学习笔记|Pytorch使用教程30(PyTorch常见报错 )

学习笔记|Pytorch使用教程30

本学习笔记主要摘自“深度之眼”，做一个总结，方便查阅。
使用Pytorch版本为1.2

• PyTorch常见报错
• PyTorch框架训练营课程总结

一.PyTorch常见报错

共同贡献PyTorch常见错误与坑汇总文档:
《PyTorch常 见报错/坑汇总》

1.报错: ValueError: num_samples should be a positive integer value, but got num_samples=0

可能的原因:

• 传入的Dataset中的len(self.data_ info)= =0，即传入该dataloader的dataset里没有数据

解决方法:

• 检查dataset中的路径
• 检查Dataset的len__.()函数为何输出为零

测试代码:

import os
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.models as models
from torch.utils.data import DataLoader
from tools.my_dataset import RMBDataset
from torch.utils.data import Dataset
from model.lenet import LeNet
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# ========================== 1 num_samples=0
# flag = 0
flag = 1
if flag:
    train_dir = os.path.join("..", "data", "rmb_split", "train")
    # train_dir = os.path.join("..", "..", "data", "rmb_split", "train")
    train_data = RMBDataset(data_dir=train_dir)

    # 构建DataLoder
    train_loader = DataLoader(dataset=train_data, batch_size=16, shuffle=True)

输出:

Traceback (most recent call last):
  File "common_errors.py", line 32, in <module>
    train_loader = DataLoader(dataset=train_data, batch_size=16, shuffle=True)
  File "/home/omnisky/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 213, in __init__
    sampler = RandomSampler(dataset)
  File "/home/omnisky/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/utils/data/sampler.py", line 94, in __init__
    "value, but got num_samples={}".format(self.num_samples))
ValueError: num_samples should be a positive integer value, but got num_samples=0

这是路径错误，设置train_dir = os.path.join("..", "..", "data", "rmb_split", "train")

2.报错: TypeError: pic should be PIL Image or ndarray. Got 'torch.Tensor >
可能的原因:

• 当前操作需要PIL Image或ndarray数据类型,但传入了Tensor

解决方法:

• 检查transform中是否存在两次ToTensor()方法
• 检查transform中每一一个操作的数据类型变化

测试代码:

# ========================== 2
# TypeError: pic should be PIL Image or ndarray. Got 
# flag = 0
flag = 1
if flag:
    train_transform = transforms.Compose([
        transforms.Resize((224, 224)),

        transforms.FiveCrop(200),
        transforms.Lambda(lambda crops: torch.stack([(transforms.ToTensor()(crop)) for crop in crops])),

        transforms.ToTensor(),
        # transforms.ToTensor(),
    ])

    train_dir = os.path.join("..", "..", "data", "rmb_split", "train")
    train_data = RMBDataset(data_dir=train_dir, transform=train_transform)
    train_loader = DataLoader(dataset=train_data, batch_size=16, shuffle=True)

    data, label = next(iter(train_loader))

输出:

Traceback (most recent call last):
  File "common_errors.py", line 54, in <module>
    data, label = next(iter(train_loader))
  File "/home/omnisky/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 346, in __next__
    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
  File "/home/omnisky/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/home/omnisky/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/home/omnisky/zdh1911/pytorch/07-04-代码-PyTorch常见报错/lesson-31/tools/my_dataset.py", line 34, in __getitem__
    img = self.transform(img)   # 在这里做transform，转为tensor等等
  File "/home/omnisky/anaconda3/envs/pytorch/lib/python3.7/site-packages/torchvision/transforms/transforms.py", line 61, in __call__
    img = t(img)
  File "/home/omnisky/anaconda3/envs/pytorch/lib/python3.7/site-packages/torchvision/transforms/transforms.py", line 92, in __call__
    return F.to_tensor(pic)
  File "/home/omnisky/anaconda3/envs/pytorch/lib/python3.7/site-packages/torchvision/transforms/functional.py", line 54, in to_tensor
    raise TypeError('pic should be PIL Image or ndarray. Got {}'.format(type(pic)))
TypeError: pic should be PIL Image or ndarray. Got <class 'torch.Tensor'>

需要注释代码:transforms.ToTensor(),

3.报错: RuntimeError: invalid argument 0: Sizes of tensors must match except in dimension 0. Got 93 and 89 in dimension 1 at /Users/soumith/code/builder/wheel/pytorch-src/aten/src/TH/generic/THTensorMath.cpp:3616

可能的原因:

• dataloader的__ getitem__ 函数中 ，返回的图片形状不一致，导致无法stack

解决方法:

• 检查__ getitem__ 函数中的操作

测试代码:

# ========================== 3
# RuntimeError: invalid argument 0: Sizes of tensors must match except in dimension 0
# flag = 0
flag = 1
if flag:

    class FooDataset(Dataset):
        def __init__(self,  num_data, data_dir=None, transform=None):
            self.foo = data_dir
            self.transform = transform
            self.num_data = num_data

        def __getitem__(self, item):

            size = torch.randint( 64, size=(1, ))
            fake_data = torch.zeros((3, size, size))
            fake_label = torch.randint(0, 10, size=(1, ))

            return fake_data, fake_label

        def __len__(self):
            return self.num_data


    foo_dataset = FooDataset(num_data=10)
    foo_dataloader = DataLoader(dataset=foo_dataset, batch_size=4)

    data, label = next(iter(foo_dataloader))

输出:

......
   return torch.stack(batch, 0, out=out)
RuntimeError: invalid argument 0: Sizes of tensors must match except in dimension 0. Got 4 and 23 in dimension 2 at /pytorch/aten/src/TH/generic/THTensor.cpp:689

尺度(shape)不匹配，设置代码为:size = torch.randint(63, 64, size=(1, ))

4.报错：
conv: RuntimeError: Given groups=1, weight of size 6 1 5 5, expected input[16, 3, 32, 32] to have 1 channels, but got 3 channels instead linear: RuntimeError: size mismatch, m1: [16 x 576], m2: [400 x 120] at …/aten/src/TH/generic/THTensorMath.cpp:752

可能的原因:

• 网络层输入数据与网络的参数不匹配

解决方法:

• 检查对应网络层前后定义是否有误
• 检查输入数据shape

测试代码:

# ========================== 4
# Given groups=1, weight of size 6 3 5 5, expected input[16, 1, 32, 32] to have 3 channels, but got 1 channels instead
# RuntimeError: size mismatch, m1: [16 x 576], m2: [400 x 120] at ../aten/src/TH/generic/THTensorMath.cpp:752
# flag = 0
flag = 1
if flag:

    class FooDataset(Dataset):
        def __init__(self,  num_data, shape, data_dir=None, transform=None):
            self.foo = data_dir
            self.transform = transform
            self.num_data = num_data
            self.shape = shape

        def __getitem__(self, item):

            fake_data = torch.zeros(self.shape)
            fake_label = torch.randint(0, 10, size=(1, ))

            if self.transform is not None:
                fake_data = self.transform(fake_data)

            return fake_data, fake_label

        def __len__(self):
            return self.num_data

    # ============================ step 1/5 数据 ============================
    channel = 1     # 1 3
    img_size = 32   # 36 32
    train_data = FooDataset(num_data=32, shape=(channel, img_size, img_size))
    train_loader = DataLoader(dataset=train_data, batch_size=16, shuffle=True)

    # ============================ step 2/5 模型 ============================
    net = LeNet(classes=2)
    # ============================ step 3/5 损失函数 ============================
    criterion = nn.CrossEntropyLoss()
    # ============================ step 4/5 优化器 ============================
    optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9)  # 选择优化器
    scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10)
    # ============================ step 5/5 训练 ============================
    data, label = next(iter(train_loader))
    outputs = net(data)

输出:

......
    self.padding, self.dilation, self.groups)
RuntimeError: Given groups=1, weight of size 6 3 5 5, expected input[16, 1, 32, 32] to have 3 channels, but got 1 channels instead

输入输出不匹配，修改代码:channel = 3
如果设置img_size = 36
报错:

......
RuntimeError: size mismatch, m1: [16 x 576], m2: [400 x 120] at /pytorch/aten/src/TH/generic/THTensorMath.cpp:197

5.报错: AttributeError: ‘DataParallel’ object has no attribute ‘linear’

可能的原因:

• 并行运算时，模型被dataparallel包装，所有module都增加一个属性module.因此需要通过net.module.linear调用

解决方法:

• 网络层前加入module.

测试代码:

# ========================== 5
# AttributeError: 'DataParallel' object has no attribute 'linear'
# flag = 0
flag = 1
if flag:
    class FooNet(nn.Module):
        def __init__(self):
            super(FooNet, self).__init__()
            self.linear = nn.Linear(3, 3, bias=True)
            self.conv1 = nn.Conv2d(3, 6, 5)
            self.pool1 = nn.MaxPool2d(5)

        def forward(self, x):
            return 1234567890

    net = FooNet()

    for layer_name, layer in net.named_modules():
        print(layer_name)

    net = nn.DataParallel(net)

    for layer_name, layer in net.named_modules():
        print(layer_name)

    print(net.linear)

输出:

linear
conv1
pool1

module
module.linear
module.conv1
module.pool1
Traceback (most recent call last):
  File "common_errors.py", line 157, in <module>
    print(net.linear)
  File "/home/omnisky/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py", line 585, in __getattr__
    type(self).__name__, name))
AttributeError: 'DataParallel' object has no attribute 'linear'

修改代码:print(net.module.linear)
输出:

linear
conv1
pool1

module
module.linear
module.conv1
module.pool1
Linear(in_features=3, out_features=3, bias=True)

6.报错：
RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_ available() is False. If you are running on a CPU-only machine, please use torch.load with map_location= torch.device(‘cpu’) to map your storages to the CPU.
可能的原因:

• gpu训练的模型保存后，在无gpu设备上无法直接加载

解决方法:

• 需要设置map_ location= “cpu”

# ========================== 6
# RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False.
#  If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu')
# to map your storages to the CPU.

# flag = 0
flag = 1
if flag:

    path_state_dict = "./model_in_multi_gpu.pkl"
    state_dict_load = torch.load(path_state_dict)
    # state_dict_load = torch.load(path_state_dict, map_location="cpu")

7.报错：
AttributeError: Can’t get attribute ‘FooNet2’ on main’ from
可能的原因:

• 保存的网络模型在当前python脚本中没有定义

解决方法:

• 提前定义该类

测试代码:

# ========================== 7
# AttributeError: Can't get attribute 'FooNet2' on 
# flag = 0
flag = 1
if flag:
    path_net = os.path.join(BASE_DIR, "foo_net.pkl")

    # save
    """
    class FooNet2(nn.Module):
        def __init__(self):
            super(FooNet2, self).__init__()
            self.linear = nn.Linear(3, 3, bias=True)

        def forward(self, x):
            return 1234567890
    """
    #
    # net = FooNet2()
    # torch.save(net, path_net)

    # load

    net_load = torch.load(path_net)

输出:

......
AttributeError: Can't get attribute 'FooNet2' on __main__' from 'common_errors.py'>

这是因为类没有被定义。

8.报错:
RuntimeError: Assertion ‘cur. target >= 0 && cur. target < n classes’ failed. at …/aten/src/THNN/generic/ClassNLLCriterion.c:94
可能的原因:

• 标签数大于等于类别数量，即不满足cur_ target < n_ classes，通常是因为标签从1开始而不是从0开始

解决方法:

• 修改label从0开始

测试代码:

# ========================== 8
# RuntimeError: Assertion `cur_target >= 0 && cur_target < n_classes' failed.
# flag = 0
flag = 1
if flag:

    inputs = torch.tensor([[1, 2], [1, 3], [1, 3]], dtype=torch.float)
    target = torch.tensor([0, 0, 2], dtype=torch.long)

    criterion = nn.CrossEntropyLoss()

    loss = criterion(inputs, target)

输出:

.......
RuntimeError: Assertion `cur_target >= 0 && cur_target < n_classes' failed.  at /pytorch/aten/src/THNN/generic/ClassNLLCriterion.c:97

修改代码为:target = torch.tensor([0, 0, 1], dtype=torch.long)

9.报错:
RuntimeError: expected device cuda:0 and dtype Long but got device cpu and dtype Long
Expected object of backend CPU but got backend CUDA for argument #2 ‘weight’
可能的原因:

• 需计算的两个数据不在同一个设备上

解决方法:

• 采用to函数将数据迁移到同一个设备上

测试代码:

# ========================== 9
# RuntimeError: expected device cuda:0 and dtype Long but got device cpu and dtype Long
# flag = 0
flag = 1
if flag:

    x = torch.tensor([1])
    w = torch.tensor([2]).to(device)
    y = w * x

    #x = x.to(device)
    #y = w * x

输出:

    y = w * x
RuntimeError: expected device cuda:0 but got device cpu

修改为:

flag = 1
if flag:

    x = torch.tensor([1])
    w = torch.tensor([2]).to(device)
    # y = w * x
    x = x.to(device)
    y = w * x

输出:

outputs device:cuda:0
outputs device:cuda:0

10.报错：
RuntimeError: DataLoader worker (pid 27) is killed by signal: Killed. Details are lost due to multiprocessing. Rerunning with num_ workers=0 may give better error trace.
可能原因:

• 内存不够(不是gpu显存，是内存)

解决方法:

• 申请更大内存

测试代码:

# ========================== 10
# RuntimeError: Expected object of backend CPU but got backend CUDA for argument #2 'weight'
# flag = 0
flag = 1
if flag:
    def data_loader(num_data):
        for _ in range(num_data):
            img_ = torch.randn(1, 3, 224, 224)
            label_ = torch.randint(0, 10, size=(1,))
            yield img_, label_

    resnet18 = models.resnet18()
    resnet18.to(device)

    for inputs, labels in data_loader(2):

        inputs.to(device)
        labels.to(device)
        outputs = resnet18(inputs)
        
        """
        inputs = inputs.to(device)
        labels = labels.to(device)
        outputs = resnet18(inputs)
        """
        print("outputs device:{}".format(outputs.device))

输出:

RuntimeError: Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same

修改代码:

        inputs = inputs.to(device)
        labels = labels.to(device)
        outputs = resnet18(inputs)

输出:

outputs device:cuda:0
outputs device:cuda:0

二.PyTorch框架训练营课程总结