Pytorch-day06-复杂模型构建-checkpoint
1、PyTorch 复杂模型构建
- 1、模型截图
- 2、模型部件实现
- 3、模型组装
2、模型定义
2.1、Sequential
- 1、当模型的前向计算为简单串联各个层的计算时， Sequential 类可以通过更加简单的方式定义模型。
- 2、可以接收一个子模块的有序字典(OrderedDict) 或者一系列子模块作为参数来逐一添加 Module 的实例，模型的前向计算就是将这些实例按添加的顺序逐⼀计算
- 3、使用Sequential定义模型的好处在于简单、易读，同时使用Sequential定义的模型不需要再写forward
import torch.nn as nn
net = nn.Sequential(
nn.Linear(784, 256),
nn.ReLU(),
nn.Linear(256, 10),
)
print(net)
Sequential(
(0): Linear(in_features=784, out_features=256, bias=True)
(1): ReLU()
(2): Linear(in_features=256, out_features=10, bias=True)
)
import collections
import torch.nn as nn
net2 = nn.Sequential(collections.OrderedDict([
('fc1', nn.Linear(784, 256)),
('relu1', nn.ReLU()),
('fc2', nn.Linear(256, 10))
]))
print(net2)
Sequential(
(fc1): Linear(in_features=784, out_features=256, bias=True)
(relu1): ReLU()
(fc2): Linear(in_features=256, out_features=10, bias=True)
)
2.2、ModuleList
- ModuleList 接收一个子模块（或层，需属于nn.Module类）的列表作为输入，然后也可以类似List那样进行append和extend操作
- nn.ModuleList 并没有定义一个网络，它只是将不同的模块储存在一起。ModuleList中元素的先后顺序并不代表其在网络中的真实位置顺序
net = nn.ModuleList([nn.Linear(784, 256), nn.ReLU()])
net.append(nn.Linear(256, 10)) # # 类似List的append操作
print(net[-1]) # 类似List的索引访问
print(net)
Linear(in_features=256, out_features=10, bias=True)
ModuleList(
(0): Linear(in_features=784, out_features=256, bias=True)
(1): ReLU()
(2): Linear(in_features=256, out_features=10, bias=True)
)
2.3、ModuleDict
- ModuleList 接收一个子模块（或层，需属于nn.Module类）的列表作为输入，然后也可以类似List那样进行append和extend操作
- 增加子模块或层的同时权重也会自动添加到网络中来
net = nn.ModuleDict({
'linear': nn.Linear(784, 256),
'act': nn.ReLU(),
})
net['output'] = nn.Linear(256, 10) # 添加
print(net['linear']) # 访问
print(net.output)
print(net)
Linear(in_features=784, out_features=256, bias=True)
Linear(in_features=256, out_features=10, bias=True)
ModuleDict(
(linear): Linear(in_features=784, out_features=256, bias=True)
(act): ReLU()
(output): Linear(in_features=256, out_features=10, bias=True)
)
3、手搓Restnet50
3.1、Restnet50
resnet 在imageNet竞赛中分类任务第一名、目标检测第一名，获得COCO数据集中目标检测第一名，图像分割第一名。
3.2、手搓思路
resnet50讲解，网络的输入照片大小是224x224的经过conv1，conv2，conv3，conv4，conv5最后在平均池化，全连接层。由于中间有重复利用的模块，所以我们需要将它们写成一个类，用来重复调用即可
3.3、resetnet核心要点：
- 1、提出residual模块（残差）
- 2、使用Batch Normalization加速训练（均值为0，方差为1）
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3.4 模型结构解析（restnet50）
- 1、conv1,stride=2,kernel_size=7,out_chnnels=64
- 2、conv2_x
- 2.1、 max_pool:kernel_size=3, stride=2
- 2.2、 conv_01:stride=1,kernel_size=1,out_chnnels=64
- 2.3、 conv_02:stride=2,kernel_size=3,out_chnnels=64
- 2.4、 conv_03:stride=1,kernel_size=1,out_chnnels=256
- 2.5、 layers（conv_01+conv_02+conv_03）*3
- 3、conv3_x
- 3.1、conv_01:stride=1,kernel_size=1,out_chnnels=128
- 3.2、conv_02:stride=2,kernel_size=3,out_chnnels=128
- 3.3、conv_03:stride=1,kernel_size=1,out_chnnels=512
- 3.4、residual:stride=2,kernel_size=1,out_chnnels=512
- 3.5、layers（conv_01+conv_02+conv_03）*4
- 4、conv4_x
- 4.1、conv_01:stride=1,kernel_size=1,out_chnnels=256
- 4.2、conv_02:stride=2,kernel_size=3,out_chnnels=256
- 4.3、conv_03:stride=1,kernel_size=1,out_chnnels=1024
- 4.4、residual:stride=2,kernel_size=1,out_chnnels=1024
- 4.5、layers（conv_01+conv_02+conv_03）*6
- 5、conv5_x
- 5.1、conv_01:stride=1,kernel_size=1,out_chnnels=512
- 5.2、conv_02:stride=2,kernel_size=3,out_chnnels=512
- 5.3、conv_03:stride=1,kernel_size=1,out_chnnels=2048
- 5.4、residual:stride=2,kernel_size=1,out_chnnels=2048
- 5.5、layers（conv_01+conv_02+conv_03）*3
- 6、fc
- 6.1、AdaptiveAvgPool2d:output=（1,1）
- 6.2、flatten：(x, 1)
- 6.3、fc:linear(512 * 4，num_class)
import torch.nn as nn
import torch
class Block(nn.Module):
def __init__(self, in_channels, out_channels, stride=1, downsample=False):
super(Block, self).__init__()
out_channel_01, out_channel_02, out_channel_03 = out_channels
self.downsample = downsample
self.relu = nn.ReLU(inplace=True)
self.conv1 = nn.Sequential(
nn.Conv2d(in_channels, out_channel_01, kernel_size=1, stride=1,bias=False),
nn.BatchNorm2d(out_channel_01),
nn.ReLU(inplace=True)
)
self.conv2 = nn.Sequential(
nn.Conv2d(out_channel_01, out_channel_02, kernel_size=3, stride=stride, padding=1, bias=False),
nn.BatchNorm2d(out_channel_02),
nn.ReLU(inplace=True)
)
self.conv3 = nn.Sequential(
nn.Conv2d(out_channel_02, out_channel_03, kernel_size=1, stride=1, bias=False),
nn.BatchNorm2d(out_channel_03),
)
if downsample:
self.shortcut = nn.Sequential(
nn.Conv2d(in_channels, out_channel_03, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(out_channel_03)
)
def forward(self,x):
x_shortcut = x
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
if self.downsample:
x_shortcut = self.shortcut(x_shortcut)
x = x + x_shortcut
x = self.relu(x)
return x
class Resnet50(nn.Module):
def __init__(self):
super(Resnet50,self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3),
nn.BatchNorm2d(64),
nn.ReLU(),
)
Layers = [3, 4, 6, 3]
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.conv2 = self._make_layer(64, (64, 64, 256), Layers[0],1)
self.conv3 = self._make_layer(256, (128, 128, 512), Layers[1], 2)
self.conv4 = self._make_layer(512, (256, 256, 1024), Layers[2], 2)
self.conv5 = self._make_layer(1024, (512, 512, 2048), Layers[3], 2)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Sequential(
nn.Linear(2048, 1000)
)
def forward(self, input):
x = self.conv1(input)
x = self.maxpool(x)
x = self.conv2(x)
x = self.conv3(x)
x = self.conv4(x)
x = self.conv5(x)
x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.fc(x)
return x
def _make_layer(self, in_channels, out_channels, blocks, stride=1):
layers = []
block_1 = Block(in_channels, out_channels, stride=stride, downsample=True)
layers.append(block_1)
for i in range(1, blocks):
layers.append(Block(out_channels[2], out_channels, stride=1, downsample=False))
return nn.Sequential(*layers)
#打印网络结构
net = Resnet50()
x = torch.rand((10, 3, 224, 224))
for name,layer in net.named_children():
if name != "fc":
x = layer(x)
print(name, 'output shaoe:', x.shape)
else:
x = x.view(x.size(0), -1)
x = layer(x)
print(name, 'output shaoe:', x.shape)
conv1 output shaoe: torch.Size([10, 64, 112, 112])
maxpool output shaoe: torch.Size([10, 64, 56, 56])
conv2 output shaoe: torch.Size([10, 256, 56, 56])
conv3 output shaoe: torch.Size([10, 512, 28, 28])
conv4 output shaoe: torch.Size([10, 1024, 14, 14])
conv5 output shaoe: torch.Size([10, 2048, 7, 7])
avgpool output shaoe: torch.Size([10, 2048, 1, 1])
fc output shaoe: torch.Size([10, 1000])
#torchinfo 可视化网络结构
from torchinfo import summary
net = Resnet50()
summary(net,((10, 3, 224, 224)))
D:\Users\xulele\Anaconda3\lib\site-packages\torchinfo\torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
action_fn=lambda data: sys.getsizeof(data.storage()),
D:\Users\xulele\Anaconda3\lib\site-packages\torch\storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
return super().__sizeof__() + self.nbytes()
==========================================================================================
Layer (type:depth-idx) Output Shape Param #
==========================================================================================
Resnet50 [10, 1000] --
├─Sequential: 1-1 [10, 64, 112, 112] --
│ └─Conv2d: 2-1 [10, 64, 112, 112] 9,472
│ └─BatchNorm2d: 2-2 [10, 64, 112, 112] 128
│ └─ReLU: 2-3 [10, 64, 112, 112] --
├─MaxPool2d: 1-2 [10, 64, 56, 56] --
├─Sequential: 1-3 [10, 256, 56, 56] --
│ └─Block: 2-4 [10, 256, 56, 56] --
│ │ └─Sequential: 3-1 [10, 64, 56, 56] 4,224
│ │ └─Sequential: 3-2 [10, 64, 56, 56] 36,992
│ │ └─Sequential: 3-3 [10, 256, 56, 56] 16,896
│ │ └─Sequential: 3-4 [10, 256, 56, 56] 16,896
│ │ └─ReLU: 3-5 [10, 256, 56, 56] --
│ └─Block: 2-5 [10, 256, 56, 56] --
│ │ └─Sequential: 3-6 [10, 64, 56, 56] 16,512
│ │ └─Sequential: 3-7 [10, 64, 56, 56] 36,992
│ │ └─Sequential: 3-8 [10, 256, 56, 56] 16,896
│ │ └─ReLU: 3-9 [10, 256, 56, 56] --
│ └─Block: 2-6 [10, 256, 56, 56] --
│ │ └─Sequential: 3-10 [10, 64, 56, 56] 16,512
│ │ └─Sequential: 3-11 [10, 64, 56, 56] 36,992
│ │ └─Sequential: 3-12 [10, 256, 56, 56] 16,896
│ │ └─ReLU: 3-13 [10, 256, 56, 56] --
├─Sequential: 1-4 [10, 512, 28, 28] --
│ └─Block: 2-7 [10, 512, 28, 28] --
│ │ └─Sequential: 3-14 [10, 128, 56, 56] 33,024
│ │ └─Sequential: 3-15 [10, 128, 28, 28] 147,712
│ │ └─Sequential: 3-16 [10, 512, 28, 28] 66,560
│ │ └─Sequential: 3-17 [10, 512, 28, 28] 132,096
│ │ └─ReLU: 3-18 [10, 512, 28, 28] --
│ └─Block: 2-8 [10, 512, 28, 28] --
│ │ └─Sequential: 3-19 [10, 128, 28, 28] 65,792
│ │ └─Sequential: 3-20 [10, 128, 28, 28] 147,712
│ │ └─Sequential: 3-21 [10, 512, 28, 28] 66,560
│ │ └─ReLU: 3-22 [10, 512, 28, 28] --
│ └─Block: 2-9 [10, 512, 28, 28] --
│ │ └─Sequential: 3-23 [10, 128, 28, 28] 65,792
│ │ └─Sequential: 3-24 [10, 128, 28, 28] 147,712
│ │ └─Sequential: 3-25 [10, 512, 28, 28] 66,560
│ │ └─ReLU: 3-26 [10, 512, 28, 28] --
│ └─Block: 2-10 [10, 512, 28, 28] --
│ │ └─Sequential: 3-27 [10, 128, 28, 28] 65,792
│ │ └─Sequential: 3-28 [10, 128, 28, 28] 147,712
│ │ └─Sequential: 3-29 [10, 512, 28, 28] 66,560
│ │ └─ReLU: 3-30 [10, 512, 28, 28] --
├─Sequential: 1-5 [10, 1024, 14, 14] --
│ └─Block: 2-11 [10, 1024, 14, 14] --
│ │ └─Sequential: 3-31 [10, 256, 28, 28] 131,584
│ │ └─Sequential: 3-32 [10, 256, 14, 14] 590,336
│ │ └─Sequential: 3-33 [10, 1024, 14, 14] 264,192
│ │ └─Sequential: 3-34 [10, 1024, 14, 14] 526,336
│ │ └─ReLU: 3-35 [10, 1024, 14, 14] --
│ └─Block: 2-12 [10, 1024, 14, 14] --
│ │ └─Sequential: 3-36 [10, 256, 14, 14] 262,656
│ │ └─Sequential: 3-37 [10, 256, 14, 14] 590,336
│ │ └─Sequential: 3-38 [10, 1024, 14, 14] 264,192
│ │ └─ReLU: 3-39 [10, 1024, 14, 14] --
│ └─Block: 2-13 [10, 1024, 14, 14] --
│ │ └─Sequential: 3-40 [10, 256, 14, 14] 262,656
│ │ └─Sequential: 3-41 [10, 256, 14, 14] 590,336
│ │ └─Sequential: 3-42 [10, 1024, 14, 14] 264,192
│ │ └─ReLU: 3-43 [10, 1024, 14, 14] --
│ └─Block: 2-14 [10, 1024, 14, 14] --
│ │ └─Sequential: 3-44 [10, 256, 14, 14] 262,656
│ │ └─Sequential: 3-45 [10, 256, 14, 14] 590,336
│ │ └─Sequential: 3-46 [10, 1024, 14, 14] 264,192
│ │ └─ReLU: 3-47 [10, 1024, 14, 14] --
│ └─Block: 2-15 [10, 1024, 14, 14] --
│ │ └─Sequential: 3-48 [10, 256, 14, 14] 262,656
│ │ └─Sequential: 3-49 [10, 256, 14, 14] 590,336
│ │ └─Sequential: 3-50 [10, 1024, 14, 14] 264,192
│ │ └─ReLU: 3-51 [10, 1024, 14, 14] --
│ └─Block: 2-16 [10, 1024, 14, 14] --
│ │ └─Sequential: 3-52 [10, 256, 14, 14] 262,656
│ │ └─Sequential: 3-53 [10, 256, 14, 14] 590,336
│ │ └─Sequential: 3-54 [10, 1024, 14, 14] 264,192
│ │ └─ReLU: 3-55 [10, 1024, 14, 14] --
├─Sequential: 1-6 [10, 2048, 7, 7] --
│ └─Block: 2-17 [10, 2048, 7, 7] --
│ │ └─Sequential: 3-56 [10, 512, 14, 14] 525,312
│ │ └─Sequential: 3-57 [10, 512, 7, 7] 2,360,320
│ │ └─Sequential: 3-58 [10, 2048, 7, 7] 1,052,672
│ │ └─Sequential: 3-59 [10, 2048, 7, 7] 2,101,248
│ │ └─ReLU: 3-60 [10, 2048, 7, 7] --
│ └─Block: 2-18 [10, 2048, 7, 7] --
│ │ └─Sequential: 3-61 [10, 512, 7, 7] 1,049,600
│ │ └─Sequential: 3-62 [10, 512, 7, 7] 2,360,320
│ │ └─Sequential: 3-63 [10, 2048, 7, 7] 1,052,672
│ │ └─ReLU: 3-64 [10, 2048, 7, 7] --
│ └─Block: 2-19 [10, 2048, 7, 7] --
│ │ └─Sequential: 3-65 [10, 512, 7, 7] 1,049,600
│ │ └─Sequential: 3-66 [10, 512, 7, 7] 2,360,320
│ │ └─Sequential: 3-67 [10, 2048, 7, 7] 1,052,672
│ │ └─ReLU: 3-68 [10, 2048, 7, 7] --
├─AdaptiveAvgPool2d: 1-7 [10, 2048, 1, 1] --
├─Sequential: 1-8 [10, 1000] --
│ └─Linear: 2-20 [10, 1000] 2,049,000
==========================================================================================
Total params: 25,557,096
Trainable params: 25,557,096
Non-trainable params: 0
Total mult-adds (G): 40.90
==========================================================================================
Input size (MB): 6.02
Forward/backward pass size (MB): 1778.32
Params size (MB): 102.23
Estimated Total Size (MB): 1886.57
==========================================================================================
from torch.utils.data import Dataset, DataLoader
from torchvision.transforms import transforms
import torchvision
import os
import numpy as np
import torch
#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
# 方案一：指定GPU的方式
# os.environ['CUDA_VISIBLE_DEVICES'] = '0,1' # 指明调用的GPU为0,1号
# 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:0" if torch.cuda.is_available() else "cpu") # 指明调用的GPU为1号
# 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)
# from tensorboard import SummaryWriter
from torch.utils.tensorboard import SummaryWriter
writer = SummaryWriter('./runs')
#训练&验证
writer = SummaryWriter('./runs')
# Set fixed random number seed
torch.manual_seed(42)
# 定义损失函数和优化器
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
My_model = Resnet50()
My_model = My_model.to(device)
# 交叉熵
criterion = torch.nn.CrossEntropyLoss()
# 优化器
optimizer = torch.optim.Adam(My_model.parameters(), lr=lr)
epoch = max_epochs
total_step = len(train_loader)
train_all_loss = []
test_all_loss = []
for i in range(epoch):
My_model.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)
# Write the network graph at epoch 0, batch 0
if epoch == 0 and iter == 0:
writer.add_graph(My_model, input_to_model=(images,labels)[0], verbose=True)
# Write an image at every batch 0
if iter == 0:
writer.add_image("Example input", images[0], global_step=epoch)
outputs = My_model(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 statistics
writer.add_scalar("Loss/Minibatches", train_total_loss, train_total_num)
print("Epoch [{}/{}], Iter [{}/{}], train_loss:{:4f}".format(i+1,epoch,iter+1,total_step,loss.item()/labels.shape[0]))
# Write loss for epoch
writer.add_scalar("Loss/Epochs", train_total_loss, epoch)
My_model.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 = My_model(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:0.430043
Epoch [1/2], Iter [2/3125], train_loss:0.399217
Epoch [1/2], Iter [3/3125], train_loss:0.391730
Epoch [1/2], Iter [4/3125], train_loss:0.381970
Epoch [1/2], Iter [5/3125], train_loss:0.337084
Epoch [1/2], Iter [6/3125], train_loss:0.322986
Epoch [1/2], Iter [7/3125], train_loss:0.328911
Epoch [1/2], Iter [8/3125], train_loss:0.287385
Epoch [1/2], Iter [9/3125], train_loss:0.289794
Epoch [1/2], Iter [10/3125], train_loss:0.247583
Epoch [1/2], Iter [11/3125], train_loss:0.239406
Epoch [1/2], Iter [12/3125], train_loss:0.252444
Epoch [1/2], Iter [13/3125], train_loss:0.204779
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Epoch [1/2], Iter [1266/3125], train_loss:0.110129
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Epoch [1/2], Iter [1270/3125], train_loss:0.132897
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Epoch [1/2], Iter [3102/3125], train_loss:0.077105
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Epoch [1/2], Iter [3111/3125], train_loss:0.087032
Epoch [1/2], Iter [3112/3125], train_loss:0.088426
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Epoch [1/2], Iter [3117/3125], train_loss:0.091098
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Epoch [1/2], Iter [3120/3125], train_loss:0.105961
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Epoch [2/2], Iter [5/3125], train_loss:0.089171
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Epoch [2/2], Iter [25/3125], train_loss:0.075299
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Epoch [2/2], Iter [61/3125], train_loss:0.102972
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Epoch [2/2], Iter [76/3125], train_loss:0.093906
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Epoch [2/2], Iter [81/3125], train_loss:0.098161
Epoch [2/2], Iter [82/3125], train_loss:0.110207
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Epoch [2/2], Iter [94/3125], train_loss:0.075366
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Epoch [2/2], Iter [97/3125], train_loss:0.086412
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Epoch [2/2], Iter [99/3125], train_loss:0.110689
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Epoch [2/2], Iter [101/3125], train_loss:0.087105
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Epoch [2/2], Iter [865/3125], train_loss:0.114276
Epoch [2/2], Iter [866/3125], train_loss:0.093187
Epoch [2/2], Iter [867/3125], train_loss:0.069209
Epoch [2/2], Iter [868/3125], train_loss:0.104793
Epoch [2/2], Iter [869/3125], train_loss:0.073808
Epoch [2/2], Iter [870/3125], train_loss:0.092707
Epoch [2/2], Iter [871/3125], train_loss:0.085808
Epoch [2/2], Iter [872/3125], train_loss:0.099127
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Epoch [2/2], Iter [874/3125], train_loss:0.081842
Epoch [2/2], Iter [875/3125], train_loss:0.083346
Epoch [2/2], Iter [876/3125], train_loss:0.109375
Epoch [2/2], Iter [877/3125], train_loss:0.069028
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Epoch [2/2], Iter [879/3125], train_loss:0.082911
Epoch [2/2], Iter [880/3125], train_loss:0.064488
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Epoch [2/2], Iter [887/3125], train_loss:0.067995
Epoch [2/2], Iter [888/3125], train_loss:0.085687
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Epoch [2/2], Iter [890/3125], train_loss:0.071787
Epoch [2/2], Iter [891/3125], train_loss:0.081613
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Epoch [2/2], Iter [894/3125], train_loss:0.087902
Epoch [2/2], Iter [895/3125], train_loss:0.130290
Epoch [2/2], Iter [896/3125], train_loss:0.075751
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Epoch [2/2], Iter [898/3125], train_loss:0.088319
Epoch [2/2], Iter [899/3125], train_loss:0.107320
Epoch [2/2], Iter [900/3125], train_loss:0.069297
Epoch [2/2], Iter [901/3125], train_loss:0.059855
Epoch [2/2], Iter [902/3125], train_loss:0.090469
Epoch [2/2], Iter [903/3125], train_loss:0.083430
Epoch [2/2], Iter [904/3125], train_loss:0.060752
Epoch [2/2], Iter [905/3125], train_loss:0.088156
Epoch [2/2], Iter [906/3125], train_loss:0.089071
Epoch [2/2], Iter [907/3125], train_loss:0.084885
Epoch [2/2], Iter [908/3125], train_loss:0.048224
Epoch [2/2], Iter [909/3125], train_loss:0.113041
Epoch [2/2], Iter [910/3125], train_loss:0.116053
Epoch [2/2], Iter [911/3125], train_loss:0.074417
Epoch [2/2], Iter [912/3125], train_loss:0.091008
Epoch [2/2], Iter [913/3125], train_loss:0.092575
Epoch [2/2], Iter [914/3125], train_loss:0.113760
Epoch [2/2], Iter [915/3125], train_loss:0.120776
Epoch [2/2], Iter [916/3125], train_loss:0.139293
Epoch [2/2], Iter [917/3125], train_loss:0.069343
Epoch [2/2], Iter [918/3125], train_loss:0.098188
Epoch [2/2], Iter [919/3125], train_loss:0.061732
Epoch [2/2], Iter [920/3125], train_loss:0.138873
Epoch [2/2], Iter [921/3125], train_loss:0.108592
Epoch [2/2], Iter [922/3125], train_loss:0.108380
Epoch [2/2], Iter [923/3125], train_loss:0.089235
Epoch [2/2], Iter [924/3125], train_loss:0.098835
Epoch [2/2], Iter [925/3125], train_loss:0.084797
Epoch [2/2], Iter [926/3125], train_loss:0.086078
Epoch [2/2], Iter [927/3125], train_loss:0.096045
Epoch [2/2], Iter [928/3125], train_loss:0.103381
Epoch [2/2], Iter [929/3125], train_loss:0.064686
Epoch [2/2], Iter [930/3125], train_loss:0.101205
Epoch [2/2], Iter [931/3125], train_loss:0.083386
Epoch [2/2], Iter [932/3125], train_loss:0.124332
Epoch [2/2], Iter [933/3125], train_loss:0.071771
Epoch [2/2], Iter [934/3125], train_loss:0.068327
Epoch [2/2], Iter [935/3125], train_loss:0.069932
Epoch [2/2], Iter [936/3125], train_loss:0.088089
Epoch [2/2], Iter [937/3125], train_loss:0.088597
Epoch [2/2], Iter [938/3125], train_loss:0.104114
Epoch [2/2], Iter [939/3125], train_loss:0.083072
Epoch [2/2], Iter [940/3125], train_loss:0.101029
Epoch [2/2], Iter [941/3125], train_loss:0.108483
Epoch [2/2], Iter [942/3125], train_loss:0.100051
Epoch [2/2], Iter [943/3125], train_loss:0.106296
Epoch [2/2], Iter [944/3125], train_loss:0.072279
Epoch [2/2], Iter [945/3125], train_loss:0.143448
Epoch [2/2], Iter [946/3125], train_loss:0.084587
Epoch [2/2], Iter [947/3125], train_loss:0.073256
Epoch [2/2], Iter [948/3125], train_loss:0.083115
Epoch [2/2], Iter [949/3125], train_loss:0.076965
Epoch [2/2], Iter [950/3125], train_loss:0.083379
Epoch [2/2], Iter [951/3125], train_loss:0.078656
Epoch [2/2], Iter [952/3125], train_loss:0.080206
Epoch [2/2], Iter [953/3125], train_loss:0.088033
Epoch [2/2], Iter [954/3125], train_loss:0.094281
Epoch [2/2], Iter [955/3125], train_loss:0.109771
Epoch [2/2], Iter [956/3125], train_loss:0.098340
Epoch [2/2], Iter [957/3125], train_loss:0.103174
Epoch [2/2], Iter [958/3125], train_loss:0.070675
Epoch [2/2], Iter [959/3125], train_loss:0.092117
Epoch [2/2], Iter [960/3125], train_loss:0.093642
Epoch [2/2], Iter [961/3125], train_loss:0.128867
Epoch [2/2], Iter [962/3125], train_loss:0.072056
Epoch [2/2], Iter [963/3125], train_loss:0.094215
Epoch [2/2], Iter [964/3125], train_loss:0.091706
Epoch [2/2], Iter [965/3125], train_loss:0.076420
Epoch [2/2], Iter [966/3125], train_loss:0.110798
Epoch [2/2], Iter [967/3125], train_loss:0.066716
Epoch [2/2], Iter [968/3125], train_loss:0.104807
Epoch [2/2], Iter [969/3125], train_loss:0.086580
Epoch [2/2], Iter [970/3125], train_loss:0.105679
Epoch [2/2], Iter [971/3125], train_loss:0.084984
Epoch [2/2], Iter [972/3125], train_loss:0.093323
Epoch [2/2], Iter [973/3125], train_loss:0.088777
Epoch [2/2], Iter [974/3125], train_loss:0.090154
Epoch [2/2], Iter [975/3125], train_loss:0.096426
Epoch [2/2], Iter [976/3125], train_loss:0.107699
Epoch [2/2], Iter [977/3125], train_loss:0.110699
Epoch [2/2], Iter [978/3125], train_loss:0.072643
Epoch [2/2], Iter [979/3125], train_loss:0.078052
Epoch [2/2], Iter [980/3125], train_loss:0.090422
Epoch [2/2], Iter [981/3125], train_loss:0.071456
Epoch [2/2], Iter [982/3125], train_loss:0.095594
Epoch [2/2], Iter [983/3125], train_loss:0.092027
Epoch [2/2], Iter [984/3125], train_loss:0.116863
Epoch [2/2], Iter [985/3125], train_loss:0.114535
Epoch [2/2], Iter [986/3125], train_loss:0.079183
Epoch [2/2], Iter [987/3125], train_loss:0.090277
Epoch [2/2], Iter [988/3125], train_loss:0.124222
Epoch [2/2], Iter [989/3125], train_loss:0.115095
Epoch [2/2], Iter [990/3125], train_loss:0.114542
Epoch [2/2], Iter [991/3125], train_loss:0.106006
Epoch [2/2], Iter [992/3125], train_loss:0.095041
Epoch [2/2], Iter [993/3125], train_loss:0.076730
Epoch [2/2], Iter [994/3125], train_loss:0.109610
Epoch [2/2], Iter [995/3125], train_loss:0.107274
Epoch [2/2], Iter [996/3125], train_loss:0.066058
Epoch [2/2], Iter [997/3125], train_loss:0.065898
Epoch [2/2], Iter [998/3125], train_loss:0.117909
Epoch [2/2], Iter [999/3125], train_loss:0.069444
Epoch [2/2], Iter [1000/3125], train_loss:0.107684
Epoch [2/2], Iter [1001/3125], train_loss:0.094535
Epoch [2/2], Iter [1002/3125], train_loss:0.098872
Epoch [2/2], Iter [1003/3125], train_loss:0.097507
Epoch [2/2], Iter [1004/3125], train_loss:0.091864
Epoch [2/2], Iter [1005/3125], train_loss:0.078213
Epoch [2/2], Iter [1006/3125], train_loss:0.099576
Epoch [2/2], Iter [1007/3125], train_loss:0.100277
Epoch [2/2], Iter [1008/3125], train_loss:0.124750
Epoch [2/2], Iter [1009/3125], train_loss:0.104891
Epoch [2/2], Iter [1010/3125], train_loss:0.079731
Epoch [2/2], Iter [1011/3125], train_loss:0.085950
Epoch [2/2], Iter [1012/3125], train_loss:0.084804
Epoch [2/2], Iter [1013/3125], train_loss:0.075454
Epoch [2/2], Iter [1014/3125], train_loss:0.130603
Epoch [2/2], Iter [1015/3125], train_loss:0.096016
Epoch [2/2], Iter [1016/3125], train_loss:0.090073
Epoch [2/2], Iter [1017/3125], train_loss:0.074195
Epoch [2/2], Iter [1018/3125], train_loss:0.122536
Epoch [2/2], Iter [1019/3125], train_loss:0.112131
Epoch [2/2], Iter [1020/3125], train_loss:0.109132
Epoch [2/2], Iter [1021/3125], train_loss:0.115335
Epoch [2/2], Iter [1022/3125], train_loss:0.140687
Epoch [2/2], Iter [1023/3125], train_loss:0.083916
Epoch [2/2], Iter [1024/3125], train_loss:0.095654
Epoch [2/2], Iter [1025/3125], train_loss:0.084160
Epoch [2/2], Iter [1026/3125], train_loss:0.114870
Epoch [2/2], Iter [1027/3125], train_loss:0.101187
Epoch [2/2], Iter [1028/3125], train_loss:0.082069
Epoch [2/2], Iter [1029/3125], train_loss:0.072046
Epoch [2/2], Iter [1030/3125], train_loss:0.086769
Epoch [2/2], Iter [1031/3125], train_loss:0.089113
Epoch [2/2], Iter [1032/3125], train_loss:0.061093
Epoch [2/2], Iter [1033/3125], train_loss:0.090316
Epoch [2/2], Iter [1034/3125], train_loss:0.085117
Epoch [2/2], Iter [1035/3125], train_loss:0.104584
Epoch [2/2], Iter [1036/3125], train_loss:0.081303
Epoch [2/2], Iter [1037/3125], train_loss:0.091452
Epoch [2/2], Iter [1038/3125], train_loss:0.112761
Epoch [2/2], Iter [1039/3125], train_loss:0.088501
Epoch [2/2], Iter [1040/3125], train_loss:0.084058
Epoch [2/2], Iter [1041/3125], train_loss:0.078801
Epoch [2/2], Iter [1042/3125], train_loss:0.087638
Epoch [2/2], Iter [1043/3125], train_loss:0.106893
Epoch [2/2], Iter [1044/3125], train_loss:0.087472
Epoch [2/2], Iter [1045/3125], train_loss:0.130255
Epoch [2/2], Iter [1046/3125], train_loss:0.097685
Epoch [2/2], Iter [1047/3125], train_loss:0.095756
Epoch [2/2], Iter [1048/3125], train_loss:0.115433
Epoch [2/2], Iter [1049/3125], train_loss:0.079820
Epoch [2/2], Iter [1050/3125], train_loss:0.116015
Epoch [2/2], Iter [1051/3125], train_loss:0.146984
Epoch [2/2], Iter [1052/3125], train_loss:0.129607
Epoch [2/2], Iter [1053/3125], train_loss:0.098001
Epoch [2/2], Iter [1054/3125], train_loss:0.076012
Epoch [2/2], Iter [1055/3125], train_loss:0.098679
Epoch [2/2], Iter [1056/3125], train_loss:0.079336
Epoch [2/2], Iter [1057/3125], train_loss:0.127889
Epoch [2/2], Iter [1058/3125], train_loss:0.093738
Epoch [2/2], Iter [1059/3125], train_loss:0.096781
Epoch [2/2], Iter [1060/3125], train_loss:0.079172
Epoch [2/2], Iter [1061/3125], train_loss:0.074400
Epoch [2/2], Iter [1062/3125], train_loss:0.094194
Epoch [2/2], Iter [1063/3125], train_loss:0.085245
Epoch [2/2], Iter [1064/3125], train_loss:0.094455
Epoch [2/2], Iter [1065/3125], train_loss:0.081712
Epoch [2/2], Iter [1066/3125], train_loss:0.096517
Epoch [2/2], Iter [1067/3125], train_loss:0.140057
Epoch [2/2], Iter [1068/3125], train_loss:0.087830
Epoch [2/2], Iter [1069/3125], train_loss:0.083283
Epoch [2/2], Iter [1070/3125], train_loss:0.081132
Epoch [2/2], Iter [1071/3125], train_loss:0.083004
Epoch [2/2], Iter [1072/3125]