Michio JP

Michio JP

1630747757

Building a Convolution as an Inception-like Unit

Diverse Branch Block: Building a Convolution as an Inception-like Unit (PyTorch) (CVPR-2021)

DBB is a powerful ConvNet building block to replace regular conv. It improves the performance without any extra inference-time costs. This repo contains the code for building DBB and converting it into a single conv. You can also get the equivalent kernel and bias in a differentiable way at any time (get_equivalent_kernel_bias in diversebranchblock.py). This may help training-based pruning or quantization.

This is the PyTorch implementation. The MegEngine version is at https://github.com/megvii-model/DiverseBranchBlock

Another nice implementation by @zjykzj. Please check here.

Paper: https://arxiv.org/abs/2103.13425

Update: released the code for building the block, transformations and verification.

Update: a more efficient implementation of BNAndPadLayer

Update: MobileNet, ResNet-18 and ResNet-50 models released. You can download them from Google Drive or Baidu Cloud. For the 1x1-KxK branch of MobileNet, we used internal_channels = 2x input_channels for every depthwise conv. 1x also worked but the accuracy was slightly lower (72.71% v.s. 72.88%). On dense conv like ResNet, we used internal_channels = input_channels, and larger internal_channels seemed useless.

Sometimes I call it ACNet v2 because 'DBB' is two bits larger than 'ACB' in ASCII. (lol)

@inproceedings{ding2021diverse,
title={Diverse Branch Block: Building a Convolution as an Inception-like Unit},
author={Ding, Xiaohan and Zhang, Xiangyu and Han, Jungong and Ding, Guiguang},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={10886--10895},
year={2021}
}

Abstract

We propose a universal building block of Convolutional Neural Network (ConvNet) to improve the performance without any inference-time costs. The block is named Diverse Branch Block (DBB), which enhances the representational capacity of a single convolution by combining diverse branches of different scales and complexities to enrich the feature space, including sequences of convolutions, multi-scale convolutions, and average pooling. After training, a DBB can be equivalently converted into a single conv layer for deployment. Unlike the advancements of novel ConvNet architectures, DBB complicates the training-time microstructure while maintaining the macro architecture, so that it can be used as a drop-in replacement for regular conv layers of any architecture. In this way, the model can be trained to reach a higher level of performance and then transformed into the original inference-time structure for inference. DBB improves ConvNets on image classification (up to 1.9% higher top-1 accuracy on ImageNet), object detection and semantic segmentation.

image
image
image

Use our pretrained models

You may download the models reported in the paper (ResNet-18, ResNet-50, MobileNet) from Google Drive (https://drive.google.com/drive/folders/1BPuqY_ktKz8LvHjFK5abD0qy3ESp8v6H?usp=sharing) or Baidu Cloud (https://pan.baidu.com/s/1wPaQnLKyNjF_bEMNRo4z6Q, the access code is "dbbk"). For the ease of transfer learning on other tasks, we provide both training-time and inference-time models. For ResNet-18 as an example, assume IMGNET_PATH is the path to your directory that contains the "train" and "val" directories of ImageNet, you may test the accuracy by running

python test.py IMGNET_PATH train ResNet-18_DBB_7101.pth -a ResNet-18 -t DBB

Here "train" indicates the training-time structure

Convert the training-time models into inference-time

You may convert a trained model into the inference-time structure with

python convert.py [weights file of the training-time model to load] [path to save] -a [architecture name]

For example,

python convert.py ResNet-18_DBB_7101.pth ResNet-18_DBB_7101_deploy.pth -a ResNet-18

Then you may test the inference-time model by

python test.py IMGNET_PATH deploy ResNet-18_DBB_7101_deploy.pth -a ResNet-18 -t DBB

Note that the argument "deploy" builds an inference-time model.

ImageNet training

The multi-processing training script in this repo is based on the official PyTorch example for the simplicity and better readability. The modifications include the model-building part and cosine learning rate scheduler. You may train and test like this:

python train.py -a ResNet-18 -t DBB --dist-url tcp://127.0.0.1:23333 --dist-backend nccl --multiprocessing-distributed --world-size 1 --rank 0 --workers 64 IMGNET_PATH
python test.py IMGNET_PATH train model_best.pth.tar -a ResNet-18

Use like this in your own code

Assume your model is like

class SomeModel(nn.Module):
    def __init__(self, ...):
        ...
        self.some_conv = nn.Conv2d(...)
        self.some_bn = nn.BatchNorm2d(...)
        ...
        
    def forward(self, inputs):
        out = ...
        out = self.some_bn(self.some_conv(out))
        ...

For training, just use DiverseBranchBlock to replace the conv-BN. Then SomeModel will be like

class SomeModel(nn.Module):
    def __init__(self, ...):
        ...
        self.some_dbb = DiverseBranchBlock(..., deploy=False)
        ...
        
    def forward(self, inputs):
        out = ...
        out = self.some_dbb(out)
        ...

Train the model just like you train the other regular models. Then call switch_to_deploy of every DiverseBranchBlock, test, and save.

model = SomeModel(...)
train(model)
for m in train_model.modules():
    if hasattr(m, 'switch_to_deploy'):
        m.switch_to_deploy()
test(model)
save(model)

FAQs

Q: Is the inference-time model's output the same as the training-time model?

A: Yes. You can verify that by

python dbb_verify.py

Q: What is the relationship between DBB and RepVGG?

A: RepVGG is a plain architecture, and the RepVGG-style structural re-param is designed for the plain architecture. On a non-plain architecture, a RepVGG block shows no superiority compared to a single 3x3 conv (it improves Res-50 by only 0.03%, as reported in the RepVGG paper). DBB is a universal building block that can be used on numerous architectures.

Q: How to quantize a model with DBB?

A1: Post-training quantization. After training and conversion, you may quantize the converted model with any post-training quantization method. Then you may insert a BN after the conv converted from a DBB and finetune to recover the accuracy just like you quantize and finetune the other models. This is the recommended solution. Please see the quantization example of RepVGG.

A2: Quantization-aware training. During the quantization-aware training, instead of constraining the params in a single kernel (e.g., making every param in {-127, -126, .., 126, 127} for int8) for an ordinary conv, you should constrain the equivalent kernel of a DBB (get_equivalent_kernel_bias()).

Q: I tried to finetune your model with multiple GPUs but got an error. Why are the names of params like "xxxx.weight" in the downloaded weight file but sometimes like "module.xxxx.weight" (shown by nn.Module.named_parameters()) in my model?

A: DistributedDataParallel may prefix "module." to the name of params and cause a mismatch when loading weights by name. The simplest solution is to load the weights (model.load_state_dict(...)) before DistributedDataParallel(model). Otherwise, you may insert "module." before the names like this

checkpoint = torch.load(...)    # This is just a name-value dict
ckpt = {('module.' + k) : v for k, v in checkpoint.items()}
model.load_state_dict(ckpt)

Likewise, if the param names in the checkpoint file start with "module." but those in your model do not, you may strip the names like

ckpt = {k.replace('module.', ''):v for k,v in checkpoint.items()}   # strip the names
model.load_state_dict(ckpt)

Q: So a DBB derives the equivalent KxK kernels before each forwarding to save computations?

A: No! More precisely, we do the conversion only once right after training. Then the training-time model can be discarded, and every resultant block is just a KxK conv. We only save and use the resultant model.

Contact

dxh17@mails.tsinghua.edu.cn

Google Scholar Profile: https://scholar.google.com/citations?user=CIjw0KoAAAAJ&hl=en

My open-sourced papers and repos:

The Structural Re-parameterization Universe:

  1. (preprint, 2021) A powerful MLP-style CNN building block
    RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition
    code.

2. (CVPR 2021) A super simple and powerful VGG-style ConvNet architecture. Up to 83.55% ImageNet top-1 accuracy!
RepVGG: Making VGG-style ConvNets Great Again
code.

3. (preprint, 2020) State-of-the-art channel pruning
Lossless CNN Channel Pruning via Decoupling Remembering and Forgetting
code.

4. ACB (ICCV 2019) is a CNN component without any inference-time costs. The first work of our Structural Re-parameterization Universe.
ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks.
code.

5. DBB (CVPR 2021) is a CNN component with higher performance than ACB and still no inference-time costs. Sometimes I call it ACNet v2 because "DBB" is 2 bits larger than "ACB" in ASCII (lol).
Diverse Branch Block: Building a Convolution as an Inception-like Unit
code.

Model compression and acceleration:

  1. (CVPR 2019) Channel pruning: Centripetal SGD for Pruning Very Deep Convolutional Networks with Complicated Structure
    code

2. (ICML 2019) Channel pruning: Approximated Oracle Filter Pruning for Destructive CNN Width Optimization
code

3. (NeurIPS 2019) Unstructured pruning: Global Sparse Momentum SGD for Pruning Very Deep Neural Networks
code

Download Details:
 

Author: DingXiaoH
Download Link: Download The Source Code
Official Website: https://github.com/DingXiaoH/DiverseBranchBlock 
License: Apache-2.0

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Building a Convolution as an Inception-like Unit
Carmen  Grimes

Carmen Grimes

1595494844

How to start an electric scooter facility/fleet in a university campus/IT park

Are you leading an organization that has a large campus, e.g., a large university? You are probably thinking of introducing an electric scooter/bicycle fleet on the campus, and why wouldn’t you?

Introducing micro-mobility in your campus with the help of such a fleet would help the people on the campus significantly. People would save money since they don’t need to use a car for a short distance. Your campus will see a drastic reduction in congestion, moreover, its carbon footprint will reduce.

Micro-mobility is relatively new though and you would need help. You would need to select an appropriate fleet of vehicles. The people on your campus would need to find electric scooters or electric bikes for commuting, and you need to provide a solution for this.

To be more specific, you need a short-term electric bike rental app. With such an app, you will be able to easily offer micro-mobility to the people on the campus. We at Devathon have built Autorent exactly for this.

What does Autorent do and how can it help you? How does it enable you to introduce micro-mobility on your campus? We explain these in this article, however, we will touch upon a few basics first.

Micro-mobility: What it is

micro-mobility

You are probably thinking about micro-mobility relatively recently, aren’t you? A few relevant insights about it could help you to better appreciate its importance.

Micro-mobility is a new trend in transportation, and it uses vehicles that are considerably smaller than cars. Electric scooters (e-scooters) and electric bikes (e-bikes) are the most popular forms of micro-mobility, however, there are also e-unicycles and e-skateboards.

You might have already seen e-scooters, which are kick scooters that come with a motor. Thanks to its motor, an e-scooter can achieve a speed of up to 20 km/h. On the other hand, e-bikes are popular in China and Japan, and they come with a motor, and you can reach a speed of 40 km/h.

You obviously can’t use these vehicles for very long commutes, however, what if you need to travel a short distance? Even if you have a reasonable public transport facility in the city, it might not cover the route you need to take. Take the example of a large university campus. Such a campus is often at a considerable distance from the central business district of the city where it’s located. While public transport facilities may serve the central business district, they wouldn’t serve this large campus. Currently, many people drive their cars even for short distances.

As you know, that brings its own set of challenges. Vehicular traffic adds significantly to pollution, moreover, finding a parking spot can be hard in crowded urban districts.

Well, you can reduce your carbon footprint if you use an electric car. However, electric cars are still new, and many countries are still building the necessary infrastructure for them. Your large campus might not have the necessary infrastructure for them either. Presently, electric cars don’t represent a viable option in most geographies.

As a result, you need to buy and maintain a car even if your commute is short. In addition to dealing with parking problems, you need to spend significantly on your car.

All of these factors have combined to make people sit up and think seriously about cars. Many people are now seriously considering whether a car is really the best option even if they have to commute only a short distance.

This is where micro-mobility enters the picture. When you commute a short distance regularly, e-scooters or e-bikes are viable options. You limit your carbon footprints and you cut costs!

Businesses have seen this shift in thinking, and e-scooter companies like Lime and Bird have entered this field in a big way. They let you rent e-scooters by the minute. On the other hand, start-ups like Jump and Lyft have entered the e-bike market.

Think of your campus now! The people there might need to travel short distances within the campus, and e-scooters can really help them.

How micro-mobility can benefit you

benefits-micromobility

What advantages can you get from micro-mobility? Let’s take a deeper look into this question.

Micro-mobility can offer several advantages to the people on your campus, e.g.:

  • Affordability: Shared e-scooters are cheaper than other mass transportation options. Remember that the people on your campus will use them on a shared basis, and they will pay for their short commutes only. Well, depending on your operating model, you might even let them use shared e-scooters or e-bikes for free!
  • Convenience: Users don’t need to worry about finding parking spots for shared e-scooters since these are small. They can easily travel from point A to point B on your campus with the help of these e-scooters.
  • Environmentally sustainable: Shared e-scooters reduce the carbon footprint, moreover, they decongest the roads. Statistics from the pilot programs in cities like Portland and Denver showimpressive gains around this key aspect.
  • Safety: This one’s obvious, isn’t it? When people on your campus use small e-scooters or e-bikes instead of cars, the problem of overspeeding will disappear. you will see fewer accidents.

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Carmen  Grimes

Carmen Grimes

1595491178

Best Electric Bikes and Scooters for Rental Business or Campus Facility

The electric scooter revolution has caught on super-fast taking many cities across the globe by storm. eScooters, a renovated version of old-school scooters now turned into electric vehicles are an environmentally friendly solution to current on-demand commute problems. They work on engines, like cars, enabling short traveling distances without hassle. The result is that these groundbreaking electric machines can now provide faster transport for less — cheaper than Uber and faster than Metro.

Since they are durable, fast, easy to operate and maintain, and are more convenient to park compared to four-wheelers, the eScooters trend has and continues to spike interest as a promising growth area. Several companies and universities are increasingly setting up shop to provide eScooter services realizing a would-be profitable business model and a ready customer base that is university students or residents in need of faster and cheap travel going about their business in school, town, and other surrounding areas.

Electric Scooters Trends and Statistics

In many countries including the U.S., Canada, Mexico, U.K., Germany, France, China, Japan, India, Brazil and Mexico and more, a growing number of eScooter users both locals and tourists can now be seen effortlessly passing lines of drivers stuck in the endless and unmoving traffic.

A recent report by McKinsey revealed that the E-Scooter industry will be worth― $200 billion to $300 billion in the United States, $100 billion to $150 billion in Europe, and $30 billion to $50 billion in China in 2030. The e-Scooter revenue model will also spike and is projected to rise by more than 20% amounting to approximately $5 billion.

And, with a necessity to move people away from high carbon prints, traffic and congestion issues brought about by car-centric transport systems in cities, more and more city planners are developing more bike/scooter lanes and adopting zero-emission plans. This is the force behind the booming electric scooter market and the numbers will only go higher and higher.

Companies that have taken advantage of the growing eScooter trend develop an appthat allows them to provide efficient eScooter services. Such an app enables them to be able to locate bike pick-up and drop points through fully integrated google maps.

List of Best Electric Bikes for Rental Business or Campus Facility 2020:

It’s clear that e scooters will increasingly become more common and the e-scooter business model will continue to grab the attention of manufacturers, investors, entrepreneurs. All this should go ahead with a quest to know what are some of the best electric bikes in the market especially for anyone who would want to get started in the electric bikes/scooters rental business.

We have done a comprehensive list of the best electric bikes! Each bike has been reviewed in depth and includes a full list of specs and a photo.

Billy eBike

mobile-best-electric-bikes-scooters https://www.kickstarter.com/projects/enkicycles/billy-were-redefining-joyrides

To start us off is the Billy eBike, a powerful go-anywhere urban electric bike that’s specially designed to offer an exciting ride like no other whether you want to ride to the grocery store, cafe, work or school. The Billy eBike comes in 4 color options – Billy Blue, Polished aluminium, Artic white, and Stealth black.

Price: $2490

Available countries

Available in the USA, Europe, Asia, South Africa and Australia.This item ships from the USA. Buyers are therefore responsible for any taxes and/or customs duties incurred once it arrives in your country.

Features

  • Control – Ride with confidence with our ultra-wide BMX bars and a hyper-responsive twist throttle.
  • Stealth- Ride like a ninja with our Gates carbon drive that’s as smooth as butter and maintenance-free.
  • Drive – Ride further with our high torque fat bike motor, giving a better climbing performance.
  • Accelerate – Ride quicker with our 20-inch lightweight cutout rims for improved acceleration.
  • Customize – Ride your own way with 5 levels of power control. Each level determines power and speed.
  • Flickable – Ride harder with our BMX /MotoX inspired geometry and lightweight aluminum package

Specifications

  • Maximum speed: 20 mph (32 km/h)
  • Range per charge: 41 miles (66 km)
  • Maximum Power: 500W
  • Motor type: Fat Bike Motor: Bafang RM G060.500.DC
  • Load capacity: 300lbs (136kg)
  • Battery type: 13.6Ah Samsung lithium-ion,
  • Battery capacity: On/off-bike charging available
  • Weight: w/o batt. 48.5lbs (22kg), w/ batt. 54lbs (24.5kg)
  • Front Suspension: Fully adjustable air shock, preload/compression damping /lockout
  • Rear Suspension: spring, preload adjustment
  • Built-in GPS

Why Should You Buy This?

  • Riding fun and excitement
  • Better climbing ability and faster acceleration.
  • Ride with confidence
  • Billy folds for convenient storage and transportation.
  • Shorty levers connect to disc brakes ensuring you stop on a dime
  • belt drives are maintenance-free and clean (no oil or lubrication needed)

**Who Should Ride Billy? **

Both new and experienced riders

**Where to Buy? **Local distributors or ships from the USA.

Genze 200 series e-Bike

genze-best-electric-bikes-scooters https://www.genze.com/fleet/

Featuring a sleek and lightweight aluminum frame design, the 200-Series ebike takes your riding experience to greater heights. Available in both black and white this ebike comes with a connected app, which allows you to plan activities, map distances and routes while also allowing connections with fellow riders.

Price: $2099.00

Available countries

The Genze 200 series e-Bike is available at GenZe retail locations across the U.S or online via GenZe.com website. Customers from outside the US can ship the product while incurring the relevant charges.

Features

  • 2 Frame Options
  • 2 Sizes
  • Integrated/Removable Battery
  • Throttle and Pedal Assist Ride Modes
  • Integrated LCD Display
  • Connected App
  • 24 month warranty
  • GPS navigation
  • Bluetooth connectivity

Specifications

  • Maximum speed: 20 mph with throttle
  • Range per charge: 15-18 miles w/ throttle and 30-50 miles w/ pedal assist
  • Charging time: 3.5 hours
  • Motor type: Brushless Rear Hub Motor
  • Gears: Microshift Thumb Shifter
  • Battery type: Removable Samsung 36V, 9.6AH Li-Ion battery pack
  • Battery capacity: 36V and 350 Wh
  • Weight: 46 pounds
  • Derailleur: 8-speed Shimano
  • Brakes: Dual classic
  • Wheels: 26 x 20 inches
  • Frame: 16, and 18 inches
  • Operating Mode: Analog mode 5 levels of Pedal Assist Thrott­le Mode

Norco from eBikestore

norco-best-electric-bikes-scooters https://ebikestore.com/shop/norco-vlt-s2/

The Norco VLT S2 is a front suspension e-Bike with solid components alongside the reliable Bosch Performance Line Power systems that offer precise pedal assistance during any riding situation.

Price: $2,699.00

Available countries

This item is available via the various Norco bikes international distributors.

Features

  • VLT aluminum frame- for stiffness and wheel security.
  • Bosch e-bike system – for their reliability and performance.
  • E-bike components – for added durability.
  • Hydraulic disc brakes – offer riders more stopping power for safety and control at higher speeds.
  • Practical design features – to add convenience and versatility.

Specifications

  • Maximum speed: KMC X9 9spd
  • Motor type: Bosch Active Line
  • Gears: Shimano Altus RD-M2000, SGS, 9 Speed
  • Battery type: Power Pack 400
  • Battery capacity: 396Wh
  • Suspension: SR Suntour suspension fork
  • Frame: Norco VLT, Aluminum, 12x142mm TA Dropouts

Bodo EV

bodo-best-electric-bikes-scootershttp://www.bodoevs.com/bodoev/products_show.asp?product_id=13

Manufactured by Bodo Vehicle Group Limited, the Bodo EV is specially designed for strong power and extraordinary long service to facilitate super amazing rides. The Bodo Vehicle Company is a striking top in electric vehicles brand field in China and across the globe. Their Bodo EV will no doubt provide your riders with high-level riding satisfaction owing to its high-quality design, strength, breaking stability and speed.

Price: $799

Available countries

This item ships from China with buyers bearing the shipping costs and other variables prior to delivery.

Features

  • Reliable
  • Environment friendly
  • Comfortable riding
  • Fashionable
  • Economical
  • Durable – long service life
  • Braking stability
  • LED lighting technology

Specifications

  • Maximum speed: 45km/h
  • Range per charge: 50km per person
  • Charging time: 8 hours
  • Maximum Power: 3000W
  • Motor type: Brushless DC Motor
  • Load capacity: 100kg
  • Battery type: Lead-acid battery
  • Battery capacity: 60V 20AH
  • Weight: w/o battery 47kg

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Create an On-Demand Mobile App like UrbanClap

Do you want to make an app like UrbanClap? UrbanClap is an on-demand marketplace where you can get all the home and beauty, etc services. AppClues Infotech is one of the leading on-demand mobile app development company based in New York that helps to create an app like UrbanClap for your business with advanced features and technology.

Our On-Demand App development services:

• Online Food Ordering App
• Beauty and Salon App
• Online Ticket Booking App
• Online Chatting or Dating App

Do you have an app development idea, AppClues Infotech helps you to convert your imagination into reality. Hire our highly skilful developers to create a robust and high-quality mobile app.

For more info:
Call: +1-978-309-9910
Email: info@appcluesinfotech.com

#build an app like urbanclap #cost to build an app like urbanclap #make an app like urbanclap #cost to make an app like urbanclap #create an app like urbanclap

Idrish Dhankot

Idrish Dhankot

1605772591

How to build an Elearning app or website like Coursera

E-learning has completely changed the education industry, specifically in the time of COVID-19. With the increased use of online learning apps, there is a huge growth opportunity that lies ahead for some of the industry.

Developing an e-learning app requires extensive experience in coding. If you are looking to develop an e-learning app, WebClues Infotech is the perfect company to guide you at every step including the Concept, Business Plan, Launch, Revenue Generation, and Support.

To know more about How you can develop and what is the cost to develop e-learning apps like Coursera read our blog How to build an E-learning app or website like Coursera

#how to build an e learning website like coursera #how much would it cost to create a website like coursera #cost of website like coursera #how to make an app like coursera #how to create a website like coursera #e learning website cost

How to build an learning app like BYJU's| Cost to build app like BYJU's?

Due to schools & Colleges being closed due to Covid-19 Pandemic, the need for e-learning platforms like Byju’s has seen a rapid rise among school students. The rise is so fast that in just 3 months from the beginning of lockdowns its user base increased by twice.

Want to help school students learn with creative methods from an e-learning app?
WebClues Infotech is there for you to bring your vision to reality with its highly skilled e-learning App Development team. With past experience in developing different types of e-learning solutions like AshAce Papers, EduPlay Cloud, Squared, and many more, WebClues Infotech is fully equipped to develop an e-learning app development solution based on your need

Want more information on Byju’s like e-learning app Development?

Visit our detailed guide at https://www.webcluesinfotech.com/how-to-create-an-elearning-app-like-byjus/

#how to build an learning app like byju's? #how to develop a learning app like byju's features & cost #how to create an app like byju's #how much does it cost to develop an app like byjus #e-learning app development solution #e-learning apps development