python_developer_tools

python 开发过程中常用到的工具;包括网站开发,人工智能,文件，数据类型转换 支付接口对接，外挂，bat,sh等我在工作中常用到的开发工具

安装和使用

pip uninstall python_developer_tools
pip install git+https://github.com/carlsummer/python_developer_tools.git
from python_developer_tools import cv

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Contents

• cv 计算机视觉
  • 基础组成成分
    • Convolution Series
      • Depthwise Separable Convolution Usage
      • MBConv
      • Involution
    • [全连接FC]
      • SSM
  • [分类模型classnetwork]
    • AlexNet
    • DenseNet
    • Efficientnet
    • InceptionV1
    • InceptionV2
    • InceptionV3
    • repVGGNet
    • ResNet
    • ResNeXt
    • VGGNet
    • GhostNet
    • MixNet
    • MobileNetV1
    • MobileNetV2
    • MobileNetV3
    • MobileNetXt
    • ShuffleNet
    • ShuffleNetV2
    • SqueezeNet
    • Xception
• files
  • common

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Depthwise Separable Convolution Usage

Paper

"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"

Overview

Code

感谢代码来源External-Attention-pytorch

import torch
from python_developer_tools.cv.bases.conv.DepthwiseSeparableConvolution import DepthwiseSeparableConvolution
input=torch.randn(1,3,224,224)
dsconv=DepthwiseSeparableConvolution(3,64)
out=dsconv(input)

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MBConv

Paper

Rethinking model scaling for convolutional neural networks

Overview

Code

感谢代码来源External-Attention-pytorch

from python_developer_tools.cv.bases.conv.MBConv import MBConvBlock
import torch
input=torch.randn(1,3,112,112)
mbconv=MBConvBlock(ksize=3,input_filters=3,output_filters=3,image_size=112)
out=mbconv(input)
print(out.shape)

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Involution

Paper

Inverting the Inherence of Convolution for Visual Recognition

Overview

Code

感谢代码来源External-Attention-pytorch

from python_developer_tools.cv.bases.conv.Involution import Involution
import torch
input=torch.randn(1,4,64,64)
involution=Involution(kernel_size=3,in_channel=4,stride=2)
out=involution(input)
print(out.shape)

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SSM

Paper

Exploiting Featureswith Split-and-Share Module

Overview

code

from python_developer_tools.cv.bases.FC.SSM import SSM
import torch
x = torch.randn(2, 2048, 1, 1)
x = x.view(x.size(0), -1)
model = SSM()
out = model(x)
print(out.shape)

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AlexNet

Code

import torch
from python_developer_tools.cv.classes.AlexNet import AlexNet
model = AlexNet()
input = torch.randn(8,3,224,224)
out = model(input)
print(out.shape)

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DenseNet

Code

import torch
from python_developer_tools.cv.classes.DenseNet import DenseNet121
model = DenseNet121()
input = torch.randn(1, 3, 224, 224)
out = model(input)
print(out.shape)

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Efficientnet

Code

import torch
from python_developer_tools.cv.classes.Efficientnet import EfficientNet
model = EfficientNet('efficientnet_b0')
input = torch.randn(1, 3, 224, 224)
out = model(input)
print(out.shape)

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InceptionV1

Code

import torch
from python_developer_tools.cv.classes.InceptionV1 import InceptionV1
model = InceptionV1()
input = torch.randn(1, 3, 224, 224)
aux1, aux2, out = model(input)
print(aux1.shape)
print(aux2.shape)
print(out.shape)

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InceptionV2

Code

import torch
from python_developer_tools.cv.classes.InceptionV2 import InceptionV2
model = InceptionV2()
input = torch.randn(1, 3, 224, 224)
out = model(input)
print(out.shape)

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InceptionV3

Code

import torch
from python_developer_tools.cv.classes.InceptionV3 import InceptionV3
model = InceptionV3()
input = torch.randn(1, 3, 299, 299)
aux,out = model(input)
print(aux.shape)
print(out.shape)

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repVGGNet

Code

import torch
from python_developer_tools.cv.classes.repVGGNet import RepVGG_A1
model = RepVGG_A1()
input = torch.randn(1,3,224,224)
out = model(input)
print(out.shape)

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ResNet

Code

import torch
from python_developer_tools.cv.classes.ResNet import ResNet50
model = ResNet50()
input = torch.randn(1, 3, 224, 224)
out = model(input)
print(out.shape)

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ResNeXt

Code

import torch
from python_developer_tools.cv.classes.ResNeXt import ResNeXtBlock
model = ResNeXtBlock(in_places=256, places=128)
input = torch.randn(1,256,64,64)
out = model(input)
print(out.shape)

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VGGNet

Code

import torch
from python_developer_tools.cv.classes.VGGNet import VGG16
model = VGG16()
input = torch.randn(1,3,224,224)
out = model(input)
print(out.shape)

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GhostNet

Code

import torch
from python_developer_tools.cv.classes.GhostNet import GhostNet
model = GhostNet()
input = torch.randn(1, 3, 224, 224)
out = model(input)
print(out.shape)

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MixNet

Code

import torch
from python_developer_tools.cv.classes.MixNet import MixNet
model = MixNet(type ='mixnet_m')
input = torch.randn(1, 3, 224, 224)
out = model(input)
print(out.shape)

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MobileNetV1

Code

import torch
from python_developer_tools.cv.classes.MobileNetV1 import MobileNetV1
model = MobileNetV1()
input = torch.randn(1, 3, 224, 224)
out = model(input)
print(out.shape)

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MobileNetV2

Code

import torch
from python_developer_tools.cv.classes.MobileNetV2 import MobileNetV2
model = MobileNetV2()
input = torch.randn(1, 3, 224, 224)
out = model(input)
print(out.shape)

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MobileNetV3

Code

import torch
from python_developer_tools.cv.classes.MobileNetV3 import MobileNetV3
model = MobileNetV3(type='small')
input = torch.randn(1, 3, 224, 224)
out = model(input)
print(out.shape)

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MobileNetXt

Code

import torch
from python_developer_tools.cv.classes.MobileNetXt import MobileNetXt
model = MobileNetXt()
input = torch.randn(1, 3, 224, 224)
out = model(input)
print(out.shape)

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ShuffleNet

Code

import torch
from python_developer_tools.cv.classes.ShuffleNet import shufflenet_g1
model = shufflenet_g1()
input = torch.randn(1, 3, 224, 224)
out = model(input)
print(out.shape)

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ShuffleNetV2

Code

import torch
from python_developer_tools.cv.classes.ShuffleNetV2 import shufflenet_v2_x2_0
model = shufflenet_v2_x2_0()
input = torch.randn(1, 3, 224, 224)
out = model(input)
print(out.shape)

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SqueezeNet

Code

import torch
from python_developer_tools.cv.classes.SqueezeNet import SqueezeNet
model = SqueezeNet()
input = torch.rand(1,3,224,224)
out = model(input)
print(out.shape)

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Xception

Code

import torch
from python_developer_tools.cv.classes.Xception import Xception
model = Xception()
input = torch.randn(1,3,299,299)
output = model(input)
print(output.shape)

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files

common

名称	功能
get_filename_suf_pix	获取路径的文件名,后缀,父路径

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