[英]Error :_pickle.PicklingError: Can't pickle <function <lambda> at 0x0000002F2175B048>: attribute lookup <lambda> on __main__ failed
I am trying to run following code that reported running well with other users, but I found this error.我正在尝试运行以下报告与其他用户运行良好的代码,但我发现了这个错误。
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
samples = []
with open('data/driving_log.csv') as csvfile:
reader = csv.reader(csvfile)
next(reader, None)
for line in reader:
samples.append(line)
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])
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)
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)
class NetworkDense(nn.Module):类 NetworkDense(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
model = NetworkLight()
optimizer = optim.Adam(model.parameters(), lr=0.0001)
criterion = nn.MSELoss()
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)
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)))
state = {
'model': model.module if device == 'cuda' else model,
}
torch.save(state, 'model.h5')
this is the error message: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
I am not sure the next step to resolve the problem.我不确定解决问题的下一步。
pickle
doesn't pickle function objects. pickle
不pickle 函数对象。 It expects to find the function object by importing its module and looking up its name.它期望通过导入其模块并查找其名称来找到函数对象。 lambdas are anonymous functions (no name) so that doesn't work. lambdas 是匿名函数(无名称),因此不起作用。 The solution is to name the function at module level.解决方案是在模块级别命名函数。 The only lambda I found in your code is我在您的代码中找到的唯一 lambda 是
transformations = transforms.Compose([transforms.Lambda(lambda x: (x / 255.0) - 0.5)])
Assuming that's the troublesome function, you can假设这是麻烦的功能,你可以
def _my_normalization(x):
return x/255.0 - 0.5
transformations = transforms.Compose([transforms.Lambda(_my_normalization])
You may have other problems because it looks like you are doing work at module level.您可能会遇到其他问题,因为您似乎是在模块级别进行工作。 If this is a multiprocessing thing and you are running on windows, the new process will import the file and run all of that module level code again.如果这是一个多处理的事情并且您在 Windows 上运行,则新进程将导入文件并再次运行所有模块级代码。 This isn't a problem on linux/mac where a forked process already has the modules loaded from the parent.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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