Pytorch-day09-模型微调-checkpoint
模型微调（fine-tune)-迁移学习
- torchvision微调
- timm微调
- 半精度训练
起源：
- 1、随着深度学习的发展，模型的参数越来越大，许多开源模型都是在较大数据集上进行训练的，比如Imagenet-1k，Imagenet-11k等
- 2、如果数据集可能只有几千张，训练几千万参数的大模型，过拟合无法避免
- 3、如果我们想从零开始训练一个大模型，那么我们的解决办法是收集更多的数据。然而，收集和标注数据会花费大量的时间和资⾦，成本无法承受
解决方案：
- 应用迁移学习(transfer learning)，将从源数据集学到的知识迁移到目标数据集上
- 比如：ImageNet数据集的图像大多跟椅子无关，但在该数据集上训练的模型可以抽取较通用的图像特征，从而能够帮助识别边缘、纹理、形状和物体组成
- 模型微调（finetune）:就是先找到一个同类的别人训练好的模型，基于已经训练好的模型换成自己的数据，通过训练调整一下参数
不同数据集下使用微调：
-
数据集1 - 数据量少，但数据相似度非常高 - 在这种情况下，我们所做的只是修改最后几层或最终的softmax图层的输出类别。
-
数据集2 - 数据量少，数据相似度低 - 在这种情况下，我们可以冻结预训练模型的初始层（比如k层），并再次训练剩余的（n-k）层。由于新数据集的相似度较低，因此根据新数据集对较高层进行重新训练具有重要意义。
-
数据集3 - 数据量大，数据相似度低 - 在这种情况下，由于我们有一个大的数据集，我们的神经网络训练将会很有效。但是，由于我们的数据与用于训练我们的预训练模型的数据相比有很大不同。使用预训练模型进行的预测不会有效。因此，最好根据你的数据从头开始训练神经网络（Training from scatch）
-
数据集4 - 数据量大，数据相似度高 - 这是理想情况。在这种情况下，预训练模型应该是最有效的。使用模型的最好方法是保留模型的体系结构和模型的初始权重。然后，我们可以使用在预先训练的模型中的权重来重新训练该模型。
微调的是什么？
- 换数据源
- 针对K层进行重新训练
- K层的权重&shape调整
1、模型微调(fine-tune)一般流程：
- 1、在源数据集(如ImageNet数据集)上预训练一个神经网络模型，即源模型
- 2、创建一个新的神经网络模型，即目标模型，它复制了源模型上除了输出层外的所有模型设计及其参数
- 3、为目标模型添加一个输出⼤小为⽬标数据集类别个数的输出层，并随机初始化该层的模型参数
- 4、在目标数据集上训练目标模型。我们将从头训练输出层，而其余层的参数都是基于源模型的参数微调得到的
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2、torchvision微调
2.1 实例化Model
import torchvision.models as models
resnet34 = models.resnet34(pretrained=True)
pretrained参数说明：
- 1、通过True或者False来决定是否使用预训练好的权重，在默认状态下pretrained = False，意味着我们不使用预训练得到的权重
- 2、当pretrained = True，意味着我们将使用在一些数据集上预训练得到的权重
注意：如果中途强行停止下载的话，一定要去对应路径下将权重文件删除干净，否则会报错。
2.2 训练特定层
如果我们正在提取特征并且只想为新初始化的层计算梯度，其他参数不进行改变。那我们就需要通过设置requires_grad = False来冻结部分层
def set_parameter_requires_grad(model, feature_extracting):
if feature_extracting:
for param in model.parameters():
param.requires_grad = False
2.3 实例
- 使用resnet34为例的将1000类改为10类，但是仅改变最后一层的模型参数
- 我们先冻结模型参数的梯度，再对模型输出部分的全连接层进行修改
import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.optim.lr_scheduler import LambdaLR
from torch.optim.lr_scheduler import StepLR
import torchvision
from torch.utils.data import Dataset, DataLoader
from torchvision.transforms import transforms
from torch.utils.tensorboard import SummaryWriter
import numpy as np
import torchvision.models as models
from torchinfo import summary
#超参数定义
# 批次的大小
batch_size = 16 #可选32、64、128
# 优化器的学习率
lr = 1e-4
#运行epoch
max_epochs = 2
# 方案二：使用“device”，后续对要使用GPU的变量用.to(device)即可
device = torch.device("cuda:1" if torch.cuda.is_available() else "cpu")
# 数据读取
#cifar10数据集为例给出构建Dataset类的方式
from torchvision import datasets
#“data_transform”可以对图像进行一定的变换，如翻转、裁剪、归一化等操作，可自己定义
data_transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))
])
train_cifar_dataset = datasets.CIFAR10('cifar10',train=True, download=False,transform=data_transform)
test_cifar_dataset = datasets.CIFAR10('cifar10',train=False, download=False,transform=data_transform)
#构建好Dataset后，就可以使用DataLoader来按批次读入数据了
train_loader = torch.utils.data.DataLoader(train_cifar_dataset,
batch_size=batch_size, num_workers=4,
shuffle=True, drop_last=True)
test_loader = torch.utils.data.DataLoader(test_cifar_dataset,
batch_size=batch_size, num_workers=4,
shuffle=False)
# 下载预训练模型 restnet50
resnet34 = models.resnet34(pretrained=True)
print(resnet34)
D:\Users\xulele\Anaconda3\lib\site-packages\torchvision\models\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
warnings.warn(
D:\Users\xulele\Anaconda3\lib\site-packages\torchvision\models\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet34_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet34_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/resnet34-b627a593.pth" to C:\Users\xulele/.cache\torch\hub\checkpoints\resnet34-b627a593.pth
100%|██████████| 83.3M/83.3M [00:10<00:00, 8.57MB/s]
ResNet(
(conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(layer1): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(1): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(2): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer2): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(2): BasicBlock(
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(3): BasicBlock(
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer3): Sequential(
(0): BasicBlock(
(conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(2): BasicBlock(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(3): BasicBlock(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(4): BasicBlock(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(5): BasicBlock(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer4): Sequential(
(0): BasicBlock(
(conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(2): BasicBlock(
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
(fc): Linear(in_features=512, out_features=1000, bias=True)
)
#查看模型结构
summary(resnet34, (1, 3, 224, 224))
==========================================================================================
Layer (type:depth-idx) Output Shape Param #
==========================================================================================
ResNet [1, 1000] --
├─Conv2d: 1-1 [1, 64, 112, 112] 9,408
├─BatchNorm2d: 1-2 [1, 64, 112, 112] 128
├─ReLU: 1-3 [1, 64, 112, 112] --
├─MaxPool2d: 1-4 [1, 64, 56, 56] --
├─Sequential: 1-5 [1, 64, 56, 56] --
│ └─BasicBlock: 2-1 [1, 64, 56, 56] --
│ │ └─Conv2d: 3-1 [1, 64, 56, 56] 36,864
│ │ └─BatchNorm2d: 3-2 [1, 64, 56, 56] 128
│ │ └─ReLU: 3-3 [1, 64, 56, 56] --
│ │ └─Conv2d: 3-4 [1, 64, 56, 56] 36,864
│ │ └─BatchNorm2d: 3-5 [1, 64, 56, 56] 128
│ │ └─ReLU: 3-6 [1, 64, 56, 56] --
│ └─BasicBlock: 2-2 [1, 64, 56, 56] --
│ │ └─Conv2d: 3-7 [1, 64, 56, 56] 36,864
│ │ └─BatchNorm2d: 3-8 [1, 64, 56, 56] 128
│ │ └─ReLU: 3-9 [1, 64, 56, 56] --
│ │ └─Conv2d: 3-10 [1, 64, 56, 56] 36,864
│ │ └─BatchNorm2d: 3-11 [1, 64, 56, 56] 128
│ │ └─ReLU: 3-12 [1, 64, 56, 56] --
│ └─BasicBlock: 2-3 [1, 64, 56, 56] --
│ │ └─Conv2d: 3-13 [1, 64, 56, 56] 36,864
│ │ └─BatchNorm2d: 3-14 [1, 64, 56, 56] 128
│ │ └─ReLU: 3-15 [1, 64, 56, 56] --
│ │ └─Conv2d: 3-16 [1, 64, 56, 56] 36,864
│ │ └─BatchNorm2d: 3-17 [1, 64, 56, 56] 128
│ │ └─ReLU: 3-18 [1, 64, 56, 56] --
├─Sequential: 1-6 [1, 128, 28, 28] --
│ └─BasicBlock: 2-4 [1, 128, 28, 28] --
│ │ └─Conv2d: 3-19 [1, 128, 28, 28] 73,728
│ │ └─BatchNorm2d: 3-20 [1, 128, 28, 28] 256
│ │ └─ReLU: 3-21 [1, 128, 28, 28] --
│ │ └─Conv2d: 3-22 [1, 128, 28, 28] 147,456
│ │ └─BatchNorm2d: 3-23 [1, 128, 28, 28] 256
│ │ └─Sequential: 3-24 [1, 128, 28, 28] 8,448
│ │ └─ReLU: 3-25 [1, 128, 28, 28] --
│ └─BasicBlock: 2-5 [1, 128, 28, 28] --
│ │ └─Conv2d: 3-26 [1, 128, 28, 28] 147,456
│ │ └─BatchNorm2d: 3-27 [1, 128, 28, 28] 256
│ │ └─ReLU: 3-28 [1, 128, 28, 28] --
│ │ └─Conv2d: 3-29 [1, 128, 28, 28] 147,456
│ │ └─BatchNorm2d: 3-30 [1, 128, 28, 28] 256
│ │ └─ReLU: 3-31 [1, 128, 28, 28] --
│ └─BasicBlock: 2-6 [1, 128, 28, 28] --
│ │ └─Conv2d: 3-32 [1, 128, 28, 28] 147,456
│ │ └─BatchNorm2d: 3-33 [1, 128, 28, 28] 256
│ │ └─ReLU: 3-34 [1, 128, 28, 28] --
│ │ └─Conv2d: 3-35 [1, 128, 28, 28] 147,456
│ │ └─BatchNorm2d: 3-36 [1, 128, 28, 28] 256
│ │ └─ReLU: 3-37 [1, 128, 28, 28] --
│ └─BasicBlock: 2-7 [1, 128, 28, 28] --
│ │ └─Conv2d: 3-38 [1, 128, 28, 28] 147,456
│ │ └─BatchNorm2d: 3-39 [1, 128, 28, 28] 256
│ │ └─ReLU: 3-40 [1, 128, 28, 28] --
│ │ └─Conv2d: 3-41 [1, 128, 28, 28] 147,456
│ │ └─BatchNorm2d: 3-42 [1, 128, 28, 28] 256
│ │ └─ReLU: 3-43 [1, 128, 28, 28] --
├─Sequential: 1-7 [1, 256, 14, 14] --
│ └─BasicBlock: 2-8 [1, 256, 14, 14] --
│ │ └─Conv2d: 3-44 [1, 256, 14, 14] 294,912
│ │ └─BatchNorm2d: 3-45 [1, 256, 14, 14] 512
│ │ └─ReLU: 3-46 [1, 256, 14, 14] --
│ │ └─Conv2d: 3-47 [1, 256, 14, 14] 589,824
│ │ └─BatchNorm2d: 3-48 [1, 256, 14, 14] 512
│ │ └─Sequential: 3-49 [1, 256, 14, 14] 33,280
│ │ └─ReLU: 3-50 [1, 256, 14, 14] --
│ └─BasicBlock: 2-9 [1, 256, 14, 14] --
│ │ └─Conv2d: 3-51 [1, 256, 14, 14] 589,824
│ │ └─BatchNorm2d: 3-52 [1, 256, 14, 14] 512
│ │ └─ReLU: 3-53 [1, 256, 14, 14] --
│ │ └─Conv2d: 3-54 [1, 256, 14, 14] 589,824
│ │ └─BatchNorm2d: 3-55 [1, 256, 14, 14] 512
│ │ └─ReLU: 3-56 [1, 256, 14, 14] --
│ └─BasicBlock: 2-10 [1, 256, 14, 14] --
│ │ └─Conv2d: 3-57 [1, 256, 14, 14] 589,824
│ │ └─BatchNorm2d: 3-58 [1, 256, 14, 14] 512
│ │ └─ReLU: 3-59 [1, 256, 14, 14] --
│ │ └─Conv2d: 3-60 [1, 256, 14, 14] 589,824
│ │ └─BatchNorm2d: 3-61 [1, 256, 14, 14] 512
│ │ └─ReLU: 3-62 [1, 256, 14, 14] --
│ └─BasicBlock: 2-11 [1, 256, 14, 14] --
│ │ └─Conv2d: 3-63 [1, 256, 14, 14] 589,824
│ │ └─BatchNorm2d: 3-64 [1, 256, 14, 14] 512
│ │ └─ReLU: 3-65 [1, 256, 14, 14] --
│ │ └─Conv2d: 3-66 [1, 256, 14, 14] 589,824
│ │ └─BatchNorm2d: 3-67 [1, 256, 14, 14] 512
│ │ └─ReLU: 3-68 [1, 256, 14, 14] --
│ └─BasicBlock: 2-12 [1, 256, 14, 14] --
│ │ └─Conv2d: 3-69 [1, 256, 14, 14] 589,824
│ │ └─BatchNorm2d: 3-70 [1, 256, 14, 14] 512
│ │ └─ReLU: 3-71 [1, 256, 14, 14] --
│ │ └─Conv2d: 3-72 [1, 256, 14, 14] 589,824
│ │ └─BatchNorm2d: 3-73 [1, 256, 14, 14] 512
│ │ └─ReLU: 3-74 [1, 256, 14, 14] --
│ └─BasicBlock: 2-13 [1, 256, 14, 14] --
│ │ └─Conv2d: 3-75 [1, 256, 14, 14] 589,824
│ │ └─BatchNorm2d: 3-76 [1, 256, 14, 14] 512
│ │ └─ReLU: 3-77 [1, 256, 14, 14] --
│ │ └─Conv2d: 3-78 [1, 256, 14, 14] 589,824
│ │ └─BatchNorm2d: 3-79 [1, 256, 14, 14] 512
│ │ └─ReLU: 3-80 [1, 256, 14, 14] --
├─Sequential: 1-8 [1, 512, 7, 7] --
│ └─BasicBlock: 2-14 [1, 512, 7, 7] --
│ │ └─Conv2d: 3-81 [1, 512, 7, 7] 1,179,648
│ │ └─BatchNorm2d: 3-82 [1, 512, 7, 7] 1,024
│ │ └─ReLU: 3-83 [1, 512, 7, 7] --
│ │ └─Conv2d: 3-84 [1, 512, 7, 7] 2,359,296
│ │ └─BatchNorm2d: 3-85 [1, 512, 7, 7] 1,024
│ │ └─Sequential: 3-86 [1, 512, 7, 7] 132,096
│ │ └─ReLU: 3-87 [1, 512, 7, 7] --
│ └─BasicBlock: 2-15 [1, 512, 7, 7] --
│ │ └─Conv2d: 3-88 [1, 512, 7, 7] 2,359,296
│ │ └─BatchNorm2d: 3-89 [1, 512, 7, 7] 1,024
│ │ └─ReLU: 3-90 [1, 512, 7, 7] --
│ │ └─Conv2d: 3-91 [1, 512, 7, 7] 2,359,296
│ │ └─BatchNorm2d: 3-92 [1, 512, 7, 7] 1,024
│ │ └─ReLU: 3-93 [1, 512, 7, 7] --
│ └─BasicBlock: 2-16 [1, 512, 7, 7] --
│ │ └─Conv2d: 3-94 [1, 512, 7, 7] 2,359,296
│ │ └─BatchNorm2d: 3-95 [1, 512, 7, 7] 1,024
│ │ └─ReLU: 3-96 [1, 512, 7, 7] --
│ │ └─Conv2d: 3-97 [1, 512, 7, 7] 2,359,296
│ │ └─BatchNorm2d: 3-98 [1, 512, 7, 7] 1,024
│ │ └─ReLU: 3-99 [1, 512, 7, 7] --
├─AdaptiveAvgPool2d: 1-9 [1, 512, 1, 1] --
├─Linear: 1-10 [1, 1000] 513,000
==========================================================================================
Total params: 21,797,672
Trainable params: 21,797,672
Non-trainable params: 0
Total mult-adds (G): 3.66
==========================================================================================
Input size (MB): 0.60
Forward/backward pass size (MB): 59.82
Params size (MB): 87.19
Estimated Total Size (MB): 147.61
==========================================================================================
#检测 模型准确率
def cal_predict_correct(model):
test_total_correct = 0
for iter,(images,labels) in enumerate(test_loader):
images = images.to(device)
labels = labels.to(device)
outputs = model(images)
test_total_correct += (outputs.argmax(1) == labels).sum().item()
# print("test_total_correct: "+ str(test_total_correct))
return test_total_correct
total_correct = cal_predict_correct(resnet34)
print("test_total_correct: "+ str(test_total_correct / 10000))
test_total_correct: 0.1
def set_parameter_requires_grad(model, feature_extracting):
if feature_extracting:
for param in model.parameters():
param.requires_grad = False
# 冻结参数的梯度
feature_extract = True
new_model = resnet34
set_parameter_requires_grad(new_model, feature_extract)
# 修改模型
#训练过程中，model仍会进行梯度回传，但是参数更新则只会发生在fc层
num_ftrs = new_model.fc.in_features
new_model.fc = nn.Linear(in_features=num_ftrs, out_features=10, bias=True)
summary(new_model, (1, 3, 224, 224))
==========================================================================================
Layer (type:depth-idx) Output Shape Param #
==========================================================================================
ResNet [1, 10] --
├─Conv2d: 1-1 [1, 64, 112, 112] (9,408)
├─BatchNorm2d: 1-2 [1, 64, 112, 112] (128)
├─ReLU: 1-3 [1, 64, 112, 112] --
├─MaxPool2d: 1-4 [1, 64, 56, 56] --
├─Sequential: 1-5 [1, 64, 56, 56] --
│ └─BasicBlock: 2-1 [1, 64, 56, 56] --
│ │ └─Conv2d: 3-1 [1, 64, 56, 56] (36,864)
│ │ └─BatchNorm2d: 3-2 [1, 64, 56, 56] (128)
│ │ └─ReLU: 3-3 [1, 64, 56, 56] --
│ │ └─Conv2d: 3-4 [1, 64, 56, 56] (36,864)
│ │ └─BatchNorm2d: 3-5 [1, 64, 56, 56] (128)
│ │ └─ReLU: 3-6 [1, 64, 56, 56] --
│ └─BasicBlock: 2-2 [1, 64, 56, 56] --
│ │ └─Conv2d: 3-7 [1, 64, 56, 56] (36,864)
│ │ └─BatchNorm2d: 3-8 [1, 64, 56, 56] (128)
│ │ └─ReLU: 3-9 [1, 64, 56, 56] --
│ │ └─Conv2d: 3-10 [1, 64, 56, 56] (36,864)
│ │ └─BatchNorm2d: 3-11 [1, 64, 56, 56] (128)
│ │ └─ReLU: 3-12 [1, 64, 56, 56] --
│ └─BasicBlock: 2-3 [1, 64, 56, 56] --
│ │ └─Conv2d: 3-13 [1, 64, 56, 56] (36,864)
│ │ └─BatchNorm2d: 3-14 [1, 64, 56, 56] (128)
│ │ └─ReLU: 3-15 [1, 64, 56, 56] --
│ │ └─Conv2d: 3-16 [1, 64, 56, 56] (36,864)
│ │ └─BatchNorm2d: 3-17 [1, 64, 56, 56] (128)
│ │ └─ReLU: 3-18 [1, 64, 56, 56] --
├─Sequential: 1-6 [1, 128, 28, 28] --
│ └─BasicBlock: 2-4 [1, 128, 28, 28] --
│ │ └─Conv2d: 3-19 [1, 128, 28, 28] (73,728)
│ │ └─BatchNorm2d: 3-20 [1, 128, 28, 28] (256)
│ │ └─ReLU: 3-21 [1, 128, 28, 28] --
│ │ └─Conv2d: 3-22 [1, 128, 28, 28] (147,456)
│ │ └─BatchNorm2d: 3-23 [1, 128, 28, 28] (256)
│ │ └─Sequential: 3-24 [1, 128, 28, 28] (8,448)
│ │ └─ReLU: 3-25 [1, 128, 28, 28] --
│ └─BasicBlock: 2-5 [1, 128, 28, 28] --
│ │ └─Conv2d: 3-26 [1, 128, 28, 28] (147,456)
│ │ └─BatchNorm2d: 3-27 [1, 128, 28, 28] (256)
│ │ └─ReLU: 3-28 [1, 128, 28, 28] --
│ │ └─Conv2d: 3-29 [1, 128, 28, 28] (147,456)
│ │ └─BatchNorm2d: 3-30 [1, 128, 28, 28] (256)
│ │ └─ReLU: 3-31 [1, 128, 28, 28] --
│ └─BasicBlock: 2-6 [1, 128, 28, 28] --
│ │ └─Conv2d: 3-32 [1, 128, 28, 28] (147,456)
│ │ └─BatchNorm2d: 3-33 [1, 128, 28, 28] (256)
│ │ └─ReLU: 3-34 [1, 128, 28, 28] --
│ │ └─Conv2d: 3-35 [1, 128, 28, 28] (147,456)
│ │ └─BatchNorm2d: 3-36 [1, 128, 28, 28] (256)
│ │ └─ReLU: 3-37 [1, 128, 28, 28] --
│ └─BasicBlock: 2-7 [1, 128, 28, 28] --
│ │ └─Conv2d: 3-38 [1, 128, 28, 28] (147,456)
│ │ └─BatchNorm2d: 3-39 [1, 128, 28, 28] (256)
│ │ └─ReLU: 3-40 [1, 128, 28, 28] --
│ │ └─Conv2d: 3-41 [1, 128, 28, 28] (147,456)
│ │ └─BatchNorm2d: 3-42 [1, 128, 28, 28] (256)
│ │ └─ReLU: 3-43 [1, 128, 28, 28] --
├─Sequential: 1-7 [1, 256, 14, 14] --
│ └─BasicBlock: 2-8 [1, 256, 14, 14] --
│ │ └─Conv2d: 3-44 [1, 256, 14, 14] (294,912)
│ │ └─BatchNorm2d: 3-45 [1, 256, 14, 14] (512)
│ │ └─ReLU: 3-46 [1, 256, 14, 14] --
│ │ └─Conv2d: 3-47 [1, 256, 14, 14] (589,824)
│ │ └─BatchNorm2d: 3-48 [1, 256, 14, 14] (512)
│ │ └─Sequential: 3-49 [1, 256, 14, 14] (33,280)
│ │ └─ReLU: 3-50 [1, 256, 14, 14] --
│ └─BasicBlock: 2-9 [1, 256, 14, 14] --
│ │ └─Conv2d: 3-51 [1, 256, 14, 14] (589,824)
│ │ └─BatchNorm2d: 3-52 [1, 256, 14, 14] (512)
│ │ └─ReLU: 3-53 [1, 256, 14, 14] --
│ │ └─Conv2d: 3-54 [1, 256, 14, 14] (589,824)
│ │ └─BatchNorm2d: 3-55 [1, 256, 14, 14] (512)
│ │ └─ReLU: 3-56 [1, 256, 14, 14] --
│ └─BasicBlock: 2-10 [1, 256, 14, 14] --
│ │ └─Conv2d: 3-57 [1, 256, 14, 14] (589,824)
│ │ └─BatchNorm2d: 3-58 [1, 256, 14, 14] (512)
│ │ └─ReLU: 3-59 [1, 256, 14, 14] --
│ │ └─Conv2d: 3-60 [1, 256, 14, 14] (589,824)
│ │ └─BatchNorm2d: 3-61 [1, 256, 14, 14] (512)
│ │ └─ReLU: 3-62 [1, 256, 14, 14] --
│ └─BasicBlock: 2-11 [1, 256, 14, 14] --
│ │ └─Conv2d: 3-63 [1, 256, 14, 14] (589,824)
│ │ └─BatchNorm2d: 3-64 [1, 256, 14, 14] (512)
│ │ └─ReLU: 3-65 [1, 256, 14, 14] --
│ │ └─Conv2d: 3-66 [1, 256, 14, 14] (589,824)
│ │ └─BatchNorm2d: 3-67 [1, 256, 14, 14] (512)
│ │ └─ReLU: 3-68 [1, 256, 14, 14] --
│ └─BasicBlock: 2-12 [1, 256, 14, 14] --
│ │ └─Conv2d: 3-69 [1, 256, 14, 14] (589,824)
│ │ └─BatchNorm2d: 3-70 [1, 256, 14, 14] (512)
│ │ └─ReLU: 3-71 [1, 256, 14, 14] --
│ │ └─Conv2d: 3-72 [1, 256, 14, 14] (589,824)
│ │ └─BatchNorm2d: 3-73 [1, 256, 14, 14] (512)
│ │ └─ReLU: 3-74 [1, 256, 14, 14] --
│ └─BasicBlock: 2-13 [1, 256, 14, 14] --
│ │ └─Conv2d: 3-75 [1, 256, 14, 14] (589,824)
│ │ └─BatchNorm2d: 3-76 [1, 256, 14, 14] (512)
│ │ └─ReLU: 3-77 [1, 256, 14, 14] --
│ │ └─Conv2d: 3-78 [1, 256, 14, 14] (589,824)
│ │ └─BatchNorm2d: 3-79 [1, 256, 14, 14] (512)
│ │ └─ReLU: 3-80 [1, 256, 14, 14] --
├─Sequential: 1-8 [1, 512, 7, 7] --
│ └─BasicBlock: 2-14 [1, 512, 7, 7] --
│ │ └─Conv2d: 3-81 [1, 512, 7, 7] (1,179,648)
│ │ └─BatchNorm2d: 3-82 [1, 512, 7, 7] (1,024)
│ │ └─ReLU: 3-83 [1, 512, 7, 7] --
│ │ └─Conv2d: 3-84 [1, 512, 7, 7] (2,359,296)
│ │ └─BatchNorm2d: 3-85 [1, 512, 7, 7] (1,024)
│ │ └─Sequential: 3-86 [1, 512, 7, 7] (132,096)
│ │ └─ReLU: 3-87 [1, 512, 7, 7] --
│ └─BasicBlock: 2-15 [1, 512, 7, 7] --
│ │ └─Conv2d: 3-88 [1, 512, 7, 7] (2,359,296)
│ │ └─BatchNorm2d: 3-89 [1, 512, 7, 7] (1,024)
│ │ └─ReLU: 3-90 [1, 512, 7, 7] --
│ │ └─Conv2d: 3-91 [1, 512, 7, 7] (2,359,296)
│ │ └─BatchNorm2d: 3-92 [1, 512, 7, 7] (1,024)
│ │ └─ReLU: 3-93 [1, 512, 7, 7] --
│ └─BasicBlock: 2-16 [1, 512, 7, 7] --
│ │ └─Conv2d: 3-94 [1, 512, 7, 7] (2,359,296)
│ │ └─BatchNorm2d: 3-95 [1, 512, 7, 7] (1,024)
│ │ └─ReLU: 3-96 [1, 512, 7, 7] --
│ │ └─Conv2d: 3-97 [1, 512, 7, 7] (2,359,296)
│ │ └─BatchNorm2d: 3-98 [1, 512, 7, 7] (1,024)
│ │ └─ReLU: 3-99 [1, 512, 7, 7] --
├─AdaptiveAvgPool2d: 1-9 [1, 512, 1, 1] --
├─Linear: 1-10 [1, 10] 5,130
==========================================================================================
Total params: 21,289,802
Trainable params: 5,130
Non-trainable params: 21,284,672
Total mult-adds (G): 3.66
==========================================================================================
Input size (MB): 0.60
Forward/backward pass size (MB): 59.81
Params size (MB): 85.16
Estimated Total Size (MB): 145.57
==========================================================================================
#训练&验证
Resnet34_new = new_model.to(device)
# 定义损失函数和优化器
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
# 损失函数：自定义损失函数
criterion = nn.CrossEntropyLoss()
# 优化器
optimizer = torch.optim.Adam(Resnet50_new.parameters(), lr=lr)
epoch = max_epochs
total_step = len(train_loader)
train_all_loss = []
test_all_loss = []
for i in range(epoch):
Resnet34_new.train()
train_total_loss = 0
train_total_num = 0
train_total_correct = 0
for iter, (images,labels) in enumerate(train_loader):
images = images.to(device)
labels = labels.to(device)
outputs = Resnet34_new(images)
loss = criterion(outputs,labels)
train_total_correct += (outputs.argmax(1) == labels).sum().item()
#backword
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_total_num += labels.shape[0]
train_total_loss += loss.item()
print("Epoch [{}/{}], Iter [{}/{}], train_loss:{:4f}".format(i+1,epoch,iter+1,total_step,loss.item()/labels.shape[0]))
Resnet34_new.eval()
test_total_loss = 0
test_total_correct = 0
test_total_num = 0
for iter,(images,labels) in enumerate(test_loader):
images = images.to(device)
labels = labels.to(device)
outputs = Resnet34_new(images)
loss = criterion(outputs,labels)
test_total_correct += (outputs.argmax(1) == labels).sum().item()
test_total_loss += loss.item()
test_total_num += labels.shape[0]
print("Epoch [{}/{}], train_loss:{:.4f}, train_acc:{:.4f}%, test_loss:{:.4f}, test_acc:{:.4f}%".format(
i+1, epoch, train_total_loss / train_total_num, train_total_correct / train_total_num * 100, test_total_loss / test_total_num, test_total_correct / test_total_num * 100
))
train_all_loss.append(np.round(train_total_loss / train_total_num,4))
test_all_loss.append(np.round(test_total_loss / test_total_num,4))
Epoch [1/2], Iter [1/3125], train_loss:0.150127
Epoch [1/2], Iter [2/3125], train_loss:0.174470
Epoch [1/2], Iter [3/3125], train_loss:0.165727
Epoch [1/2], Iter [4/3125], train_loss:0.174811
Epoch [1/2], Iter [5/3125], train_loss:0.158658
Epoch [1/2], Iter [6/3125], train_loss:0.153260
Epoch [1/2], Iter [7/3125], train_loss:0.164495
Epoch [1/2], Iter [8/3125], train_loss:0.164485
Epoch [1/2], Iter [9/3125], train_loss:0.157202
Epoch [1/2], Iter [10/3125], train_loss:0.149555
Epoch [1/2], Iter [11/3125], train_loss:0.172609
Epoch [1/2], Iter [12/3125], train_loss:0.180861
Epoch [1/2], Iter [13/3125], train_loss:0.156719
Epoch [1/2], Iter [14/3125], train_loss:0.172375
Epoch [1/2], Iter [15/3125], train_loss:0.169886
Epoch [1/2], Iter [16/3125], train_loss:0.148726
Epoch [1/2], Iter [17/3125], train_loss:0.160391
Epoch [1/2], Iter [18/3125], train_loss:0.160285
Epoch [1/2], Iter [19/3125], train_loss:0.167672
Epoch [1/2], Iter [20/3125], train_loss:0.151213
Epoch [1/2], Iter [21/3125], train_loss:0.154690
Epoch [1/2], Iter [22/3125], train_loss:0.155165
Epoch [1/2], Iter [23/3125], train_loss:0.162777
Epoch [1/2], Iter [24/3125], train_loss:0.169136
Epoch [1/2], Iter [25/3125], train_loss:0.151533
Epoch [1/2], Iter [26/3125], train_loss:0.168992
Epoch [1/2], Iter [27/3125], train_loss:0.176258
Epoch [1/2], Iter [28/3125], train_loss:0.162240
Epoch [1/2], Iter [29/3125], train_loss:0.161768
Epoch [1/2], Iter [30/3125], train_loss:0.165359
Epoch [1/2], Iter [31/3125], train_loss:0.166174
Epoch [1/2], Iter [32/3125], train_loss:0.173654
Epoch [1/2], Iter [33/3125], train_loss:0.162488
Epoch [1/2], Iter [34/3125], train_loss:0.164815
Epoch [1/2], Iter [35/3125], train_loss:0.154411
Epoch [1/2], Iter [36/3125], train_loss:0.159386
Epoch [1/2], Iter [37/3125], train_loss:0.176261
Epoch [1/2], Iter [38/3125], train_loss:0.163848
Epoch [1/2], Iter [39/3125], train_loss:0.174402
Epoch [1/2], Iter [40/3125], train_loss:0.178917
Epoch [1/2], Iter [41/3125], train_loss:0.149938
Epoch [1/2], Iter [42/3125], train_loss:0.156186
Epoch [1/2], Iter [43/3125], train_loss:0.162950
Epoch [1/2], Iter [44/3125], train_loss:0.169058
Epoch [1/2], Iter [45/3125], train_loss:0.168587
Epoch [1/2], Iter [46/3125], train_loss:0.173754
Epoch [1/2], Iter [47/3125], train_loss:0.158612
Epoch [1/2], Iter [48/3125], train_loss:0.163891
Epoch [1/2], Iter [49/3125], train_loss:0.149220
Epoch [1/2], Iter [50/3125], train_loss:0.175387
Epoch [1/2], Iter [51/3125], train_loss:0.163082
Epoch [1/2], Iter [52/3125], train_loss:0.156597
Epoch [1/2], Iter [53/3125], train_loss:0.179248
Epoch [1/2], Iter [54/3125], train_loss:0.170053
Epoch [1/2], Iter [55/3125], train_loss:0.140899
Epoch [1/2], Iter [56/3125], train_loss:0.168686
Epoch [1/2], Iter [57/3125], train_loss:0.189548
Epoch [1/2], Iter [58/3125], train_loss:0.169847
Epoch [1/2], Iter [59/3125], train_loss:0.171854
Epoch [1/2], Iter [60/3125], train_loss:0.175660
Epoch [1/2], Iter [61/3125], train_loss:0.163686
Epoch [1/2], Iter [62/3125], train_loss:0.174950
Epoch [1/2], Iter [63/3125], train_loss:0.173237
Epoch [1/2], Iter [64/3125], train_loss:0.146743
Epoch [1/2], Iter [65/3125], train_loss:0.159798
Epoch [1/2], Iter [66/3125], train_loss:0.169616
Epoch [1/2], Iter [67/3125], train_loss:0.167541
Epoch [1/2], Iter [68/3125], train_loss:0.136470
Epoch [1/2], Iter [69/3125], train_loss:0.185080
Epoch [1/2], Iter [70/3125], train_loss:0.166373
Epoch [1/2], Iter [71/3125], train_loss:0.160634
Epoch [1/2], Iter [72/3125], train_loss:0.163522
Epoch [1/2], Iter [73/3125], train_loss:0.157858
Epoch [1/2], Iter [74/3125], train_loss:0.157069
Epoch [1/2], Iter [75/3125], train_loss:0.183969
Epoch [1/2], Iter [76/3125], train_loss:0.166041
Epoch [1/2], Iter [77/3125], train_loss:0.151215
Epoch [1/2], Iter [78/3125], train_loss:0.164155
Epoch [1/2], Iter [79/3125], train_loss:0.158990
Epoch [1/2], Iter [80/3125], train_loss:0.178859
Epoch [1/2], Iter [81/3125], train_loss:0.139378
Epoch [1/2], Iter [82/3125], train_loss:0.150422
Epoch [1/2], Iter [83/3125], train_loss:0.155447
Epoch [1/2], Iter [84/3125], train_loss:0.146703
Epoch [1/2], Iter [85/3125], train_loss:0.165099
Epoch [1/2], Iter [86/3125], train_loss:0.175539
Epoch [1/2], Iter [87/3125], train_loss:0.178613
Epoch [1/2], Iter [88/3125], train_loss:0.169430
Epoch [1/2], Iter [89/3125], train_loss:0.160620
Epoch [1/2], Iter [90/3125], train_loss:0.172726
Epoch [1/2], Iter [91/3125], train_loss:0.139834
Epoch [1/2], Iter [92/3125], train_loss:0.162758
Epoch [1/2], Iter [93/3125], train_loss:0.160110
Epoch [1/2], Iter [94/3125], train_loss:0.176203
Epoch [1/2], Iter [95/3125], train_loss:0.170835
Epoch [1/2], Iter [96/3125], train_loss:0.166727
Epoch [1/2], Iter [97/3125], train_loss:0.175421
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Epoch [1/2], Iter [1238/3125], train_loss:0.165396
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Epoch [1/2], Iter [1255/3125], train_loss:0.166119
Epoch [1/2], Iter [1256/3125], train_loss:0.140963
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Epoch [1/2], Iter [1258/3125], train_loss:0.158394
Epoch [1/2], Iter [1259/3125], train_loss:0.161410
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Epoch [1/2], Iter [1264/3125], train_loss:0.163797
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Epoch [1/2], Iter [1277/3125], train_loss:0.157448
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Epoch [1/2], Iter [1295/3125], train_loss:0.151002
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Epoch [1/2], Iter [1301/3125], train_loss:0.181966
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Epoch [1/2], Iter [1309/3125], train_loss:0.176630
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Epoch [1/2], Iter [1329/3125], train_loss:0.145549
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Epoch [1/2], Iter [1336/3125], train_loss:0.183256
Epoch [1/2], Iter [1337/3125], train_loss:0.167704
Epoch [1/2], Iter [1338/3125], train_loss:0.154254
Epoch [1/2], Iter [1339/3125], train_loss:0.162098
Epoch [1/2], Iter [1340/3125], train_loss:0.161697
Epoch [1/2], Iter [1341/3125], train_loss:0.164405
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Epoch [1/2], Iter [1343/3125], train_loss:0.171982
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Epoch [1/2], Iter [1345/3125], train_loss:0.147691
Epoch [1/2], Iter [1346/3125], train_loss:0.160214
Epoch [1/2], Iter [1347/3125], train_loss:0.154677
Epoch [1/2], Iter [1348/3125], train_loss:0.152759
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Epoch [1/2], Iter [1350/3125], train_loss:0.163566
Epoch [1/2], Iter [1351/3125], train_loss:0.150434
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Epoch [1/2], Iter [1353/3125], train_loss:0.162513
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Epoch [1/2], Iter [1355/3125], train_loss:0.158046
Epoch [1/2], Iter [1356/3125], train_loss:0.151754
Epoch [1/2], Iter [1357/3125], train_loss:0.170661
Epoch [1/2], Iter [1358/3125], train_loss:0.152679
Epoch [1/2], Iter [1359/3125], train_loss:0.167173
Epoch [1/2], Iter [1360/3125], train_loss:0.156606
Epoch [1/2], Iter [1361/3125], train_loss:0.183170
Epoch [1/2], Iter [1362/3125], train_loss:0.142545
Epoch [1/2], Iter [1363/3125], train_loss:0.159119
Epoch [1/2], Iter [1364/3125], train_loss:0.164405
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Epoch [1/2], Iter [1366/3125], train_loss:0.161490
Epoch [1/2], Iter [1367/3125], train_loss:0.167248
Epoch [1/2], Iter [1368/3125], train_loss:0.165266
Epoch [1/2], Iter [1369/3125], train_loss:0.164672
Epoch [1/2], Iter [1370/3125], train_loss:0.178968
Epoch [1/2], Iter [1371/3125], train_loss:0.139022
Epoch [1/2], Iter [1372/3125], train_loss:0.157129
Epoch [1/2], Iter [1373/3125], train_loss:0.170236
Epoch [1/2], Iter [1374/3125], train_loss:0.172654
Epoch [1/2], Iter [1375/3125], train_loss:0.154364
Epoch [1/2], Iter [1376/3125], train_loss:0.191031
Epoch [1/2], Iter [1377/3125], train_loss:0.154899
Epoch [1/2], Iter [1378/3125], train_loss:0.154030
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Epoch [1/2], Iter [1384/3125], train_loss:0.148632
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Epoch [2/2], Iter [5/3125], train_loss:0.175999
Epoch [2/2], Iter [6/3125], train_loss:0.163998
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Epoch [2/2], Iter [9/3125], train_loss:0.148239
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Epoch [2/2], Iter [13/3125], train_loss:0.147608
Epoch [2/2], Iter [14/3125], train_loss:0.160673
Epoch [2/2], Iter [15/3125], train_loss:0.172021
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Epoch [2/2], Iter [24/3125], train_loss:0.147863
Epoch [2/2], Iter [25/3125], train_loss:0.176202
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Epoch [2/2], Iter [27/3125], train_loss:0.168702
Epoch [2/2], Iter [28/3125], train_loss:0.151392
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Epoch [2/2], Iter [30/3125], train_loss:0.147167
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Epoch [2/2], Iter [34/3125], train_loss:0.156876
Epoch [2/2], Iter [35/3125], train_loss:0.149783
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Epoch [2/2], Iter [39/3125], train_loss:0.165627
Epoch [2/2], Iter [40/3125], train_loss:0.169342
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Epoch [2/2], Iter [51/3125], train_loss:0.169141
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Epoch [2/2], Iter [55/3125], train_loss:0.147851
Epoch [2/2], Iter [56/3125], train_loss:0.166568
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Epoch [2/2], Iter [61/3125], train_loss:0.162462
Epoch [2/2], Iter [62/3125], train_loss:0.167202
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Epoch [2/2], Iter [64/3125], train_loss:0.147543
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Epoch [2/2], Iter [67/3125], train_loss:0.182125
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Epoch [2/2], Iter [70/3125], train_loss:0.166233
Epoch [2/2], Iter [71/3125], train_loss:0.165497
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Epoch [2/2], Iter [76/3125], train_loss:0.176339
Epoch [2/2], Iter [77/3125], train_loss:0.157601
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Epoch [2/2], Iter [79/3125], train_loss:0.171131
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Epoch [2/2], Iter [81/3125], train_loss:0.165504
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Epoch [2/2], Iter [83/3125], train_loss:0.149121
Epoch [2/2], Iter [84/3125], train_loss:0.170251
Epoch [2/2], Iter [85/3125], train_loss:0.176452
Epoch [2/2], Iter [86/3125], train_loss:0.163143
Epoch [2/2], Iter [87/3125], train_loss:0.149888
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Epoch [2/2], Iter [95/3125], train_loss:0.143823
Epoch [2/2], Iter [96/3125], train_loss:0.147380
Epoch [2/2], Iter [97/3125], train_loss:0.162991
Epoch [2/2], Iter [98/3125], train_loss:0.142088
Epoch [2/2], Iter [99/3125], train_loss:0.165098
Epoch [2/2], Iter [100/3125], train_loss:0.142414
Epoch [2/2], Iter [101/3125], train_loss:0.171030
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Epoch [2/2], Iter [103/3125], train_loss:0.155812
Epoch [2/2], Iter [104/3125], train_loss:0.166394
Epoch [2/2], Iter [105/3125], train_loss:0.162388
Epoch [2/2], Iter [106/3125], train_loss:0.156700
Epoch [2/2], Iter [107/3125], train_loss:0.153787
Epoch [2/2], Iter [108/3125], train_loss:0.146724
Epoch [2/2], Iter [109/3125], train_loss:0.146993
Epoch [2/2], Iter [110/3125], train_loss:0.161078
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Epoch [2/2], Iter [112/3125], train_loss:0.164413
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Epoch [2/2], Iter [114/3125], train_loss:0.133704
Epoch [2/2], Iter [115/3125], train_loss:0.156570
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Epoch [2/2], Iter [117/3125], train_loss:0.172428
Epoch [2/2], Iter [118/3125], train_loss:0.158011
Epoch [2/2], Iter [119/3125], train_loss:0.180269
Epoch [2/2], Iter [120/3125], train_loss:0.133947
Epoch [2/2], Iter [121/3125], train_loss:0.160919
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Epoch [2/2], Iter [124/3125], train_loss:0.170647
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Epoch [2/2], Iter [134/3125], train_loss:0.157290
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Epoch [2/2], Iter [146/3125], train_loss:0.170075
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Epoch [2/2], Iter [149/3125], train_loss:0.184253
Epoch [2/2], Iter [150/3125], train_loss:0.178670
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Epoch [2/2], Iter [152/3125], train_loss:0.175516
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Epoch [2/2], Iter [154/3125], train_loss:0.161324
Epoch [2/2], Iter [155/3125], train_loss:0.156795
Epoch [2/2], Iter [156/3125], train_loss:0.154852
Epoch [2/2], Iter [157/3125], train_loss:0.156921
Epoch [2/2], Iter [158/3125], train_loss:0.163482
Epoch [2/2], Iter [159/3125], train_loss:0.173362
Epoch [2/2], Iter [160/3125], train_loss:0.167319
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Epoch [2/2], Iter [166/3125], train_loss:0.164563
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Epoch [2/2], Iter [168/3125], train_loss:0.169574
Epoch [2/2], Iter [169/3125], train_loss:0.175531
Epoch [2/2], Iter [170/3125], train_loss:0.169590
Epoch [2/2], Iter [171/3125], train_loss:0.157394
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Epoch [2/2], Iter [174/3125], train_loss:0.169188
Epoch [2/2], Iter [175/3125], train_loss:0.181089
Epoch [2/2], Iter [176/3125], train_loss:0.157710
Epoch [2/2], Iter [177/3125], train_loss:0.154907
Epoch [2/2], Iter [178/3125], train_loss:0.139118
Epoch [2/2], Iter [179/3125], train_loss:0.148639
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