

Step2：将数据分为训练集和验证集

train_len = int(0.8*len(samples))
valid_len = len(samples) - train_len
train_samples, validation_samples = data.random_split(samples, lengths=[train_len, valid_len])

Step3a：定义数据加载器的增强、转换过程、参数和数据集

def augment(imgName, angle):
  name = 'data/IMG/' + imgName.split('/')[-1]
  current_image = cv2.imread(name)
  current_image = current_image[65:-25, :, :]
  if np.random.rand() < 0.5:
    current_image = cv2.flip(current_image, 1)
    angle = angle * -1.0  
  return current_image, angle

class Dataset(data.Dataset):

    def __init__(self, samples, transform=None):

        self.samples = samples
        self.transform = transform

    def __getitem__(self, index):
      
        batch_samples = self.samples[index]
        
        steering_angle = float(batch_samples[3])
        
        center_img, steering_angle_center = augment(batch_samples[0], steering_angle)
        left_img, steering_angle_left = augment(batch_samples[1], steering_angle + 0.4)
        right_img, steering_angle_right = augment(batch_samples[2], steering_angle - 0.4)

        center_img = self.transform(center_img)
        left_img = self.transform(left_img)
        right_img = self.transform(right_img)

        return (center_img, steering_angle_center), (left_img, steering_angle_left), (right_img, steering_angle_right)
      
    def __len__(self):
        return len(self.samples)

Step3b：使用数据加载器创建生成器来并行化流程

transformations = transforms.Compose([transforms.Lambda(lambda x: (x / 255.0) - 0.5)])

params = {'batch_size': 32,
          'shuffle': True,
          'num_workers': 4}

training_set = Dataset(train_samples, transformations)
training_generator = data.DataLoader(training_set, **params)

validation_set = Dataset(validation_samples, transformations)
validation_generator = data.DataLoader(validation_set, **params)

第四步：定义网络

NetworkDense 类（nn.Module）：

def __init__(self):
    super(NetworkDense, self).__init__()
    self.conv_layers = nn.Sequential(
        nn.Conv2d(3, 24, 5, stride=2),
        nn.ELU(),
        nn.Conv2d(24, 36, 5, stride=2),
        nn.ELU(),
        nn.Conv2d(36, 48, 5, stride=2),
        nn.ELU(),
        nn.Conv2d(48, 64, 3),
        nn.ELU(),
        nn.Conv2d(64, 64, 3),
        nn.Dropout(0.25)
    )
    self.linear_layers = nn.Sequential(
        nn.Linear(in_features=64 * 2 * 33, out_features=100),
        nn.ELU(),
        nn.Linear(in_features=100, out_features=50),
        nn.ELU(),
        nn.Linear(in_features=50, out_features=10),
        nn.Linear(in_features=10, out_features=1)
    )
    
def forward(self, input):  
    input = input.view(input.size(0), 3, 70, 320)
    output = self.conv_layers(input)
    output = output.view(output.size(0), -1)
    output = self.linear_layers(output)
    return output


class NetworkLight(nn.Module):

def __init__(self):
    super(NetworkLight, self).__init__()
    self.conv_layers = nn.Sequential(
        nn.Conv2d(3, 24, 3, stride=2),
        nn.ELU(),
        nn.Conv2d(24, 48, 3, stride=2),
        nn.MaxPool2d(4, stride=4),
        nn.Dropout(p=0.25)
    )
    self.linear_layers = nn.Sequential(
        nn.Linear(in_features=48*4*19, out_features=50),
        nn.ELU(),
        nn.Linear(in_features=50, out_features=10),
        nn.Linear(in_features=10, out_features=1)
    )
    

def forward(self, input):
    input = input.view(input.size(0), 3, 70, 320)
    output = self.conv_layers(input)
    output = output.view(output.size(0), -1)
    output = self.linear_layers(output)
    return output

Step5：定义优化器

model = NetworkLight()
optimizer = optim.Adam(model.parameters(), lr=0.0001)

criterion = nn.MSELoss()

Step6：检查设备并定义将张量移动到该设备的函数

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') 
print('device is: ', device)

def toDevice(datas, device):
  
  imgs, angles = datas
  return imgs.float().to(device), angles.float().to(device)

第七步：根据定义的最大历元训练和验证网络

max_epochs = 22

for epoch in range(max_epochs):
    
    model.to(device)
    
    # Training
    train_loss = 0
    model.train()
    for local_batch, (centers, lefts, rights) in enumerate(training_generator):
        # Transfer to GPU
        centers, lefts, rights = toDevice(centers, device), toDevice(lefts, device), toDevice(rights, device)
        
        # Model computations
        optimizer.zero_grad()
        datas = [centers, lefts, rights]        
        for data in datas:
            imgs, angles = data
#             print("training image: ", imgs.shape)
            outputs = model(imgs)
            loss = criterion(outputs, angles.unsqueeze(1))
            loss.backward()
            optimizer.step()

            train_loss += loss.data[0].item()
            
        if local_batch % 100 == 0:
            print('Loss: %.3f '
                 % (train_loss/(local_batch+1)))

    
    # Validation
    model.eval()
    valid_loss = 0
    with torch.set_grad_enabled(False):
        for local_batch, (centers, lefts, rights) in enumerate(validation_generator):
            # Transfer to GPU
            centers, lefts, rights = toDevice(centers, device), toDevice(lefts, device), toDevice(rights, device)
        
            # Model computations
            optimizer.zero_grad()
            datas = [centers, lefts, rights]        
            for data in datas:
                imgs, angles = data
#                 print("Validation image: ", imgs.shape)
                outputs = model(imgs)
                loss = criterion(outputs, angles.unsqueeze(1))
                
                valid_loss += loss.data[0].item()

            if local_batch % 100 == 0:
                print('Valid Loss: %.3f '
                     % (valid_loss/(local_batch+1)))

Step8：定义状态并将模型保存到状态

state = {
        'model': model.module if device == 'cuda' else model,
        }

torch.save(state, 'model.h5')

这是错误消息：

"D:\VICO\Back up\venv\Scripts\python.exe" "D:/VICO/Back up/venv/Scripts/self_driving_car.py"
device is:  cpu
Traceback (most recent call last):
  File "D:/VICO/Back up/venv/Scripts/self_driving_car.py", line 163, in <module>
    for local_batch, (centers, lefts, rights) in enumerate(training_generator):
  File "D:\VICO\Back up\venv\lib\site-packages\torch\utils\data\dataloader.py", line 291, in __iter__
    return _MultiProcessingDataLoaderIter(self)
  File "D:\VICO\Back up\venv\lib\site-packages\torch\utils\data\dataloader.py", line 737, in __init__
    w.start()
  File "C:\Users\isonata\AppData\Local\Programs\Python\Python37\lib\multiprocessing\process.py", line 112, in start
    self._popen = self._Popen(self)
  File "C:\Users\isonata\AppData\Local\Programs\Python\Python37\lib\multiprocessing\context.py", line 223, in _Popen
    return _default_context.get_context().Process._Popen(process_obj)
  File "C:\Users\isonata\AppData\Local\Programs\Python\Python37\lib\multiprocessing\context.py", line 322, in _Popen
    return Popen(process_obj)
  File "C:\Users\isonata\AppData\Local\Programs\Python\Python37\lib\multiprocessing\popen_spawn_win32.py", line 89, in __init__
    reduction.dump(process_obj, to_child)
  File "C:\Users\isonata\AppData\Local\Programs\Python\Python37\lib\multiprocessing\reduction.py", line 60, in dump
    ForkingPickler(file, protocol).dump(obj)
_pickle.PicklingError: Can't pickle <function <lambda> at 0x0000002F2175B048>: attribute lookup <lambda> on __main__ failed

Process finished with exit code 1

我不确定下一步要解决该问题。

python deep-learning torchvision
1个回答
8
投票

pickle
不会 pickle 函数对象。它期望通过导入其模块并查找其名称来找到函数对象。 lambda 是匿名函数（没有名称），因此不起作用。解决方案是在模块级别命名函数。我在你的代码中发现的唯一 lambda 是

transformations = transforms.Compose([transforms.Lambda(lambda x: (x / 255.0) - 0.5)])

假设这是个麻烦的功能，你可以

def _my_normalization(x):
    return x/255.0 - 0.5

transformations = transforms.Compose([transforms.Lambda(_my_normalization])

您可能会遇到其他问题，因为看起来您正在模块级别工作。如果这是一个多处理事物并且您在 Windows 上运行，则新进程将导入该文件并再次运行所有该模块级代码。这在 linux/mac 上不是问题，因为分叉进程已经从父进程加载了模块。

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