深度学习中常用的库总结
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文章目录
一、time库，显示程序运行时间
import time
# 在开头设置开始时间
start = time.perf_counter() # start = time.clock() python3.8之前可以
# 在程序运行结束的位置添加结束时间
end = time.perf_counter() # end = time.clock() python3.8之前可以
# 再将其进行打印，即可显示出程序完成的运行耗时
print(f'运行耗时{(end-start):.6f}s')
cpu测试vgg16的时间
import torch
from torchvision.models import vgg16
import time
# 在开头设置开始时间
start = time.perf_counter() # start = time.clock() python3.8之前可以
myNet = vgg16() # 实例化网络模型
img = torch.randn(32, 3, 256, 256)
pred = myNet(img)
print(pred.shape)
# 在程序运行结束的位置添加结束时间
end = time.perf_counter() # end = time.clock() python3.8之前可以
# 再将其进行打印，即可显示出程序完成的运行耗时
print(f'运行耗时{(end-start):.6f}s')
运行结果
二、可视化模型参数：
1. 使用自定义函数计算模型参数
import torch
import torchvision.models as models
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
# 加载 ResNet-18 模型
model = models.resnet18()
# 是否使用GPU计算
device = torch.device("cuda" if torch.cuda.is_available() else 'cpu')
# --------------------------使用自定义函数count_parameters()计算模型参数--------------------------
print('ResNet-18 Model Parameters: ', count_parameters(model)/ 1e6, 'Million')
2. torchsummary.summary()：可视化模型网络结构，计算模型参数
summary(model, input_size, batch_size=-1, device=“cuda”)
- model：网络模型
- input_size:输入的图像尺寸（c, h, w）（必须是元组，或者列表）（不能是ndarray或tensor）
- batch_size：输入数据的批量大小（想要调试的输入形状为[n, c, h, w]，必须改成input_size=(c, h, w), batch_size=n，否则提示输入尺寸错误）
- device：默认将“输入数据”放在GPU上, 此时模型也得放在GPU上(手动); 或者模型不移动，指定device=‘cpu’。
# 方式1：使用CPU计算网络模型结构
import torch
import torchvision.models as models
from torchsummary import summary
# 加载 ResNet-18 模型
model = models.resnet18()
# 是否使用GPU计算
device = torch.device("cuda" if torch.cuda.is_available() else 'cpu')
# --------------------------使用torchsummary.summary()现实模型结构、计算模型参数--------------------------
summary(model, (3, 224, 224), 1, device='cpu') # 输出模型网络结构 (默认，将数据放在GPU上，模型也要放在GPU上否则会出错)
# 方式2：使用GPU计算网络模型结构
import torch
import torchvision.models as models
from torchsummary import summary
# 加载 ResNet-18 模型
model = models.resnet18()
# --------------------------使用torchsummary.summary()现实模型结构、计算模型参数--------------------------
summary(model.to(device), (3, 224, 224), 1) # 输出模型网络结构
运行结果
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [1, 64, 112, 112] 9,408
BatchNorm2d-2 [1, 64, 112, 112] 128
ReLU-3 [1, 64, 112, 112] 0
MaxPool2d-4 [1, 64, 56, 56] 0
Conv2d-5 [1, 64, 56, 56] 36,864
BatchNorm2d-6 [1, 64, 56, 56] 128
ReLU-7 [1, 64, 56, 56] 0
Conv2d-8 [1, 64, 56, 56] 36,864
BatchNorm2d-9 [1, 64, 56, 56] 128
ReLU-10 [1, 64, 56, 56] 0
BasicBlock-11 [1, 64, 56, 56] 0
Conv2d-12 [1, 64, 56, 56] 36,864
BatchNorm2d-13 [1, 64, 56, 56] 128
ReLU-14 [1, 64, 56, 56] 0
Conv2d-15 [1, 64, 56, 56] 36,864
BatchNorm2d-16 [1, 64, 56, 56] 128
ReLU-17 [1, 64, 56, 56] 0
BasicBlock-18 [1, 64, 56, 56] 0
Conv2d-19 [1, 128, 28, 28] 73,728
BatchNorm2d-20 [1, 128, 28, 28] 256
ReLU-21 [1, 128, 28, 28] 0
Conv2d-22 [1, 128, 28, 28] 147,456
BatchNorm2d-23 [1, 128, 28, 28] 256
Conv2d-24 [1, 128, 28, 28] 8,192
BatchNorm2d-25 [1, 128, 28, 28] 256
ReLU-26 [1, 128, 28, 28] 0
BasicBlock-27 [1, 128, 28, 28] 0
Conv2d-28 [1, 128, 28, 28] 147,456
BatchNorm2d-29 [1, 128, 28, 28] 256
ReLU-30 [1, 128, 28, 28] 0
Conv2d-31 [1, 128, 28, 28] 147,456
BatchNorm2d-32 [1, 128, 28, 28] 256
ReLU-33 [1, 128, 28, 28] 0
BasicBlock-34 [1, 128, 28, 28] 0
Conv2d-35 [1, 256, 14, 14] 294,912
BatchNorm2d-36 [1, 256, 14, 14] 512
ReLU-37 [1, 256, 14, 14] 0
Conv2d-38 [1, 256, 14, 14] 589,824
BatchNorm2d-39 [1, 256, 14, 14] 512
Conv2d-40 [1, 256, 14, 14] 32,768
BatchNorm2d-41 [1, 256, 14, 14] 512
ReLU-42 [1, 256, 14, 14] 0
BasicBlock-43 [1, 256, 14, 14] 0
Conv2d-44 [1, 256, 14, 14] 589,824
BatchNorm2d-45 [1, 256, 14, 14] 512
ReLU-46 [1, 256, 14, 14] 0
Conv2d-47 [1, 256, 14, 14] 589,824
BatchNorm2d-48 [1, 256, 14, 14] 512
ReLU-49 [1, 256, 14, 14] 0
BasicBlock-50 [1, 256, 14, 14] 0
Conv2d-51 [1, 512, 7, 7] 1,179,648
BatchNorm2d-52 [1, 512, 7, 7] 1,024
ReLU-53 [1, 512, 7, 7] 0
Conv2d-54 [1, 512, 7, 7] 2,359,296
BatchNorm2d-55 [1, 512, 7, 7] 1,024
Conv2d-56 [1, 512, 7, 7] 131,072
BatchNorm2d-57 [1, 512, 7, 7] 1,024
ReLU-58 [1, 512, 7, 7] 0
BasicBlock-59 [1, 512, 7, 7] 0
Conv2d-60 [1, 512, 7, 7] 2,359,296
BatchNorm2d-61 [1, 512, 7, 7] 1,024
ReLU-62 [1, 512, 7, 7] 0
Conv2d-63 [1, 512, 7, 7] 2,359,296
BatchNorm2d-64 [1, 512, 7, 7] 1,024
ReLU-65 [1, 512, 7, 7] 0
BasicBlock-66 [1, 512, 7, 7] 0
AdaptiveAvgPool2d-67 [1, 512, 1, 1] 0
Linear-68 [1, 1000] 513,000
================================================================
Total params: 11,689,512
Trainable params: 11,689,512
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 62.79
Params size (MB): 44.59
Estimated Total Size (MB): 107.96
----------------------------------------------------------------
3. torchstat.stat()计算模型参数量、内存开销、FLOPs
( torchstat.stat()：
输入：
- 限制输入维度必须是三维)
输出：
- Total params：网络的整体参数量；
- Total memory：模型进行推理时候所需的内存；
- Total Flops：网络完成的浮点运算；
- Total MAdd：网络完成的乘加操作的数量。一次乘加=一次乘法+一次加法，可以大致认为（Flops ≈2*MAdd）；
- MemR+W：MemR+W = MemRead + MemWrite；
（MemRead：网络运行时，从内存中读取的大小）
（MemWrite：网络运行时，写入到内存中的大小）
import torch
import torchvision.models as models
from torchstat import stat
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
# 加载 ResNet-18 模型
model = models.resnet18()
# 是否使用GPU计算
device = torch.device("cuda" if torch.cuda.is_available() else 'cpu')
# --------------------------使用torchstat.stat()计算模型参数量、内存开销、FLOPS(限制输入维度必须是三维)--------------------------
stat(model, (3, 224, 224))
[MAdd]: AdaptiveAvgPool2d is not supported!
[Flops]: AdaptiveAvgPool2d is not supported!
[Memory]: AdaptiveAvgPool2d is not supported!
[MAdd]: AdaptiveAvgPool2d is not supported!
[Flops]: AdaptiveAvgPool2d is not supported!
[Memory]: AdaptiveAvgPool2d is not supported!
[MAdd]: AdaptiveAvgPool2d is not supported!
[Flops]: AdaptiveAvgPool2d is not supported!
[Memory]: AdaptiveAvgPool2d is not supported!
[MAdd]: AdaptiveAvgPool2d is not supported!
[Flops]: AdaptiveAvgPool2d is not supported!
[Memory]: AdaptiveAvgPool2d is not supported!
module name input shape output shape params memory(MB) MAdd Flops MemRead(B) MemWrite(B) duration[%] MemR+W(B)
0 conv1 3 224 224 64 112 112 9408.0 3.06 235,225,088.0 118,013,952.0 639744.0 3211264.0 0.00% 3851008.0
1 bn1 64 112 112 64 112 112 128.0 3.06 3,211,264.0 1,605,632.0 3211776.0 3211264.0 0.00% 6423040.0
2 relu 64 112 112 64 112 112 0.0 3.06 802,816.0 802,816.0 3211264.0 3211264.0 8.38% 6422528.0
3 maxpool 64 112 112 64 56 56 0.0 0.77 1,605,632.0 802,816.0 3211264.0 802816.0 0.00% 4014080.0
4 layer1.0.conv1 64 56 56 64 56 56 36864.0 0.77 231,010,304.0 115,605,504.0 950272.0 802816.0 8.38% 1753088.0
5 layer1.0.bn1 64 56 56 64 56 56 128.0 0.77 802,816.0 401,408.0 803328.0 802816.0 0.00% 1606144.0
6 layer1.0.relu 64 56 56 64 56 56 0.0 0.77 200,704.0 200,704.0 802816.0 802816.0 0.00% 1605632.0
7 layer1.0.conv2 64 56 56 64 56 56 36864.0 0.77 231,010,304.0 115,605,504.0 950272.0 802816.0 0.00% 1753088.0
8 layer1.0.bn2 64 56 56 64 56 56 128.0 0.77 802,816.0 401,408.0 803328.0 802816.0 0.00% 1606144.0
9 layer1.1.conv1 64 56 56 64 56 56 36864.0 0.77 231,010,304.0 115,605,504.0 950272.0 802816.0 0.00% 1753088.0
10 layer1.1.bn1 64 56 56 64 56 56 128.0 0.77 802,816.0 401,408.0 803328.0 802816.0 0.00% 1606144.0
11 layer1.1.relu 64 56 56 64 56 56 0.0 0.77 200,704.0 200,704.0 802816.0 802816.0 0.00% 1605632.0
12 layer1.1.conv2 64 56 56 64 56 56 36864.0 0.77 231,010,304.0 115,605,504.0 950272.0 802816.0 4.26% 1753088.0
13 layer1.1.bn2 64 56 56 64 56 56 128.0 0.77 802,816.0 401,408.0 803328.0 802816.0 0.59% 1606144.0
14 layer2.0.conv1 64 56 56 128 28 28 73728.0 0.38 115,505,152.0 57,802,752.0 1097728.0 401408.0 2.97% 1499136.0
15 layer2.0.bn1 128 28 28 128 28 28 256.0 0.38 401,408.0 200,704.0 402432.0 401408.0 0.00% 803840.0
16 layer2.0.relu 128 28 28 128 28 28 0.0 0.38 100,352.0 100,352.0 401408.0 401408.0 0.00% 802816.0
17 layer2.0.conv2 128 28 28 128 28 28 147456.0 0.38 231,110,656.0 115,605,504.0 991232.0 401408.0 8.44% 1392640.0
18 layer2.0.bn2 128 28 28 128 28 28 256.0 0.38 401,408.0 200,704.0 402432.0 401408.0 0.00% 803840.0
19 layer2.0.downsample.0 64 56 56 128 28 28 8192.0 0.38 12,744,704.0 6,422,528.0 835584.0 401408.0 0.00% 1236992.0
20 layer2.0.downsample.1 128 28 28 128 28 28 256.0 0.38 401,408.0 200,704.0 402432.0 401408.0 0.00% 803840.0
21 layer2.1.conv1 128 28 28 128 28 28 147456.0 0.38 231,110,656.0 115,605,504.0 991232.0 401408.0 8.37% 1392640.0
22 layer2.1.bn1 128 28 28 128 28 28 256.0 0.38 401,408.0 200,704.0 402432.0 401408.0 0.00% 803840.0
23 layer2.1.relu 128 28 28 128 28 28 0.0 0.38 100,352.0 100,352.0 401408.0 401408.0 0.00% 802816.0
24 layer2.1.conv2 128 28 28 128 28 28 147456.0 0.38 231,110,656.0 115,605,504.0 991232.0 401408.0 0.00% 1392640.0
25 layer2.1.bn2 128 28 28 128 28 28 256.0 0.38 401,408.0 200,704.0 402432.0 401408.0 0.00% 803840.0
26 layer3.0.conv1 128 28 28 256 14 14 294912.0 0.19 115,555,328.0 57,802,752.0 1581056.0 200704.0 8.37% 1781760.0
27 layer3.0.bn1 256 14 14 256 14 14 512.0 0.19 200,704.0 100,352.0 202752.0 200704.0 0.00% 403456.0
28 layer3.0.relu 256 14 14 256 14 14 0.0 0.19 50,176.0 50,176.0 200704.0 200704.0 0.00% 401408.0
29 layer3.0.conv2 256 14 14 256 14 14 589824.0 0.19 231,160,832.0 115,605,504.0 2560000.0 200704.0 0.00% 2760704.0
30 layer3.0.bn2 256 14 14 256 14 14 512.0 0.19 200,704.0 100,352.0 202752.0 200704.0 8.37% 403456.0
31 layer3.0.downsample.0 128 28 28 256 14 14 32768.0 0.19 12,794,880.0 6,422,528.0 532480.0 200704.0 0.00% 733184.0
32 layer3.0.downsample.1 256 14 14 256 14 14 512.0 0.19 200,704.0 100,352.0 202752.0 200704.0 0.00% 403456.0
33 layer3.1.conv1 256 14 14 256 14 14 589824.0 0.19 231,160,832.0 115,605,504.0 2560000.0 200704.0 0.00% 2760704.0
34 layer3.1.bn1 256 14 14 256 14 14 512.0 0.19 200,704.0 100,352.0 202752.0 200704.0 8.37% 403456.0
35 layer3.1.relu 256 14 14 256 14 14 0.0 0.19 50,176.0 50,176.0 200704.0 200704.0 0.00% 401408.0
36 layer3.1.conv2 256 14 14 256 14 14 589824.0 0.19 231,160,832.0 115,605,504.0 2560000.0 200704.0 0.00% 2760704.0
37 layer3.1.bn2 256 14 14 256 14 14 512.0 0.19 200,704.0 100,352.0 202752.0 200704.0 0.00% 403456.0
38 layer4.0.conv1 256 14 14 512 7 7 1179648.0 0.10 115,580,416.0 57,802,752.0 4919296.0 100352.0 8.38% 5019648.0
39 layer4.0.bn1 512 7 7 512 7 7 1024.0 0.10 100,352.0 50,176.0 104448.0 100352.0 0.00% 204800.0
40 layer4.0.relu 512 7 7 512 7 7 0.0 0.10 25,088.0 25,088.0 100352.0 100352.0 0.00% 200704.0
41 layer4.0.conv2 512 7 7 512 7 7 2359296.0 0.10 231,185,920.0 115,605,504.0 9537536.0 100352.0 8.37% 9637888.0
42 layer4.0.bn2 512 7 7 512 7 7 1024.0 0.10 100,352.0 50,176.0 104448.0 100352.0 0.00% 204800.0
43 layer4.0.downsample.0 256 14 14 512 7 7 131072.0 0.10 12,819,968.0 6,422,528.0 724992.0 100352.0 0.00% 825344.0
44 layer4.0.downsample.1 512 7 7 512 7 7 1024.0 0.10 100,352.0 50,176.0 104448.0 100352.0 0.00% 204800.0
45 layer4.1.conv1 512 7 7 512 7 7 2359296.0 0.10 231,185,920.0 115,605,504.0 9537536.0 100352.0 8.38% 9637888.0
46 layer4.1.bn1 512 7 7 512 7 7 1024.0 0.10 100,352.0 50,176.0 104448.0 100352.0 0.00% 204800.0
47 layer4.1.relu 512 7 7 512 7 7 0.0 0.10 25,088.0 25,088.0 100352.0 100352.0 8.37% 200704.0
48 layer4.1.conv2 512 7 7 512 7 7 2359296.0 0.10 231,185,920.0 115,605,504.0 9537536.0 100352.0 0.00% 9637888.0
49 layer4.1.bn2 512 7 7 512 7 7 1024.0 0.10 100,352.0 50,176.0 104448.0 100352.0 0.00% 204800.0
50 avgpool 512 7 7 512 1 1 0.0 0.00 0.0 0.0 0.0 0.0 0.00% 0.0
51 fc 512 1000 513000.0 0.00 1,023,000.0 512,000.0 2054048.0 4000.0 0.00% 2058048.0
total 11689512.0 25.65 3,638,757,912.0 1,821,399,040.0 2054048.0 4000.0 100.00% 101756992.0
=================================================================================================================================================================
Total params: 11,689,512
-----------------------------------------------------------------------------------------------------------------------------------------------------------------
Total memory: 25.65MB
Total MAdd: 3.64GMAdd
Total Flops: 1.82GFlops
Total MemR+W: 97.04MB
4. thop.profile()计算模型的FLOPS、模型参数
import torch
import torchvision.models as models
from thop import profile
# 加载 ResNet-18 模型
model = models.resnet18()
# 准备示例输入
input_data = torch.randn(1, 3, 224, 224)
# --------------------------使用thop.profile()计算模型的FLOPS、模型参数--------------------------
flops, params = profile(model, inputs=(input_data,))
print(f"ResNet-18 FLOPs: {flops / 1e9} GFLOPs") # 将结果转换为GFLOPs
print(f"ResNet-18 Parameters: {params / 1e6} Million")
[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.
[INFO] Register count_normalization() for <class 'torch.nn.modules.batchnorm.BatchNorm2d'>.
[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.
[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.
[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.
[INFO] Register count_adap_avgpool() for <class 'torch.nn.modules.pooling.AdaptiveAvgPool2d'>.
[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.
[MAdd]: AdaptiveAvgPool2d is not supported!
[Flops]: AdaptiveAvgPool2d is not supported!
[Memory]: AdaptiveAvgPool2d is not supported!
[MAdd]: AdaptiveAvgPool2d is not supported!
[Flops]: AdaptiveAvgPool2d is not supported!
[Memory]: AdaptiveAvgPool2d is not supported!
[MAdd]: AdaptiveAvgPool2d is not supported!
[Flops]: AdaptiveAvgPool2d is not supported!
[Memory]: AdaptiveAvgPool2d is not supported!
[MAdd]: AdaptiveAvgPool2d is not supported!
[Flops]: AdaptiveAvgPool2d is not supported!
[Memory]: AdaptiveAvgPool2d is not supported!
ResNet-18 FLOPs: 1.824033792 GFLOPs
ResNet-18 Parameters: 11.689512 Million
三、collections.OrderedDict()函数：创建按照有序插入顺序存储的有序字典
方式一：使用collections.OrderedDict()函数
from collections import OrderedDict
test_results = OrderedDict() # 按照有序插入顺序存储 的有序字典
test_results['psnr'] = []
test_results['ssim'] = []
test_results['niqe'] = []
psnr, ssim, niqe = 0, 0, 0
test_results['psnr'].append(psnr)
test_results['ssim'] = ssim
print(test_results)
for key, value in test_results.items():
print(key, value)
方式二：直接创建集合
test_results = {'psnr': 0, 'ssim': 0, 'niqe': 0}
test_results['psnr'] += 1
print(test_results)
四、torchviz库，可视化模型计算图
import torch
import torchvision.models as models
# 是否使用GPU计算
device = torch.device("cuda" if torch.cuda.is_available() else 'cpu')
# 加载 ResNet-18 模型
model = models.resnet18()
# 准备示例输入
input_data = torch.randn(1, 3, 224, 224)
# 前向传播
pred = model(input_data)
# ---------------------可视化模型计算图---------------------
from torchviz import make_dot
# 构造图对象(3种方式)
# g = make_dot(y) # 只显示了输出节点的计算图
# g = make_dot(y, params=dict(model.named_parameters())) # 生成一个包含模型参数的完整计算图
pic_1 = make_dot(pred, params=dict(list(model.named_parameters()) + [('x', input_data)])) # 生成的计算图将显示模型参数以及额外的输入参数。
# 指定文件类型与文件路径
# pic_1.format = "png" # 定义pic为模型可视化后的输出,这里输出为png格式
# 指定文件生成的文件夹pic里面
# pic_1.directory = "pic"
# 生成文件(2种方式)
# pic_1.view() # 直接在当前路径下保存 pdf 并打开
pic_1.render(filename='pic/resnet18', view=False) # 在pic目录下，保存为 resnet18.pdf，参数view表示是否打开pdf
运行结果：
五、argparse库，用于命令项选项与参数解析
import argparse
parser = argparse.ArgumentParser(description='Train Super Resolution Models')
parser.add_argument('--crop_size', default=88, type=int, help='training images crop size')
parser.add_argument('--upscale_factor', default=4, type=int, choices=[2, 4, 8],
help='super resolution upscale factor')
parser.add_argument('--num_epochs', default=2, type=int, help='train epoch number')
opt = parser.parse_args()
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