pyWireDetect

Wire detection using synthetic data and dilated convolutional networks

Version: 1.0.0   
Author : Erenus Yildiz ,Md. Nazmuddoha Ansary

Links

• Model 2,3,4 weights and training Scripts
• High Res Only wire_set
• Mixed Low -High Res wire_set

SETUP

• Min Version: python > 3.5
• Tested on : python 3.6.9

IMPORTANT

Before cloning or executing any script the following has to be ensured in the parent system:

• There should not exist any virtualenv named wirevenv
• To ensure this,install jupyter in the parent system (not in any virtualenv) by:
  • $:~ pip3 install --upgrade pip
  • $:~ pip3 install jupyter
• See the list of kernels
  • $:~ jupyter kernelspec list
• IF THERE EXISTS A PRE-EXISTING KERNEL NAMED --> wirevenv
  • $:~ jupyter kernelspec remove wirevenv
• Confirm Deletion by :
  • $:~ jupyter kernelspec list

CLONING:

• Git clone this repo to a location where there is no additional read-write permission conflict (THIS WILL HIGHLY DEPEND ON THE PARENT SYSTEM)
• An example place can be: /home/{user_name}/WireDetect/
• $:~ git clone https://github.com/mnansary/pyWireDetect.git

Environment Setup:

• create a virtualenvironment in the cloned repo
  • WireDetect/pyWireDetect$:~ virtualenv wirevenv
• activate the virtualenvironment:
  • WireDetect/pyWireDetect$:~ source wirevenv/bin/activate
• Upgrade pip:
  • (wirevenv)WireDetect/pyWireDetect$:~ pip3 install --upgrade pip
• Install requirements:
  • (wirevenv)WireDetect/pyWireDetect$:~ pip3 install -r requirements.txt
  • depending on permission --user flag may be needed
• Add wirevenv to jupyter:
  • (wirevenv)WireDetect/pyWireDetect$:~ python3 -m ipykernel install --user --name=wirevenv
• Confirm Addition by :
  • (wirevenv)WireDetect/pyWireDetect$:~ jupyter kernelspec list

DataSet Creation

• The extracted folder should have a wire_set folder which has the following tree:

    wire_set
    ├── test
    │   ├── images
    │   │   ├── xx.jpg
    ..................
    │   │   ├── xx.jpg
    │   │   └── xx.jpg
    │   └── masks
    │       ├── xx.jpg
    ..................
    │       ├── xx.jpg
    │       └── xx.jpg
    ├── train
    │   ├── images
    │   │   ├── xx.jpg
    ..................
    │   │   ├── xx.jpg
    │   │   └── xx.jpg
    │   └── masks
    │       ├── xx.jpg
    ....................
    │       ├── xx.jpg
    │       └── xx.jpg
    └── val
        ├── images
        │   ├── xx.jpg
        ...............
        │   └── xx.jpg
        └── masks
            ├── xx.jpg
            ...........
            └── xx.jpg
  

• Copy the wire_set folder to the git repo (pyWireDetect) so that the datagen.ipynb and wire_set are under the same folder. I.E- the repo tree should update to show as:

    ├── datagen.ipynb
    ├── wire_set
    ............
  

• Move The test folder in wire_set to TPU_COLAB folder. The wire_set folder should now only contain train and val folders

• Open the repo in jupyter-notebook within the activated virtualenv:

  • (wirevenv)WireDetect/pyWireDetect$:~ jupyter-notebook

• Select wirevenv as the kernel for datagen.ipynb

• Run all the blocks in datagen.ipynb

• Upon Successfull Execution The DataSet folder now created should have the following tree with populated data:

    DataSet
    ├── Eval
    │   ├── images
    │   └── masks
    ├── Train
    │   ├── images
    │   └── masks
    └── WireDTF
        ├── Eval
        └── Train
  

Data Upload

Upload The WireDTF folder to your public bucket

Training:

TPU COLAB

TPU’s have been recently added to the Google Colab portfolio making it even more attractive for quick-and-dirty machine learning projects when your own local processing units are just not fast enough. While the Tesla K80 available in Google Colab delivers respectable 1.87 TFlops and has 12GB RAM, the TPUv2 available from within Google Colab comes with a whopping 180 TFlops, give or take. It also comes with 64 GB High Bandwidth Memory (HBM).

• Create A folder named WIRE_DETECTION (in all caps) in you google drive

• Upload the folder TPU_COLAB into WIRE_DETECTION folder. The TPU_COLAB should have the following folder tree before Uploding:

    ├── model1.ipynb
    ├── model2.ipynb
    ├── model3.ipynb
    ├── model4.ipynb
    ├── model_weights
    ├── test
    └── train.ipynb
  

SOTA MODELS (SELECTED)

• The selected SOTA models for training and scoring are as follows:

  • model1.ipynb : 'efficientnetb7'
  • model2.ipynb : 'inceptionv3'
  • model3.ipynb : 'inceptionresnetv2'
  • model4.ipynb : 'densenet201'

• In order to train and evaluate a selected SOTA model, open the uploaded model{x}.ipynb in google colab

• Go To Edit>Notebook Settings > Hardware Accelerator select TPU

• Run the cell MOUNT GOOGLE Drive only

• Upon Successfull mount run the cell Change your working directory

• Make sure you are using you bucket name properly

• If both Mounting and Directory Change are successfull, Go To Runtime>Restart and run all

• Upon successfull run the model predictions on test data will be displayed within the notebook with SSIM and IoU/F1 score summary

• The trained model will be saved at WIRE_DETECTION/TPU_COLAB/model_weights/model{x}.h5 in google drive

• The predicted masks can be found at WIRE_DETECTION/TPU_COLAB/test/preds/model{x}/ in google drive

train.ipynb

• The train.ipynb is used to train other models
• Available SOTA model identifiers:

Model Type	Identifiers
VGG	'vgg16' 'vgg19'
ResNet	'resnet18' 'resnet34' 'resnet50' 'resnet101' 'resnet152'
SE-ResNet	'seresnet18' 'seresnet34' 'seresnet50' 'seresnet101' 'seresnet152'
ResNeXt	'resnext50' 'resnext101'
SE-ResNeXt	'seresnext50' 'seresnext101'
SENet154	'senet154'
DenseNet	'densenet121' 'densenet169' 'densenet201'
Inception	'inceptionv3' 'inceptionresnetv2'
MobileNet	'mobilenet' 'mobilenetv2'
EfficientNet	'efficientnetb0' 'efficientnetb1' 'efficientnetb2' 'efficientnetb3' 'efficientnetb4' 'efficientnetb5' 'efficientnetb6' 'efficientnetb7'

The process for training is same as before with exception of MODEL SPEC cell execution

• Select The model you want to train
  

• Copy the model identifier with single quotes to model_name. i.e- for example to train the ResNet50 model you need to copy 'resnet50' (with quotes)

• After selection:

  • MOUNT GOOGLE Drive
  • Change your working directory
  • Ensure Bucket Name
  • Runtime>Restart and run all

• The trained model will be saved at WIRE_DETECTION/TPU_COLAB/model_weights/{identifier}.h5 in google drive

• The predicted masks can be found at WIRE_DETECTION/TPU_COLAB/test/preds/{IDENTIFIER}/ in google drive

• The model arch is the famous SOTA arch Unet

Colab connect

• Open the page source with inspect in chrome and goto console

• Copy the following javascript code and paste and hit enter

    function ClickConnect(){
        try {
            console.log("Working"); 
                document.querySelector("paper-button#ok").click()
                console.log("clicked");
        }
        catch(error) {
            console.log("connected");
        }
    }
        
    setInterval(ClickConnect,60000) 
  

Testing : Local Deployment

• Download the model1.h5 from WIRE_DETECTION/TPU_COLAB/model_weights/ in drive
• place the model1.h5 in TESTING/weights folder in local repo
• place the images to test on in TESTING/images
• run Testing.ipynb from Jupyter-notebook
• predictions will be found at TESTING/preds

UNSTABLE DIR

• The files in the unstable dir are some random scripts that might come in handy in the future

EXECUTION ENVIRONMENT

OS          : Ubuntu 18.04.3 LTS (64-bit) Bionic Beaver        
Memory      : 7.7 GiB  
Processor   : Intel® Core™ i5-8250U CPU @ 1.60GHz × 8    
Graphics    : Intel® UHD Graphics 620 (Kabylake GT2)  
Gnome       : 3.28.2