semantic_segment_servicev2

Train Classification model as a service. Easy as ABC!

The goal of this project is to train and test a classification model without any code or knowledge of the deeplearning.

Usage

Docker Compose

Install dependencies

• Docker
• Docker Compose
• Docker gpu

# It should be install for gpu support
bash docker-gpu-installation.sh

docker-compose up -d

---

Shell

Install dependencies

• Pytorch & TorchVision

cd train
pip install -r requirements.txt

cd inference
pip install -r requirements.txt

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Train

Prepare data

Put your dataset in the following format:

• If we have coco dataset

- dataset
  - images
    - image.jpg
    - image.jpg
    - image.jpg
      ...
      
  {COCOLABELS}.json
  
      

Train from bash

cd train
python trainer.py --images_dir {YOUR_DATASET_PATH} \
                  --masks_dir {YOUR_DATASET_PATH} \ # If you have mask dataset masks_dir and if not, coco dataset set {COCOLABELS}.json
                  --model_name {MODEL_NAME} \
                  --w_size {WIDTH} \
                  --h_size {HEIGHT} \
                  --n_classes {NUMBER_OF_CLASSES} \ 
                  --response_url {RESPONSE_URL} \ # If you want to send response to send final result to your server (optional)
                  --log_url {LOG_URL} \ # If you want to send log to your server (optional)
                  --epochs {EPOCHS} \
                  --batch_size {BATCH_SIZE} \
                  --num_dataloader_workers {NUM_DATALOADER_WORKERS} \
                  --image_width {NETWORK_INPUT_WIDTH} \
                  --image_height {NETWORK_INPUT_HEIGHT} \
                  --validation_split {VALIDATION_SPLIT} \
                  --pretrained_path {PRETRAINED_PATH} \
                  --backbone {BACKBONE} 

Train with API

If you run the docker compose file you should put your dataset in volumes/dataset folder and your weights in volumes/weights folder.

Parameters

{
  "dataset_p": dataset_p,
  "save_name": save_name,
  "batch_size": batch_size,
  "num_dataloader_workers": num_dataloader_workers,
  "epochs": epochs,
  "validation_split": validation_split,
  "pretrained_path": pretrained_path,
  "backbone": backbone,
  "image_width": image_width,
  "image_height": image_height,
  "response_url": response_url,
  "extra_kwargs": extra_kwargs
}

• response_url is the url to send the response. It can be Null or not send

• extra_kwargs is a dictionary that will send back to your response API after the train is finished.

Example

• For train example refer to the example
• For inference example refer to the example